Connected Traffic Control System (CTCS): Research Planning and …€¦ · Task 3 concluded in a...

Connected Traffic Control System (CTCS):

Research Planning and Concept Development

Task 4: Assessment of Technology Readiness

Levels for Priority Research Areas

Final

October 8, 2019

Prepared by

WSP

Prepared for

The Connected Vehicle Pooled Fund Study

(University of Virginia Center for Transportation Studies)

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TABLE OF CONTENTS

1 INTRODUCTION ...................................................... 1

1.1 Background ............................................................................ 1

1.2 Document Organization ................................................... 1

2 SCOPE AND METHODOLOGY ......................... 2

2.1 Synopsis of Other Project Tasks ...................................2

2.2 Stakeholder Workshop Summary ...............................2

2.3 Overview of Technology Readiness Levels ............. 5

2.4 Definition of Deployment Readiness ......................... 7

3 PREVIOUS AND ONGOING RESEARCH AND DEVELOPMENT EFFORTS ...................... 8

3.1 Queue Length Detection ................................................. 8

3.1.1 Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections ................................................................. 8

3.1.2 Real-Time Queue Length Estimation for Congested Signalized Intersections ................................................................................................................... 9

3.1.3 USDOT Connected Vehicle Pilot Deployment: Tampa................... 10

3.1.4 Queue Spillback Detection and Control Strategies based on Connected Vehicle Technology in a Congested Network............ 10

3.1.5 Research Assessment .............................................................................................. 11

3.2 MAP File Creation ............................................................... 11

3.2.1 Basic Infrastructure Message Development and Standards Support for Connected Vehicles Applications ...................................... 12

3.2.2 Standardisation of SPaT and MAP ................................................................. 13

3.2.3 Research Assessment ............................................................................................. 13

3.3 Pedestrian Safety .............................................................. 14

3.3.1 Accessible Transportation Technologies Research Initiative (ATTRI): State of the Practice Scan ................................................................. 14

3.3.2 Discussion Guide for Automated and Connected Vehicles, Pedestrians, and Bicyclists ...................................................................................15

3.3.3 USDOT Connected vehicle pilot deployment: New York City ....15

3.3.4 SmartCross ..................................................................................................................... 16

3.3.5 Smart Walk Assistant ............................................................................................. 16


3.4 Real-time Signal Optimization for Groups ............ 17

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3.4.1 Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept ............................................................................................................................ 18

3.4.2 Advanced Traffic Signal Control Algorithms, Appendix A: Exploratory Advanced Research Project: BMW Final Report ..... 18

3.4.3 GlidePath Prototype Application ................................................................... 19

3.4.4 Research Assessment ............................................................................................ 19

3.5 Virtual Detection............................................................... 20

3.5.1 Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data ..................................................................................... 20

3.5.2 Virtual Detection at Intersections using Connected Vehicle Trajectory Data ............................................................................................................. 21


3.6 Imminent Safety at Rail Crossings ........................... 22

3.6.1 TRAINFO .......................................................................................................................... 22

3.6.2 USDOT Connected Vehicle Safety for Rail............................................... 23

3.6.3 2017 Grade Crossing Research Needs Workshop ............................... 23


3.7 Data Sharing ....................................................................... 24

3.7.1 Regional Integrated Transportation Information System (RITIS) ................................................................................................................................................25

3.7.2 I-210 Connected Corridors ................................................................................. 26

3.7.3 TRANSCOM................................................................................................................... 26


3.8 Position Correction .......................................................... 27

3.8.1 Connected Vehicle Pilot Positioning and Timing Report: Summary of Positioning and Timing Approaches in CV Pilot Sites.................................................................................................................................... 28

3.8.2 Evaluation of Vehicle Positioning Accuracy by Using GPS-Enabled Smartphones ......................................................................................... 28

3.8.3 Research Assessment ........................................................................................... 29

3.9 Lane-Level Dynamic Mapping .................................... 29

3.9.1 Dynamic Map Update Protocol for Highly Automated Driving Vehicles ........................................................................................................................... 30

3.9.2 Dynamic Map Update Using Connected Vehicle Data ................. 30


3.10 Lane Availability ................................................................. 31

3.10.1 Human Factors for Connected Vehicles Transit Bus Research . 32

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3.10.2 Effects of Connected and Autonomous Vehicles on Contraflow Operations for Emergency Evacuation: a Microsimulation Study ................................................................................................................................................ 32


4 CONCLUSIONS AND NEXT STEPS ............. 34

5 BIBLIOGRAPHY ................................................... 36

6 APPENDIX A: STAKEHOLDERS LIST ......... 39

7 APPENDIX B: STAKEHOLDER NEEDS WORKSHOP MEETING NOTES ..................... 41

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TABLES

TABLE 2-1: PRIORITIZED RESEARCH AREAS BY USE CASE ..................................................................... 3

TABLE 2-2: TECHNOLOGY READINESS LEVELS ......... 6 TABLE 3-1: QUEUE LENGTH DETECTION

TECHNOLOGY READINESS LEVEL . 11 TABLE 3-2: MAP FILE CREATION TECHNOLOGY

READINESS LEVEL ................................... 13 TABLE 3-3: PEDESTRIAN SAFETY TECHNOLOGY

READINESS LEVEL ................................... 17 TABLE 3-4: REAL TIME SIGNAL OPTIMIZATION FOR

GROUPS TECHNOLOGY READINESS LEVEL ................................. 20

TABLE 3-5: VIRTUAL DETECTION TECHNOLOGY READINESS LEVEL ................................... 21

TABLE 3-6: IMMINENT SAFETY AT RAIL CROSSINGS TECHNOLOGY READINESS LEVEL ............................................................................... 24

TABLE 3-7: DATA SHARING TECHNOLOGY READINESS LEVEL .................................. 27

TABLE 3-8: POSITION CORRECTION TECHNOLOGY READINESS LEVEL .................................. 29

TABLE 3-9: LANE-LEVEL DYNAMIC MAPPING TECHNOLOGY READINESS LEVEL 31

TABLE 3-10: LANE AVAILABILITY TECHNOLOGY READINESS LEVEL .................................. 33

TABLE 4-1: TECHNOLOGY READINESS LEVEL SUMMARY ..................................................... 34

FIGURES

FIGURE 2-1: STAKEHOLDER MEETING PRIORITIZATION EXERCISE RESULTS ............................................................ 5

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1 INTRODUCTION The University of Virginia (UVA) Center for Transportation Studies (CTS), on behalf of the Connected Vehicle Pooled Fund Study (CV PFS) and the United States Department of Transportation (USDOT), is working towards advancing Multi-Modal Intelligent Traffic Signal System (MMITSS) research efforts to develop Connected Traffic Control System (CTCS) concepts, concepts that span beyond single signalized intersections to consider the entire signalized corridor. This desire and effort have led to an assessment of existing and ongoing research and development activities, both nationally and internationally, intended to inform the team’s roadmap for future research. This document provides such an assessment for ten potential applications and enabling technologies identified as high-priority research areas by CV PFS stakeholders.

1.1 BACKGROUND As government and industry move to embrace connected and automated vehicle (C/AV) technologies, there is a developing need to progress towards next generation research that more fully integrates transportation systems management and operations (TSMO) with C/AV technologies to further improve safety, mobility, and the environment. This includes continuing to work to advance an understanding of how connected vehicle (CV) technologies interface with all transportation network infrastructure and a preliminary consideration of how to best plan for the introduction of automated vehicles (AV) within this environment. Accomplishing this goal requires in-depth knowledge on the context, status, and outcomes of research that has already been conducted, to identify where the project team could provide best value in filling any gaps.

1.2 DOCUMENT ORGANIZATION This document is organized into the following sections:

• Section 2 provides a summary of work done on other project tasks, presents detailed results from the stakeholder needs workshop, and contains an overview of the technology and deployment readiness assessment approaches to offer background for this report.

• Section 3 contains an overview of completed and ongoing research and development efforts related to the prioritized research areas, as well as a technology readiness level assessment for each.

• Section 4 presents conclusions of this report and next steps.

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2 SCOPE AND METHODOLOGY Building off an in-person stakeholder workshop held in April 2019, as well as other stakeholder engagements, this document considers research areas identified as high priority in terms of value to the stakeholder, and further assesses the maturation of the research and development efforts and verifies the value and feasibility of each research area. This relies primarily on a literature review and a readiness assessment, both presented in Section 3.

This section provides background to inform Section 3, including an overview of prior work and stakeholder engagements and definitions of the essential terms “technology readiness level assessment” and “deployment readiness,” which will be important to this report and to correctly developing a research roadmap in Task 5.

2.1 SYNOPSIS OF OTHER PROJECT TASKS The primary objective of Task 4 is to engage stakeholders in order to guide the prioritization of CTCS research areas identified in Tasks 1 through 3. This will support the development of the research roadmap in Task 5 and application-specific Concepts of Operations for a select set of high-priority research areas in Task 6.

Task 1 presented the project management approach for this project, including an introduction of the project team, schedule, resources, and deliverables. Task 2 developed a plan for identifying and engaging the many users and operators who have a stake in this project and the topics it covers. A list of stakeholders, first developed for the Task 2 report, is included in Appendix A of this report. The approach to stakeholder engagement includes four main strategies: identify, recruit, engage, and inform. This recognizes that this project’s success depends on stakeholder input, but that time is a limited resource and must be utilized appropriately.

Task 3 initiated the research review. The project team, CV PFS working group, and key stakeholders documented gaps in existing research to identify potential research areas and priorities, user needs, transformative benefits or goals, and corresponding performance measures for the CTCS. Task 3 concluded in a report that summarized the existing research for six use cases of interest and provided a high-level technology readiness assessment for each. The results of Task 3 were then presented to stakeholders and used to guide the formation of research areas to be explored further in this Task 4 report.

Building on the Task 4 report, the project team will develop a CTCS research plan in Task 5 which defines a path from concept to deployment for each research area, identifying current opportunities to incrementally deploy newly developed technologies. The research activities will be separated into short-, mid-, and long-term research, corresponding to 0-3 years, 4-6 years, and 7 or more years, respectively. Further building from this work, Task 6 will conclude the project with the development of one or two Concepts of Operations each specific to a chosen research area. This documentation will describe the high-priority concept(s) that the stakeholder team believes best demonstrate integrated control operations along arterials and eventually other roadway types.

2.2 STAKEHOLDER WORKSHOP SUMMARY On April 30, 2019, an all-day stakeholder workshop was conducted to present the findings and high-level technology readiness assessment classifications of the Task 3 report and solicit feedback to inform the results of Task 4. The agenda for the workshop included:

- Welcome and introductions

- Objectives of the workshop and a summary of the project to date

- Sessions on the use cases presented on the next page, to solicit feedback on proposed definitions, needs assessments, definitions of success, draft high-level technology readiness level assessments, and early identified gaps

- A discussion of remaining gaps, including an opportunity for stakeholders to voice any additional ideas

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- A prioritization exercise that utilized surveying tools to help narrow down this project’s focus areas

Minutes from the stakeholder workshop are provided in Appendix B for reference and are summarized in this section. The majority of the workshop was organized around six use cases, first summarized in the Task 2 report as identified and of interest to the CV PFS members. The use cases originated as part of a longer list in the original solicitation for the project and were selected from that list in a previous stakeholder meeting in 2018. These use cases were selected because they focus on surface street applications and because they are seen as opportunity research areas for further exploration. The six use cases are as follows:

- Arterial/surface streets with traffic control and ramp meters

- Multi-modal aspects including transit, freight, pedestrians, bikes, etc.

- Connectivity and early automation such as Level 1 longitudinal control

- Connected vehicle integrated corridor management

- Lane management

- Railroad crossing violation warning

Prior and ongoing research related to these six use cases was documented in the Task 3 report, and further discussed at the stakeholder workshop. During the workshop, the discussion focused on prioritizing these use cases, however, as part of the process, additional, specific research areas, all with interest to be pursued in support of these six broader use cases, were identified. These research areas included both applications and enabling technologies, and the broad-ranging discussion was narrowed down to 14 highest-priority research areas by splitting or combining topics as a group. These 14 further research areas are presented in Table 2-1. In many cases, there is overlap between different use cases and research areas, and as will be elaborated on in the detailed descriptions, this at times influenced the decision on which research areas to pursue and what to focus on within these research areas.

Table 2-1: Prioritized Research Areas by Use Case

Use Cases and Research Areas

Arterial/Surface Streets

- Queue length detection - Virtual detection

Integrated Corridor Management

- Interagency coordination - Data sharing

Multi-Modal

- Pedestrian safety - Bicyclist safety

Lane Management

- Lane-level dynamic mapping - Lane availability

Connectivity and Early Automation

- MAP file creation - Position correction - Real-time signal optimization for groups

Railroad Crossing Violation Warning

- Advanced information for travelers - Advanced information for traffic engineers - Imminent safety at rail crossings

Detailed Descriptions

In the arterial and surface streets use case, stakeholders voiced a desire to improve the technology to accurately determine queue lengths and share this information in a standardized way. Queues at ramps and intersections can have a significant impact on travel time and route choice, so solving this challenge could greatly improve route planning applications. There are also expected to be challenges during the interim period in which a combination of CV-equipped and non-CV-equipped vehicles must share the roadway, which led to the need for virtual detection, or sensing of non-equipped vehicles by vehicles equipped as both CVs and with the ability to detect other vehicles.

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For the multi-modal use case, the primary area of interest was safety, particularly the safety of vulnerable road users sharing the right-of-way with larger vehicles. However, it will be important to distinguish between applications that can help improve pedestrian safety and those that can help improve bicyclist safety because the two types of users have very different speed profiles, safety needs, and travel behavior. During stakeholder engagement, there was less interest in pursuing multi-modal mobility applications, of pedestrians and cyclists as well as transit and freight vehicles. However, other mobility use cases and research areas could and should be applied across modes, either to benefit these vehicles in the same way as any others (i.e., imminent safety at rail crossings) or to provide special conditions to certain modes (i.e., lane availability for a transit-only lane or similar). Last-mile travel, of both people and goods, was another topic of interest to stakeholders under the multi-modal use case and may cover similar topics as the vulnerable road user research areas due to the size of such solutions (for example, delivery robots).

Deploying applications and enabling technologies to support connectivity and early automation was a significant area of interest for the stakeholder group. A major need is the standardization of MAP messages – both in terms of what additional information should be included (particularly for non-intersection MAP files) as well as how MAP files could be created in a more efficient and accurate way. There is also a need to ensure equipped vehicles are receiving sufficiently precise information, including research into how precise localization needs to be and what types of position correction could be used to meet this requirement. The required level of precision will likely depend on the type of application, with more accurate positioning required for safety-critical applications compared to mobility and environmental applications. In addition, strategies and best practices around GPS correction that build from the experience of other industries are now being applied to transportation. Lastly, there is interest in determining the capability and impact of signal optimization for groups (i.e., both platoons and strings) in real-time.

Integrated Corridor Management (ICM) is a concept many stakeholders have experience with, to varying degrees of success. The most important needs expressed for this use case tended to be policy-based rather than technological, though there are enabling technologies that could support these policy needs. This includes interagency coordination among stakeholders in the corridor as well as data sharing between these stakeholders and with private service providers and other partners. Stakeholders are interested in employing best practices learned from prior deployments and possibly other industries, to develop firm and comprehensive interagency agreements and deploy enabling technologies such as integrated and user-friendly data sharing platforms. There is less of an interest in focusing on the actual ICM applications, as these are not seen as the primary impediment to successful deployment at this time.

Lane management that includes precise, lane-level information on any conditions that override normal roadway features as well as data on current lane availability and restrictions will provide vital mobility and potentially safety-related information to advanced vehicle operations and/or navigation applications. While the creation of MAP files (discussed previously) is a cross-cutting issue that already comes into play during early pilots and deployments, as C/AV technologies continue to advance there will likely be additional needs for MAP files and other information on roadway geometry and conditions to be updated and communicated in real-time, using dynamic information sources including that which can be communicated by connected vehicles.

The railroad crossing violation warning use case is of interest to many stakeholders, who see a significant number of at-grade crossings in their jurisdictions and would be interested in new methods to improve safety and mobility at these locations. This includes imminent safety warnings (to warn of an impending conflict, providing a redundant set of information to help avoid the collision), advanced information for travelers (for route planning purposes), and advanced information for traffic engineers (for planning and analysis purposes). However, in most cases, the railroad right-of-way is under a separate jurisdiction, and may be managed by a private entity, which could lead to data sharing and other coordination challenges. This could impede the ability to deploy a solution quickly, even if the technology were ready to implement.

During the stakeholder workshop a vote was held to rank the 14 research areas under these six use cases. Votes were collected from participants using the survey tool Mentimeter, and 30 workshop participants in the room provided a response. Survey results are included in Figure 2-1. Participants listed three research areas and voiced that they generally did not prioritize between the three research areas they provided, so all three are weighted equally in the following graph. 13 of the 90 suggestions selected the broader use case and not the specific research area and are therefore not included in this visualization.

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Figure 2-1: Stakeholder Meeting Prioritization Exercise Results

In the subsequent discussion of results, those who participated in the vote revealed that they had tended to pick use cases and research areas they could work on right away, such as those that leverage existing deployments, rather than those that may not be feasible in the short- or mid-term. They preferred projects that could likely be implemented quickly, and where results would quickly be achieved. These preferences should be considered when creating the research plan and when determining the earliest steps to pursue within the overall, longer-term research framework.

The April 2019 stakeholder workshop provided an opportunity to validate CTCS work done to date and guide the project team’s next steps. Monthly stakeholder conference calls continue to be conducted to validate progress and provide a means for stakeholder engagement. Drafts of this Task 4 report and other deliverables are also circulated to the stakeholder email list and discussed at these monthly engagement meetings, with comments and responses documented to ensure feedback is integrated and substantiated in the report.

2.3 OVERVIEW OF TECHNOLOGY READINESS LEVELS Technology Readiness Level (TRL) assessments are a method of expressing the current maturity level of a technology on a scale from 1 to 9, with 9 being the most mature technology. This process can help to establish a clear understanding of current research and development efforts and gaps, influencing the decision on the most worthwhile path for future research. Table 2-2 defines the nine TRL levels. Also, as shown, the TRL scale can be approximately grouped into four categories: basic research, applied research, development, and implementation.

TRL assessments were originally performed for the six high-level use cases and documented in Task 3. As the outcomes of the workshop added one, if not more, levels of granularity to these original use cases in the form of research areas, including both applications and enabling technologies, it became necessary to consider the TRLs at the ‘research area’ level. A critical consideration for this project is the level of integration of C/AV systems with the existing technologies and practices, rather than a broader assessment of the research area as a whole. Section 3 documents the TRL at that level in order to support the next step in developing the research roadmap. There is not an optimal TRL that would determine whether or not a research area should be pursued further, rather, this process is used to help identify the needs and opportunities under each research area in order to provide guidance for the roles the PFS and other entities could play, in terms of bringing a technology closer to deployment or exploring ways to deploy it more widely and/or complexly.

0 2 4 6 8 10 12 14

Queue length detection

MAP file creation

Pedestrian safety

Real-time signal optimization for groups

Virtual detection

Imminent safety at rail crossings

Data sharing

Position correction

Lane-level dynamic mapping

Lane availability

Interagency coordination

Bicyclist safety

Advanced information for travelers

Advanced information for traffic engineers

12

34

56

78

91

01

11

21

31

4Prioritized Research Areas

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The project team will use the technology readiness level criteria and other factors to rate each potential research area. Once these assessments are identified and agreed to, activities will then be conducted to further develop the next actions to be taken as part of the research roadmap and concept development.

Table 2-2: Technology Readiness Levels1

TRL Definition Description

Bas

ic R

esea

rch

1 Basic principles and research

• Do basic scientific principles support the concept?

• Has the technology development methodology or approach been developed?

2 Application formulated

• Are potential system applications identified?

• Are system components and the user interface at least partly described?

• Do preliminary analyses or experiments confirm that the application might meet the user need?

3 Proof of concept • Are system performance metrics established?

• Is system feasibility fully established?

• Do experiments or modeling and simulation validate performance predictions of system capability?

• Does the technology address a need or introduce an innovation in the field of transportation?

App

lied

Res

earc

h

4 Components validated in laboratory environment

• Are end-user requirements documented?

• Does a plausible draft integration plan exist, and is component compatibility demonstrated?

• Were individual components successfully tested in a laboratory environment (a fully controlled test environment where a limited number of critical functions are tested)?

5 Integrated components demonstrated in a laboratory environment

• Are external and internal system interfaces documented?

• Are target and minimum operational requirements developed?

• Is component integration demonstrated in a laboratory environment (i.e., fully controlled setting)?

Dev

elop

men

t

6 Prototype demonstrated in relevant environment

• Is the operational environment (i.e., user community, physical environment, and input data characteristics, as appropriate) fully known?

1 https://www.fhwa.dot.gov/publications/research/ear/17047/001.cfm

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TRL Definition Description

• Was the prototype tested in a realistic and relevant environment outside the laboratory?

• Does the prototype satisfy all operational requirements when confronted with realistic problems?

7 Prototype demonstrated in operational environment

• Are available components representative of production components?

• Is the fully integrated prototype demonstrated in an operational environment (i.e., real-world conditions, including the user community)?

• Are all interfaces tested individually under stressed and anomalous conditions?

8 Technology proven in operational environment

• Are all system components form-, fit-, and function-compatible with each other and with the operational environment?

• Is the technology proven in an operational environment (i.e., meet target performance measures)?

• Was a rigorous test and evaluation process completed successfully?

• Does the technology meet its stated purpose and functionality as designed?

Impl

emen

tati

on

9 Technology refined and adopted

• Is the technology deployed in its intended operational environment?

• Is information about the technology disseminated to the user community?

• Is the technology adopted by the user community?

2.4 DEFINITION OF DEPLOYMENT READINESS Deployment readiness also includes institutional and other factors that are not limited to technical capability. These factors will also be considered as part of this evaluation. Discussion of relevant deployment readiness factors, including potential uncertainties and impediments, has been included as part of the discussions with stakeholders and throughout this report. Deployment readiness factors include but are not limited to:

• Technical capability • Cost • Benefit-to-cost ratio • Return on investment • Institutional/partner buy-in • Policy, regulatory, and political constraints

• Risk (of incomplete execution, of negative consequences, etc.)

• Data security and privacy • Dependency on wider CV fleet penetration • Scalability • Infrastructure readiness/compatibility

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3 PREVIOUS AND ONGOING RESEARCH AND DEVELOPMENT EFFORTS

This section contains a more in-depth assessment of previous and ongoing research in the ten highest priority research areas established in the stakeholder meeting. The other four research areas are also mentioned throughout and may still be considered for future research direction (especially as some are cross-cutting issues or technologies that touch many other research areas and use cases).

3.1 QUEUE LENGTH DETECTION A major need of the arterial/surface streets with traffic control and ramp meters use case is the ability to obtain and share information on the length of a queue at a ramp meter or other entrance or exit point to a limited access facility. This could support both safety applications (e.g. back of queue warnings) and mobility applications (e.g. eco-braking in advance of a queue or re-routing to alternate routes with a shorter travel time considering the length of the queue). Important considerations for further analysis include the level of C/AV penetration that may be necessary in a roadway fleet in order to reliably understand an entire traffic stream and whether connected vehicles can obtain information on other nearby vehicles to support this and other needs. Research in this area may also include distinctions between vehicles in the queue to enable variable metering and/or queue bypass lane access based on vehicle type, including transit but also other types of shared mobility such as carpools.

Queue length estimation, using connected vehicle and other technologies, is a topic that has been explored and researched quite extensively. However, most research to date has focused on freeway applications, rather than on arterials and ramps, which is the focus of this section. Queue length detection may have previously been done using infrastructure-based, Vehicle-to-Infrastructure (V2I) technologies, but in the future, there could be the ability to obtain and provide this information through a vehicle’s Basic Safety Messages (BSM). BSMs are a key component that will enable Vehicle-to-Vehicle (V2V) technology to be implemented without any roadside infrastructure. This could help resolve a limitation of infrastructure-based technologies, which can only detect whether a queue has reached a certain point and not how far the queue extends past that point, to be able to detect a queue on any segment of the roadway.

3.1.1 CONNECTED VEHICLE AS A MOBILE SENSOR FOR REAL TIME QUEUE LENGTH AT SIGNALIZED INTERSECTIONS2

Changsha University of Science & Technology (China), 2019.

RESEARCH OVERVIEW

Research reviewed tends to build from other more complex models to propose a queue length sensing model that uses V2X technology. Data sources include state information from connected vehicles to analyze the formation process of the queue and to predict the queue length of any unconnected vehicles as well as historical connected vehicle data. Simulation results show that the sensing accuracy of the combined model is proportional to the

2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538986/

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penetration rate of connected vehicles, and that sensing of queue length can be achieved even in low penetration rate environments.

RELEVANCE TO CTCS RESEARCH

The model developed for this paper strives to use connected vehicles as “mobile sensors” to collect traffic data and better manage a mixed fleet of connected and non-connected vehicles, particularly to reduce vehicle delays at intersections by estimating entrance queue lengths.

KEY OUTCOMES

The model developed in this research can, in general, sense queue length with high accuracy in mixed traffic environments, even at low penetration rates. For example, the model can achieve a sensing accuracy of 85% with 10% connected vehicles. However, this model is subject to limitations, for example that it requires at least one connected vehicle per cycle. In addition, the location of the connected vehicles matters, especially the latest connected vehicle in the queue. The author acknowledges that it would be useful to develop an algorithm that is less sensitive to the location of connective vehicles in the queue. However, the sensing model in this paper still has applications for flow prediction and signal management based on connected vehicles in the medium term when the penetration rate is sufficiently high.

3.1.2 REAL-TIME QUEUE LENGTH ESTIMATION FOR CONGESTED SIGNALIZED INTERSECTIONS3

University of Minnesota Twin Cities, 2009.

RESEARCH OVERVIEW

Traditional technology for queue length estimation can generally only estimate queues that are shorter than the distance between vehicle detector and intersection stop line. In this paper, instead of counting arrival traffic flow in the current signal cycle, the approach is to measure intersection queue length by measuring the queue discharge process in the immediate past cycle. This approach can estimate time-dependent queue length even when the signal links are congested, and queues are long.


This paper acknowledges that estimating queue length at signalized intersections in real-time is a problem that has not yet been thoroughly tackled or resolved, and that it can be especially challenging when the approaches are congested and queues are long – which may be the exact time such queue length data would be most helpful to warn of backups and provide travelers with alternate routes at a time when the decision can still be made to divert.

KEY OUTCOMES

The model developed in this paper is evaluated by comparing the estimated maximum queue length with field-observed ground truth data. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy, with some limitations. Additional research is necessary, but the results show that utilizing past cycle data can be an effective source of additional information.

3 https://www.researchgate.net/publication/222662104_Real-time_queue_length_estimation_for_congested_signalized_intersections

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3.1.3 USDOT CONNECTED VEHICLE PILOT DEPLOYMENT: TAMPA4

USDOT, 2019.

RESEARCH OVERVIEW

Tampa is one of three sites to be selected for the USDOT Connected Vehicle Pilot Deployment Program, which seeks to spur innovation among early adopters of connected vehicle applications. Starting in September 2015, Tampa began to explore connected vehicle-based solutions to resolve specific issues with morning backups, wrong-way entry, pedestrian safety, transit signal priority, streetcar conflicts, and traffic flow optimization. The morning backups, transit signal priority, and traffic flow optimization applications include queue length estimation components, either to enable the application or for performance measurement.


This project explored various CV technologies for queue detection, including V2I and V2V. Queue detection with V2I would include one roadside unit in the curve for speed-curve warning and a data link for advance warning at the exit where queues frequently occur. A V2V solution would involve brake warning and speed-curve warning applications. Performance would be measured by delay, queue lengths, and crash data. Results of this pilot should indicate whether these data sources are sufficiently accurate for reliable queue length information, and whether this information could then be used for additional purposes and applications. Consideration should also be given to the type of data that is provided, such as if it is raw or filtered data or if an open or closed source application provides the information, and whether this has an impact on the effectiveness of the system.

KEY OUTCOMES

Deployment is ongoing, but results will be shared once available. Early lessons learned recommended combining various sources of data and collection/management tools, some traditional and some emerging technologies, for best results.5 For example, using both roadside unit and the existing Advanced Transportation Controllers can allow data streams to be converted to more usable information such as queue lengths. This can also enable data sharing with traffic managers to improve the allocation of phase time, progression, platoon management, and other safety and mobility applications.

3.1.4 QUEUE SPILLBACK DETECTION AND CONTROL STRATEGIES BASED ON CONNECTED VEHICLE TECHNOLOGY IN A CONGESTED NETWORK6

California PATH, 2014.

RESEARCH OVERVIEW

This paper presents two queue spillback detection methods that are based on CV and/or probe data. The first method requires only CV data and assumes that non-equipped vehicles that arrive after the last CV-equipped vehicle can be modeled using a geometric distribution. The second method combines CV data with information about signal settings at the upstream intersection and is based on a kinematic wave theory of traffic. The authors also developed a signal control strategy to mitigate queue spillbacks once they were detected. These methods and strategies were tested through simulation.

4 https://rosap.ntl.bts.gov/view/dot/40144 5 https://rosap.ntl.bts.gov/view/dot/37681/dot_37681_DS1.pdf? 6 amonline.trb.org/1.2481044

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This paper acknowledges that queue spillbacks are a major problem in urban signalized arterials, in part because the effects can propagate and lead to gridlock and excessive delays elsewhere on the arterial. It explores a method to both estimate queue lengths and employ strategies to mitigate their impact.

KEY OUTCOMES

Results show the penetration rate thresholds of CV-equipped vehicles required for accurate queue detection. In addition, the proposed signal control strategy was shown to improve traffic operations for upstream cross streets without compromising traffic operations on either direction of the arterial traffic.

3.1.5 RESEARCH ASSESSMENT

Queue length detection is assessed to be a TRL 5 because there has been extensive research on the use of connected vehicles to determine queue length estimates for freeway applications that could be applied to similar applications on ramps and arterials. In addition, ramp and arterial queue length estimation has been researched independently, though to a lesser extent. Most research to date has used simulation models, and while results have shown that queue length detection systems are feasible with relatively low rates of equipped vehicles, additional research could strengthen this argument and provide guidance on the impact of the location of equipped vehicles in the queue. The location of CVs and non-CVs within a queue is more important for safety-critical applications, while mobility applications may be able to rely on the level of accuracy that has been available in prior research. In terms of deployment readiness, such a system could likely be deployed with the support of an existing or new infrastructure-based system relatively quickly, though higher fleet penetration of connected vehicles would be necessary before a fully non-infrastructure supported system could be effectively deployed.

Table 3-1: Queue Length Detection Technology Readiness Level

TRL Definition Comments


The research presented above includes examples of laboratory demonstrations specifically focused on connected vehicles. Operational deployments have not yet been fully implemented, and additional research is needed to explore the integration of CV data with other data sources as well as the minimum safe level of CV fleet penetration. The TRL may change when the results of the Tampa pilot become available.

3.2 MAP FILE CREATION A cross-cutting issue that currently impacts many different CV applications and deployments as a whole is the difficulty in creating MAP files. This applies to both intersections and other parts of the roadway in which providing location-specific mapping information to vehicles may be useful, such as midblock crossings and work zones. There is a need for standardization of digital MAP messages, a need for more detailed messages depending on application need and in conjunction an approach to deal with these larger messages, and a need for the ability for files to be updated in real-time or near real-time (particularly for work zones).

Intersection MAP files are currently created in many different ways, depending on agency and personal preference. In some cases, a site survey is used (which may include LiDAR), but in many cases a MAP creator will create a file remotely without a site visit using Bing Maps or a similar online tool, zooming in, and manually adding labels to lanes and other roadway markings and features. The strategy that many employ is to find a recognizable feature such as a manhole cover and use it to label where other objects are in relation to that object.

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USDOT has created a tool that they recommend be used for MAP and SPaT (Signal Phase and Timing) file creation.7 Instructions for the tool are available and were distributed widely especially for the SPaT challenge.8 Many stakeholders use this tool, but it has some limitations. For example, it does not include pedestrians and it does not use the most updated maps available or even allow the use of different types of maps. This is particularly challenging in work zones where a satellite map just a few days old may already be inaccurate. Overall, there is no real standardization of MAP files, and no real strategies to use for midblock, ramps, and other locations since the focus in the past has primarily been on intersections. There also hasn’t been enough of a discussion on how to reach centimeter-level accuracy that may be required for some vehicle applications, which ties into the position correction research area as well.

The average time reported to create a MAP file is around 10 to 15 minutes for experienced users, particularly when they have other internal tools they can use. Validation may require additional time but is necessary especially when the map being used is outdated. The existing tool does not allow the user to take advantage of all data frames and data elements that a user could possibly want or need in a MAP message, and it could be more robust. This existing tool is useful to many stakeholders but there are suggestions to improve it, and research is needed on how such improvements could be made and integrated into the existing and/or new standardized tools.

3.2.1 BASIC INFRASTRUCTURE MESSAGE DEVELOPMENT AND STANDARDS SUPPORT FOR CONNECTED VEHICLES APPLICATIONS9

Connected Vehicle Pooled Fund Study, 2018.

RESEARCH OVERVIEW

This research sought to develop a Basic Infrastructure Message (BIM); and to establish a means to collaborate with the relevant standards development organizations. The creation and standardization of a BIM, that would essentially be a standardized message similar to MAP that provides information on all aspects of the roadway infrastructure, not just intersections. In addition, this will also help the public transportation agencies to know what kind of information to broadcast from their Road Side Equipment (RSE). This white paper was one part of a larger project that proposes streamlining the process of creating MAP files.


As a CV PFS study, this paper demonstrates the existing desire to explore infrastructure message development for use cases beyond the intersection. This paper also reaches similar conclusions on the limitations of existing MAP creation tools.

KEY OUTCOMES

The paper recommends creating a tool to convert from open-source inputs to MAP messages. It also recommends creating roadway data definitions that fit within existing standards but are specifically designed for longer-distance roadways, so they can be used for many additional use cases. Lastly, they recommend creating a tool that provides a template for an intersection with pre-populated default values that would make the process of creating a file more efficient each time it is used.

7 https://webapp.connectedvcs.com/ 8 https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20-%20J2735%20MAP%20Tool%20Demo%20-%20NOCoE%20-%20Overall%20Slide%20Deck.pdf 9 http://www.cts.virginia.edu/wp-content/uploads/2018/12/Whitepaper2-MAP-20180425_Final.pdf

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3.2.2 STANDARDISATION OF SPAT AND MAP10

SWARCO, 2014.

RESEARCH OVERVIEW

The purpose of this project was to “make travelers and infrastructure act as a team adapting to each other and to the situation, creating optimized mobility conditions.” It consists of a collaborative team of partners across Europe and across industries that share goals and see a benefit in taking a standardized approach.


This initiative shows that other organizations outside the US are also developing roadmaps to standardize MAP and other messages. In particular, this demonstrates a collaboration between the automotive industry and infrastructure organizations in support of initial CV deployments in Europe.

KEY OUTCOMES

The group presented that data that should be included in a MAP file includes a topological definition of lanes within an intersection, a topological definition of lanes for a road-segment, a definition of the links between segments and the types of lanes, and any lane restrictions. An approach was also developed to reach standardization based on available standards, essentially building off what had been externally mandated or recommended to come to a collaborative agreement on what else should or could be included.


MAP file creation is assessed to be a TRL 8 because there are existing tools and procedures to create MAP files that are consistent across independent entities. However, there are many improvements that could be made to the existing tools and procedures, so it would likely be beneficial to develop a new technology that integrates various approaches and may currently be either at a lower TRL or entirely non-existent. For example, MAP file creation is currently primarily a human-driven, computer-based process and it may be possible to create a more automated procedure that limits the need for human oversight and validation, if existing limitations are worked through that currently make this a less favorable approach.

Working to deploying a new tool may reveal jurisdictional and adoption challenges, as agencies have varying ways to capture the supporting data, and vehicle manufacturers have not yet converged on a standardized approach to creating and using MAP files. Because travelers are accustomed to traveling across different jurisdictions and roadway types (including state lines and country borders) without compatibility issues, consistency will need to be established before a widespread solution can be deployed.

Table 3-2: MAP File Creation Technology Readiness Level


8 Technology proven in operational environment

A tool to create MAP files exists and has been proven, but additional refinement is needed to make the tool sufficiently user-friendly, efficient, flexible, and effective to be more widely adopted and enjoyed by users. A gap in existing tools is the ability to more fully automate the process, and to better tackle outlying use cases such as uncommon roadway geometry.

10 https://www.collaborative-team.eu/downloads/get/SWARCO%20standardisation%20activities%20in%20cooperative%20systems.pdf

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3.3 PEDESTRIAN SAFETY Pedestrian safety, while widely known as a vital and important need to be met on shared public roadways, is an issue that has not yet been fully be resolved for connected vehicles. This particularly applies to pedestrian activity outside of marked crosswalks and other locations where roadside detection equipment is likely to be installed. In addition, pedestrian infrastructure needs to be better defined in MAP and other file formats even at marked crossing locations with infrastructure-based detection. Pedestrian-based equipment, such as smartphones, are also an option that is being explored to better enable pedestrians to be detected by other types of roadway users. The focus of this research area remains broad, as there are many potential issues, enabling technologies, and applications that could be pursued, but at a high-level the focus is on improving the safety and visibility of pedestrians as vulnerable road users on roads shared with connected vehicles.

Similar to pedestrian safety is the safety of other vulnerable road users, most notably bicyclists. However, pedestrians and bicyclists tend to have very different travel profiles – in terms of where in the roadway they can be found (sidewalk/separated lane/shared lane), what types of roads they are likely to be using, their speed, and what assumptions should be made to predict their upcoming travel behavior. For this reason, it is recommended that pedestrian and bicyclist safety be researched and resolved separately to a certain extent.

3.3.1 ACCESSIBLE TRANSPORTATION TECHNOLOGIES RESEARCH INITIATIVE (ATTRI): STATE OF THE PRACTICE SCAN11

Carnegie Melon University, 2017.

RESEARCH OVERVIEW

The Accessible Transportation Technologies Research Initiative (ATTRI) focuses on research to improve the independent mobility of travelers with disabilities through the use of intelligent transportation systems (ITS) and other advanced technologies.12 This report is one of three intended to provide an overview of technologies, innovations, and research that are applicable to the ATTRI vision. The particular focus of this report is on a State of the Practice Scan, to survey technologies that are currently in use on a wide scale within the United States.


This scan reinforces the need for additional research to allow for limited-mobility travelers, particularly pedestrians, to safely interact with vehicles, whether present day, connected, or automated. This includes wayfinding and navigation support technologies, automated vehicles (particularly those that help with first-mile/last-mile travel), and other assistive technologies (such as automated wheelchair lifts and securements and methods of detecting pedestrians that may need additional time in a crosswalk).

KEY OUTCOMES

This document also contains characterizations of the challenges that face the stakeholders. Descriptions of user experiences with SOP technologies are determined by broad surveys of the user population. This common understanding allows for the ability to understand and meet the needs of the underserved vulnerable road user population.

11 https://www.ri.cmu.edu/wp-content/uploads/2017/04/1_ATTRI_SOP_2017-04.pdf 12 https://www.its.dot.gov/research_areas/attri/index.htm

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3.3.2 DISCUSSION GUIDE FOR AUTOMATED AND CONNECTED VEHICLES, PEDESTRIANS, AND BICYCLISTS13

Pedestrian and Bicycle Information Center, 2017.

RESEARCH OVERVIEW

This discussion guide addresses the content gap of including pedestrians and bicyclists in US and international articles about connected and automated vehicles. The guide presents ten key “challenge areas” to broaden the discussion and provide a high-level summary of the types of issues and needs pedestrians and bicyclists will likely have when traveling on roadways shared with connected and automated vehicles.


This discussion guide confirms many of the limitations facing pedestrian and bicyclist safety as connected and automated vehicles are introduced on shared public roadways.

KEY OUTCOMES

The ten challenge areas are limited detection capabilities, issues with current V2X technology, the difficulty of communicating intentions, inconsistencies on who has right of way when, passing safety (particularly for bikes), the speed differential (and the nonlinear decrease in safety of higher speed collisions), pick up and drop off space conflicts, the handoff time required for drivers to step in for vehicles at low levels of automation, mode shift concerns (and the corresponding changes in land use that could make walking and biking less attractive travel options), and data sharing issues.

3.3.3 USDOT CONNECTED VEHICLE PILOT DEPLOYMENT: NEW YORK CITY14

NYC DOT, 2019.

RESEARCH OVERVIEW

Two of the applications being pursued as part of the federally funded New York City DOT Connected Vehicle Pilot relate to pedestrians – the pedestrian in signalized crosswalk warning and the mobile accessible pedestrian signal system. The Tampa deployment mentioned previously also has a pedestrian safety application.


This pilot provides examples of pedestrian safety applications that are actively being deployed and could help define the path for future research. One application is designed to support the visually challenged by utilizing intersection geometry and signal timing information transmitted by the intersection to help the pedestrian cross the street. The other application is a more traditional pedestrian detection technology, designed to determine if there is a potential conflict between an approaching CV and a pedestrian in a crosswalk.

KEY OUTCOMES

The project has provided specification for these pedestrian safety applications and is currently in the operate and maintain phase of the project. Results will be shared when available.

13 http://www.pedbikeinfo.org/cms/downloads/PBIC_AV_Discussion_Guide.pdf 14 https://cvp.nyc/cv-safety-apps

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3.3.4 SMARTCROSS15

Savari, 2016-2019.

RESEARCH OVERVIEW

Savari developed a tool called SmartCross that was deployed as part of the MMITSS project.16 This is a vehicle-to-phone application that enables a Mobile Accessible Pedestrian Signal System (PED-SIG). The primary focus is pedestrian mobility – a pedestrian could use their smartphone to send a signal request message (SRM) which would place a pedestrian call to the intersection signal controller. The pedestrian would then receive a signal status message (SSM) that acknowledges the receipt of the pedestrian SRM, and they may receive priority, reducing their wait time.


This study and deployment were supported by the CV PFS, demonstrating a historic interest in pedestrian safety and mobility applications that has carried into the current desire for additional research. While it is mostly focused on pedestrian mobility, there are also some findings relevant to pedestrian safety, and many of the enabling technologies overlap.

KEY OUTCOMES

While the PED-SIG application was demonstrated successfully, it could be enhanced. Adding the ability to send automated calls would reduce the need for pedestrians to press a button. The pedestrian application would also be improved by adding the enhanced ability to send a pedestrian safety message (PSM) to support other safety applications, like conflict warnings for turning vehicles. In addition, the final report stated that integration with other connected vehicle applications could provide additional opportunities for other compatible and beneficial applications in future simulations and test beds.

3.3.5 SMART WALK ASSISTANT17

University of Arizona, 2018.

RESEARCH OVERVIEW

Conflicts between vehicles and vulnerable road users (VRUs) often result in injuries and fatalities. This paper presents a V2I and Wi-Fi based pedestrian conflict avoidance system to improve VRUs’ safety. Compared with the vision-based systems, the proposed system can improve VRUs’ safety in non-line-of-sight situations. In particular, it can be helpful in a situation when drivers are making a right or left turn where there is a crosswalk and visibility conditions are poor. The system, called Smart Walk Assistant, was implemented as an application on a smartphone and server on a roadside unit. The P2V application was designed and developed and will be field tested in the near future.


This study and deployment were supported by the CV PFS, demonstrating a historic interest in pedestrian safety and mobility applications that has carried into the current desire for additional research and testing for use cases involving vulnerable users.

15 http://savari.net/solutions/smart-phone/ 16 http://www.cts.virginia.edu/wp-content/uploads/2014/04/53-MMITSS-Phase-2-Final-Report-FINAL-092520161.pdf 17 https://doi.org/10.1177/0361198118783598

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KEY OUTCOMES

The Smart Walk Assistant system may be especially beneficial to differently abled pedestrians, including blind or visually impaired pedestrians, who would benefit from active support to safely cross streets at signalized intersections.


Pedestrian safety is assessed to be a TRL 6 because there have been limited pilot deployments of pedestrian safety applications using connected vehicle technology in a limited number of places. However, the effectiveness of these applications is still to be proven, and they do not address all types of conflicts pedestrians are likely to encounter when operating on shared public roadways. Similarly, they do not address all types of pedestrians, who may have varying wants and needs. Additional research is needed to identify the specific gaps that remain and how to resolve the remaining technological and policy issues. There may be privacy concerns, particularly with the use of mobile devices, as well as a risk of pedestrian overdependence on these systems (i.e., a pedestrian believing themselves to be detected by the system may make a legal but personally dangerous movement). In addition, many solutions are costly, making it difficult to deploy at a large scale.

Table 3-3: Pedestrian Safety Technology Readiness Level



The necessary equipment and technology that would enable pedestrian safety use cases have been identified, but there have been mixed results on the feasibility of implementing these tools and strategies. In particular, the infrastructure cost necessary for each deployment has precluded more widespread use, and the ability of mobile phones or other existing devices to be a more cost-effective solution is limited by the inaccuracy and unreliability of location data from these devices.

3.4 REAL-TIME SIGNAL OPTIMIZATION FOR GROUPS The stakeholder group acknowledged that vehicle platooning and vehicle stringing are both CV applications that are likely to be deployed relatively early and will demonstrate measurable benefits. However, long-term research is needed to determine the impact of how changing signal timing schemes for groups of C/AVs may impact the safety and mobility of that group of vehicles as well as other vehicles and roadway users. There may be environmental and congestion mitigation benefits as well.

This line of research should be relatively easy to deploy when ready, as it should be possible to leverage the ability of traffic signal operators to measure performance and assess traffic impacts in real-time and/or over longer periods. Traditionally, actuated signals using detectors upstream of the signal could detect breaks in traffic before turning red to limit the disruption to traffic flow and increase efficiency. This research area could further improve such a system by allowing vehicles that are connected to coordinate their travel behavior and speeds with each other, when approaching at the same or different approaches, further optimizing the signal timing system in real-time in response to actual demand.

Similar to large and medium sized freight and other vehicles operating in platoons and strings, there is also a possibility of altering signal timing to enable better mobility and safety for delivery robots, pedestrians, cyclists, and other smaller first/last mile operators, while considering the impacts on other traffic.

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3.4.1 INTERSECTION MANAGEMENT VIA VEHICLE CONNECTIVITY: THE INTERSECTION COOPERATIVE ADAPTIVE CRUISE CONTROL SYSTEM CONCEPT18

Virginia Polytechnic Institute and State University, 2014.

RESEARCH OVERVIEW

This article describes a cooperative adaptive cruise control (CACC) system, designed to improve connectivity, safety, and mobility by allowing vehicles to travel in denser groups using V2V communications. This system, which is named iCACC, assumed that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimal course of action to ensure no collisions occur, while minimizing intersection delay to the extent possible.


The research presented in this article worked to develop a simulation and optimization tool to model the movement of CACC-equipped vehicles instead of using traditional intersection control. It demonstrated that if the system is optimized at the intersection level (rather than for individual platoons/strings passing through the intersection), then it may be possible to enhance mobility for all intersection travelers by allowing them to pass through at an optimal time, rather than in phases within a cycle.

KEY OUTCOMES

Four intersection control scenarios are compared: traffic signal, all-way stop control, roundabout, and the iCACC controller developed as part of this research. The results showed that the proposed iCACC concept significantly reduced the average intersection delay and fuel consumption by 90 and 45%, respectively. The research also investigated the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles. Additional CACC research is reviewed in the CTCS Task 3 report, showing similar results.

3.4.2 ADVANCED TRAFFIC SIGNAL CONTROL ALGORITHMS, APPENDIX A: EXPLORATORY ADVANCED RESEARCH PROJECT: BMW FINAL REPORT19

University of California, Berkeley, 2013.

RESEARCH OVERVIEW

This project strove to build a prototype system that reduced fuel consumption for vehicles traveling through a series of traffic signals. The system included an in-vehicle component that provided a recommended speed using current SPaT information. This speed was provided to the driver using a graphical interface. The position data of the vehicle was also sent to an Adaptive Priority for Individual Vehicle system, which is an operational strategy that adapts signal timing to facilitate the movement of individual vehicles through signalized intersections.

18 https://www.tandfonline.com/doi/abs/10.1080/15472450.2014.889918 19 https://trid.trb.org/view/1323846

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While this research did not address vehicle platooning and stringing directly, it demonstrated the ability to modify signal timing to enable more efficient movements through an intersection. While it was only performed for a single vehicle, it could likely be expanded to groups of vehicles.

KEY OUTCOMES

The results showed that drivers could reduce fuel consumption by 12% by using SPaT guidance. A similar message could be sent to drivers in a vehicle string to enable not just optimized travel speeds but also vehicle grouping, for drafting, mobility, and safety purposes. The system also helped reduce the number of stops at red lights and the associated intersection delay, resulting in a reduction in travel time.

3.4.3 GLIDEPATH PROTOTYPE APPLICATION20

USDOT, 2015.

RESEARCH OVERVIEW

In support of USDOT CV research, and in particular the Applications for the Environment: Real-Time Information Synthesis (AERIS) program, a prototype application called GlidePath was developed. This application provides automated longitudinal control to optimize environmental performance of a vehicle approaching a signalized intersection.


This prototype, while focused on a single vehicle, demonstrated the potential benefits of eco approach at intersections in the real world, showing the feasibility of making adaptations in real-time to enable more efficient operations at intersections.

KEY OUTCOMES

Depending on the phase timing, the vehicle knows to maintain its speed, to increase or decrease its speed within safe limits in order to avoid having to stop, or to slow down early and coast if the signal will inevitably be red when it is expected to arrive at the intersection. All of these scenarios demonstrated environmental benefits.


Real-time signal optimization for groups is assessed to be a TRL 3 because the proof of concept has been demonstrated through the ability to optimize signal timing for individual connected vehicles, as well as for an intersection as a whole. However, there is limited research and development that currently exists towards a product that could be deployed to benefit groups specifically. Additional research is needed in particular to determine the extent to which it would be possible to do so without negatively impacting other traffic to too significant of a degree. This could be easily deployed in jurisdictions that already have adaptive signal timing systems but may not be as easy to deploy everywhere (especially in a consistent manner). It also may be difficult to deploy for groups of vehicles that include CVs and non-CVs.

20 https://www.its.dot.gov/research_archives/aeris/glidepath.htm

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Table 3-4: Real Time Signal Optimization for Groups Technology Readiness Level


3 Proof of concept The proof of concept of signal optimization for connected vehicles has been shown in prior research on eco-approach at intersections, but it was only done for individual vehicles. Additional research and deployments for groups of vehicles could build on these results for further applications.

3.5 VIRTUAL DETECTION A challenge facing connected vehicle technology is the ability to detect vehicles that are not equipped during the interim period in which there is a mix of connected and non-connected vehicles on the roadway (as well as automated and non-automated vehicles). There are various methods of detecting non-equipped vehicles, including some that are infrastructure-based and others that are vehicle-based (essentially equipped AVs sharing information about other vehicles). It will be important with any system that is pursued and/or deployed to ensure that it does not double count any vehicles, whether they be equipped or not. Data fusion from multiple sources could be performed to rectify location and motion properties for vehicles that are detected more than once and could also provide importation on the fleet penetration rate of CVs.

Meeting this need is essential to enabling many of the other mobility and safety applications presented throughout this report. The benefit of a vehicle-based virtual detection system over an infrastructure or ITS-based system is that it could more easily be implemented through a network, rather than just at a single link.

3.5.1 DETECTOR-FREE OPTIMIZATION OF TRAFFIC SIGNAL OFFSETS WITH CONNECTED VEHICLE DATA21

Purdue University, 2017.

RESEARCH OVERVIEW

This research demonstrated that signal offset optimization can be feasible with vehicle trajectory data at low levels of market penetration. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for intersections in two corridors.


This research showed how data from connected vehicles could be used to revolutionize how traffic is managed. In particular, the study demonstrated that probe vehicle data could be used as a proxy for CV data in optimizing offsets. A potential application of this study’s findings is use on corridors that lack the infrastructure for physical detection.

The report states that future work in this area could include refining the geometric analysis further to better filter erroneous data, conducting additional comparisons of match quality over different sampling periods, expanding to multimodal applications, and investigating the impact of the operating environment on the data characteristics. These could be potential directions for CTCS research.

21 https://pdfs.semanticscholar.org/afbd/b92407ff9b64fc8a0e6483a689d510fba354.pdf

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KEY OUTCOMES

After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, and arrival profiles were compared statistically with those measured with physical detectors. Most approaches showed statistically significant goodness of fit at a 90% confidence level. These results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data.

3.5.2 VIRTUAL DETECTION AT INTERSECTIONS USING CONNECTED VEHICLE TRAJECTORY DATA22

Purdue University, 2016.

RESEARCH OVERVIEW

While striving to find less resource-intensive alternatives to vehicle detectors and communication equipment, this study explored the use of CV data for signal performance measurement at various levels of market penetration. In particular, this study explored whether CV trajectory data could currently be used for this purpose. To do this, the study compared in-pavement detector data with “virtual” detector data based on geo-fencing of trajectory splines.


This study explored the use of CV data for signal performance measurement, though it does not appear to have analyzed this data in real-time, and rather over longer time periods after the fact. However, this study demonstrated the ability to successfully use CV data for this purpose, even at relatively low rates of market penetration.

KEY OUTCOMES

The market penetration rate of CVs in this study varied between approximately 0.6 and 2%. The results showed possibilities for analyzing coordinated operations with penetration rates as low as 1-5%.


Virtual detection is assessed to be a TRL 3. The benefits of this technology have been shown in simulation, and the concept has been shown to be feasible given high enough market levels of CV penetration (which have been shown to be surprisingly low). However, prototyped technologies have not yet been widely developed or deployed. Some proprietary solutions have been developed, and it is possible that this technology is at a higher TRL behind closed doors. However, a public deployment and/or publicly-funded research would help move this research area forward in terms of demonstrating actual, real-world results and increasing public confidence in connected vehicles at low levels of fleet penetration. However, vehicle manufacturers may still be hesitant to participate and to share data, especially if they could be held liable for any errors.

Table 3-5: Virtual Detection Technology Readiness Level


3 Proof of concept The research presented in this section shows the concept is technically feasible, but additional research and pilots are necessary to demonstrate the benefits and limitations of virtual detection in real-world conditions.

22 https://ieeexplore.ieee.org/document/7795969

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3.6 IMMINENT SAFETY AT RAIL CROSSINGS There are currently 208,303 highway-rail grade crossings in the US. In 2018, there were 2,214 highway-rail collisions at these crossings.23 Safety at highway-rail crossings is an issue that affects many different stakeholders in many different environments, from unsignalized crossings in rural locations to signalized crossings in dense urban areas where both light and heavy transit rail are operated in the same right-of-way as vehicular traffic.

In 2015, more than half of public at-grade crossings had automatic warning systems (flashing lights and/or gates) but very few private at-grade crossings did. Still, more than 60% of collisions occurred at crossings with automatic warning systems. A study on driver behavior using cameras installed in cars found that 46% of the time, drivers were likely engaged in secondary tasks and 35% of the time, drivers failed to look either left or right on approach to passive grade crossings.24 In general, 94% of train-vehicle collisions could be attributed to driver behavior or poor judgement. An advanced warning system that provides another source of information to travelers, perhaps an in-vehicle notification to immediately and directly alert the driver, could help further improve safety. This could also be a valuable data source for automated vehicle systems.

The solution would likely include train-to-infrastructure and infrastructure-to-vehicle communications, as this would be a more feasible solution than direct train-to-vehicle communications because a train likely would not deploy the same type of technology as a vehicle, particularly given the distances over which trains travel. For example, Positive Train Control could possibly be used as a data source for train location information, which would be sent to the infrastructure and then passed on to the vehicle via a message the vehicle system could understand.

Other research areas related to highway-rail crossings discussed during the stakeholder meeting were advanced information for travelers for route planning purposes and advanced information for traffic engineers for planning purposes. However, these research areas were of low interest to stakeholders because they are not safety-critical and do not seem feasible due to political and policy constraints. From stakeholder experience having worked on advance warning systems, many private railroads companies maintain that their data is private, and that they won’t share it for security reasons. In some ways this is more of a policy-level issue, as the technology could be available if data sharing agreements were in place.

3.6.1 TRAINFO25

TRAINFO, 2019.

RESEARCH OVERVIEW

TRAINFO has three products:

- Blockage Insights, that provides information on when and how long a highway-rail crossing is blocked (available in real-time)

- Congestion Analytics, a tool to help planners understand the traffic impacts highway-rail crossings have caused

- Mobility, an application designed to help reduce traffic delays caused by blockages at highway-rail crossings

Some or all of these products have been deployed in Seattle, London (Canada), Winnipeg, and Vancouver.

23 https://safetydata.fra.dot.gov/OfficeofSafety/Default.aspx 24 https://www.fra.dot.gov/Elib/Document/14371 25 http://trainfo.ca/

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This shows there is a market-ready solution to meet some of the railroad crossing needs. However, the applications TRAINFO has deployed are more focused on the travel planning applications, rather than imminent safety, which was of highest interest to stakeholders.

KEY OUTCOMES

In Winnipeg, TRAINFO integrated their information into Waze and a dynamic message sign located 1.5 kilometers ahead of the crossing to allow drivers to choose alternate routes if the crossing is blocked.26 Preliminary results indicated a 25% reduction in congestion and greenhouse gas emissions as well as positive feedback from drivers, including that they were more relaxed knowing when the crossing will be blocked, that they no longer believe the infrastructure investment of an underpass is needed, and that this will save them time because they can easily check the information to decide whether and when to re-route.

3.6.2 USDOT CONNECTED VEHICLE SAFETY FOR RAIL27

USDOT, 2019.

RESEARCH OVERVIEW

USDOT has a Connected Vehicle Safety for Rail initiative that is intended to undertake research to enhance the safety of trains and all other vehicles at highway-rail crossings. This research focuses on integrating DSRC hardware with existing railroad safety systems so crossing status can be broadcast to approaching connected vehicles and interpreted by vehicle onboard equipment and communicated to a driver.


This USDOT program is also engaging stakeholders to develop a Concept of Operations and other systems engineering documents for safety and mobility improvements at highway-rail crossings. This progress and insights should be monitored to determine whether topics CTCS is considering including or pursuing in the research plan are already being tackled.

KEY OUTCOMES

This research is intended to work towards improving safety by considering human performance in railroad operations, as well as deploying emerging technologies to help meet existing needs. Results will be shared as they become available.

3.6.3 2017 GRADE CROSSING RESEARCH NEEDS WORKSHOP28

Federal Railroad Administration, 2017.

RESEARCH OVERVIEW

The FRA hosted a Highway-Rail Grade Crossing Safety Workshop in St. Louis in August 2017. The workshop brought together national and international subject matter experts, including representatives of federal, state, and local governments as well as railroad, labor unions, academia, non-profit organizations and consultants.

26 https://issuu.com/cite7/docs/tt39.4-winter201718/33 27 https://www.its.dot.gov/research_archives/safety/safety_rail_plan.htm 28 https://rosap.ntl.bts.gov/view/dot/39204

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Workshop participants identified and shared existing best practices and explored new strategies that the rail industry could adopt to reduce the number of highway-rail grade crossing incidents.


The topic of connected vehicles, including standards and protocols as they apply to highway-rail crossings, was mentioned by a number of presenters. One recommended project is to research “in-vehicle warning using connected vehicle infrastructure with the ability to enforce stopping,” which is directly relevant to the imminent safety research area.

KEY OUTCOMES

This was primarily a knowledge sharing workshop that began to work towards the development of a research plan but does not directly have any research outcomes or results.


Imminent safety at rail crossings is assessed to be a TRL 5 because while this technology has been demonstrated in the real world, it has not always included connected vehicle technology. There are also limitations on the ability to expand existing systems due to a lack of data sharing agreements and other coordination with private railroad companies. A CV-based solution would likely be V2I (car to track-based infrastructure that can detect a train’s presence) rather than V2V (a car communicating directly with a train) due to challenges with trains operating across many jurisdictions and being of varying lengths. Such a solution has higher deployment readiness, could integrate with other existing systems such as positive train control, and would likely be less costly with similar benefits, even in the long term.

Table 3-6: Imminent Safety at Rail Crossings Technology Readiness Level



While imminent safety warnings of varying types exist at at-grade railroad crossings throughout the country, a CV-based solution has not yet been demonstrated in real world conditions, though its feasibility has been shown in research activities to date.

3.7 DATA SHARING Data sharing between different public agencies as well as private partners is an important component that would enable many of the other research areas described throughout this report. In fact, many of the other research areas may not be possible if this component is not first worked out. Data sharing is also one of the most important aspects of interagency coordination, and one that is a technology issue as well as a policy issue. Data security and privacy concerns are another potential impediment to deployment readiness, for this research area and also for others, as more widespread data sources become available but may not be allowed to be shared for transportation purposes.

CVs have the potential to collect a wealth of data on roadway infrastructure and congestion and could collect and possibly share information such as geolocated spots on the roadway with poor pavement or aggregated vehicle speed information to determine current roadway speeds. Enabling policies and procedures to enable such data sharing would need to be worked out. Another distribution and collection point for roadway data is at traffic signals. For example, information on current signal status and time remaining until the next phase change, generally in the form of SPaT data, could open the door to critical safety applications in vehicles with the potential to significantly reduce and/or eliminate crashes at intersections. However, many traffic signals across the country use outdated technology, and may need to be updated before they can be connected to a wireless

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communication system, produce the data necessary for SPaT, and communicate with vehicles and with each other.

Another type of data that could be shared is that collected by private mobility and C/AV technology companies. Such data is used in many different forms, such as for real-time operations and in order to assess current performance and identify areas of future development. This data would be valuable to public agencies as they strive to assess the current capabilities of C/AV systems and under what conditions they should be permitted to operate on public roadways. However, many of these private mobility companies are in direct competition with each other and are therefore sensitive to sharing data they may see as proprietary in a public setting. Many vendors are more willing to share data when they are contracted to provide a service (and receiving payment or other special permissions) and under a non-disclosure agreement, but they are unlikely to share all data even under these conditions.

This research area includes the desire to improve enterprise data management and modernize existing systems. There was also discussion of the value of private partners such as Google and Waze, in that they are already widely adopted by travelers, but that there must be some consideration to the differences between their abilities and goals and those of public agencies.

3.7.1 REGIONAL INTEGRATED TRANSPORTATION INFORMATION SYSTEM (RITIS)29

RITIS, 2019.

RESEARCH OVERVIEW

The Center for Advanced Transportation Technology Laboratory (CATT Lab) at the University of Maryland administers the Regional Integrated Transportation Information System (RITIS).30 RITIS is a transportation-focused data aggregation and dissemination platform that contains many analytical tools and features. It includes various data types as inputs, crowdsourced Waze data and an initial exploration of C/AV data.


While RITIS began as a way to help coordinate the real-time operations between Maryland DOT, Virginia DOT, D.C. DOT and WMATA, the system has since expanded to other states and other countries. Data is not completely open and is only shared between agencies that agree to share with each other. This demonstrates the willingness of various partners to share their data if they see value in doing so, and in gaining access to the various tools that are only enabled if a wide variety of partners also share their data. C/AV data has been added more recently, and only at low levels due to the currently low market penetration rates of such technologies.

KEY OUTCOMES

RITIS can be used for operations planning, active operations management, long-range and capital planning, research, leadership, and traveler information. It has expanded beyond its original scope, but there are still issues with getting data from all possible sources, especially private transportation providers that are becoming increasingly more prevalent on the roads. While data feeds are updated in real-time, it may not be at sufficient latency to enable C/AV applications.

29 https://www.ritis.org/intro 30 https://www.cattlab.umd.edu/?portfolio=ritis

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3.7.2 I-210 CONNECTED CORRIDORS31

Caltrans, 2019.

RESEARCH OVERVIEW

Connected Corridors is an ICM program that builds off other successful ICM programs to investigate tools and technologies to coordinate components of the corridors to operate them as a cohesive and integrated system. The goal is that by empowering drivers and traffic managers to make more informed decisions, a new set of performance management strategies will be made possible and more cooperative traffic management will result.


While this project does not directly address connected vehicle technology (other than sending traveler information to mobile applications), it is a good example of coordination between stakeholders to enable better data sharing that allows new types of strategies to be pursued and their performance to be measured in real-time or after the fact. Data from connected vehicles could likely be integrated into such a strategy.

KEY OUTCOMES

The types of data collected for this project include geometry of intersection approaches (from maps or aerial photos on Google Maps, which required considerable time and effort), location of traffic sensors (provided by traffic managers and verified on Google Maps), available signal timing sheets and intersection detection layout from Caltrans, LA County, and local jurisdictions, available five-minute traffic counts from the Arcadia TransSuite system and available vehicle detections from the Arcadia Bluetooth sensor network, a one-week sample of signal timing and 15-minute traffic flow data from the Pasadena i2 centralized traffic signal control system, reports on traffic impact studies that have been conducted for proposed projects within the I-210 corridor over the past eight years, and a synchro model developed by Iteris. Some of these data sources are more precise and detailed than others, and some took significant project team effort to convert into a usable format.

3.7.3 TRANSCOM32

TRANSCOM, 2019.

RESEARCH OVERVIEW

TRANSCOM (Transportation Operations Coordinating Committee) is a coalition of 16 transportation and public safety agencies in New York, New Jersey, and Connecticut. This coalition was created in 1986 with the goal of providing a cooperative, coordinated approach to regional transportation management. Today, this includes a regional transit trip planning application, a data feed, and other traveler information, as well as tools that can be used by members for transportation planning.


This research demonstrates that there has been a strong interest and effort in developing a wider regional approach to data sharing and transportation management for at least 30 years.

31 https://connected-corridors.berkeley.edu/sites/default/files/ICM%20Deployment%20Planning%20Grant%20for%20I-210%20Pilot.pdf 32 https://www.xcm.org/XCMWebSite/Index.aspx

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KEY OUTCOMES

Based on the Concept of Operations, member agency data is supplemented by some third-party data depending on application need.33 Overall, the primary goal of TRANSCOM is to develop a common platform for agencies to exchange transportation information on incidents, construction, special events, roadway and transit network status, parking occupancy, and message sign data using open standards.


Data sharing is assessed to be a TRL 6. There are a limited number of operational deployments in the country, all of which have had challenges but have reached some degree of success. Policy barriers continue to exist, as do data privacy concerns (i.e., many tools are only open internally to transportation agencies and there may be an entirely different conversation when it comes to allowing these tools to send information to all members of the public with connected vehicles, or to private transportation providers as part of an ICM or similar strategy). Data ownership is also an issue, as some potential providers may see higher value in using their data for other purposes or they may see a risk in sharing their data on a platform that could be viewed by their competitors. This is a technology that is technically possible (though it could be improved), but policy issues have precluded it from more widespread deployment and from the transition from prototype to full operational deployment.

Table 3-7: Data Sharing Technology Readiness Level



The three platforms identified in this section have prototyped the type of data sharing that will be necessary to enable many CV-based applications; however, none have had the ability to benefit from and/or fully integrate C/AV data into their systems due to low levels of availability of such data. Moving this research area into a higher TRL may require waiting for higher levels of fleet penetration because even if the ability to use C/AV data is demonstrated, it will be an entirely new challenge to do so at a larger scale.

3.8 POSITION CORRECTION As automated vehicle technologies continue to be developed, there is a growing question on how precise the information they receive and use to make decisions really needs to be. Additional research is needed on this topic, as well as on how precise the information that can be provided already is and whether multiple data sources could be combined to provide a higher level of confidence. GPS has randomness that is challenging to account for, but corrections for atmospheric conditions that result in a bias in positioning may be easier to resolve. This includes corrections from in-vehicle devices and software as well as from field infrastructure.

This field of research could result in additional V2I requirements or standards on what information needs to be shared with vehicles, and how precise it needs to be, especially in locations where intersection or other roadway infrastructure is complex or unexpected (such as work zones).

33 http://www.infosenseglobal.com/wp-content/uploads/2018/04/transcom-systems.pdf

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3.8.1 CONNECTED VEHICLE PILOT POSITIONING AND TIMING REPORT: SUMMARY OF POSITIONING AND TIMING APPROACHES IN CV PILOT SITES34

USDOT, 2018.

RESEARCH OVERVIEW

Position and timing related information from the three USDOT Connected Vehicle Pilot Sites during early deployment is summarized in this report, as it was of interest to project stakeholders and others observing the pilot deployments. The general approach of each site began with the identification of requirements, which may directly or indirectly include timing and position accuracy requirements, that are expected to be satisfied by the vendor equipment. For V2V safety applications, the expected requirement is to satisfy SAE J2945/1 section 6.2, which covers Positioning and Timing Requirements.


These pilot deployments all had challenges with positioning and are working together to determine the extent of the issues and collaborate on how to resolve them. They noted that the state of positioning technology is currently evolving, and capabilities and costs are likely to continue to change rapidly. Currently, there is a significant tradeoff between costs and capabilities, making many more effective solutions quite unaffordable, but that is likely to not always be the case.

KEY OUTCOMES

There are several types of differential positioning augmentation techniques discussed in this document. These approaches generally involve providing correction information collected from other fixed locations to a local positioning receiver. The NYC site has had the largest challenges, as it is being deployed in an urban canyon. This project therefore defined additional requirements to meet this need that were not as applicable to the other sites, with the exception of a few locations in Tampa.

For all techniques being explored, it is important to realize that the local receiver needs to be able to handle the type of corrections needed for a given method. Not all receivers are capable of using all corrections, and not all receivers perform equally well under different conditions.

3.8.2 EVALUATION OF VEHICLE POSITIONING ACCURACY BY USING GPS-ENABLED SMARTPHONES35

University of Alberta (Canada), 2014.

RESEARCH OVERVIEW

Given such a high smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic data monitoring has recently attracted significant research attention. This study presents a field experiment using a GPS-enabled smartphone, a cellular positioning technique, a professional GPS handset, and a combination of smartphones and geo-fences. The relative positioning errors between these devices were estimated through experimental design and evaluated in three scenarios.

34 https://rosap.ntl.bts.gov/view/dot/35427/dot_35427_DS1.pdf 35 http://pubsindex.trb.org/view/2014/C/1287871

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The high penetration rate of smartphones incorporated with the high location accuracy of GPS receivers will provide a better estimation of locations and traffic conditions and states that could be applied to what is to be expected upon wider adoption of CV technology and vehicle onboard units. Onboard units similarly rely primarily on GPS and GNSS, for which the position information is not always reliable and accurate.36

KEY OUTCOMES

The results suggest that GPS-enabled smartphones are capable of correctly positioning nearly 100% of the roadway segments to Google Earth, while achieving accuracy of within or less than 5 meters for 95% of the data. In all scenarios, the use of four data sources for obtaining locations and traffic data was shown to be feasible; and particularly, using GPS-enabled smartphones on their own or in combination with geo-fences can provide better location accuracy.


Position correction is assessed to be a TRL 5 because there has been some research demonstrating the ability to do so, for existing systems such as smartphone GPS, as well as for CV onboard units. However, the level of position correction and position accuracy needed are both still open questions, so additional research should help to both determine the extent of the need as well as prototype and develop additional technologies and strategies to help meet this need. Institutionally, guidance on a single industry approach would help clear up some confusion, provide a platform for more comprehensive research, and ensure different manufacturers are providing products with a sufficient level of safety.

Various vehicle manufacturers have created systems to ensure their vehicles can operate safely on the roadway, including by determining their current position and the level of confidence they have therein. However, as additional safety and mobility applications are developed, there will continue to be a growing need to refine the ability to determine a vehicle’s location as well as how confident that location estimate is and if it is sufficiently confident for the application at hand. These wider questions have not yet been explored in sufficient detail in available research. Requirements on higher levels of precision may lead to privacy, institutional buy-in, and/or cost concerns, though the safety benefits, if demonstrated, are likely to prevail.

Table 3-8: Position Correction Technology Readiness Level



While vehicle localization and position correction are topics that have been considered throughout various research sources to date, there is still a need to focus specifically on the requirements of these systems in order to inform the development of sufficiently accurate position correction systems. Thus, this is a topic that has been explored in laboratory environments but not truly for real-world applications.

3.9 LANE-LEVEL DYNAMIC MAPPING While the second research area (MAP File Creation) concerned the ability to create MAP files, this research area is more focused on how these files could be changed dynamically in response to changes in roadway conditions. For example, during snow conditions, vehicles and drivers today already adapt their behavior and may essentially change a snow-covered road into a narrower road, of 2 lanes rather than 3 that are more consistently cleared

36 https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20%233%20-%20NOCoE%20-%20Vehicle%20Position%20Correction%20Need%20and%20Solutions.pdf

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given current traffic volumes, and the location of the 2 lanes may shift from where the obscured lane lines usually mark. Another application is in work zones. It would be useful to be able to communicate updated, dynamic MAP information to vehicles in real-time, so they retain their confidence in the information they receive from outside sources even under degraded or unusual conditions. Assuming this data is available, additional research is also needed to distinguish between roadway states and to determine how to reliably relay that information to travelers using standard data sets. In addition, cooperative lane management could be pursued to further help refine a system’s knowledge on current roadway conditions as additional CVs are introduced to the roadway network and can provide and respond to information (such as suggestions for alternate routes and speed coordination).

Lane-level dynamic mapping supports the correction and update of MAP files by utilizing collected GPS traces and incorporating dynamic MAP attributes such as variable/reduced speed limits. Lane-level dynamic mapping is different from navigation map matching – the former is infrastructure-focused to provide accurate intersection geometry and dynamic MAP attributes; the latter is vehicle-focused to locate a vehicle on the MAP in general.

3.9.1 DYNAMIC MAP UPDATE PROTOCOL FOR HIGHLY AUTOMATED DRIVING VEHICLES37

Technical University of Darmstadt (Germany), 2017.

RESEARCH OVERVIEW

This research starts with the notion that a high definition street map (with centimeter-level accuracy) is required to enable the use of AVs on roadways, providing information that could not be obtained from vehicle-based sensors and equipment alone. These maps need to be constantly updated. This paper describes a protocol based mainly on the preselection of contextual relevant map data to provide a car with a continuous stream of updates in an efficient way.


This research demonstrates an example of the challenges toward updating a MAP or similar file in real-time and acknowledges that there will likely need to be sophisticated protocols in place in order to make this an efficient and effective process that is reliable in real-time.

KEY OUTCOMES

The capabilities of the protocol were evaluated on a map database of Berlin, which verified that it achieves a significant decrease in transmission data and processing time compared to existing map update approaches. However, this was a simulation and is not a deployed product.

3.9.2 DYNAMIC MAP UPDATE USING CONNECTED VEHICLE DATA38

Mcity, 2016.

RESEARCH OVERVIEW

This research developed estimation algorithms to dynamically detect temporary lane closures and modifications in order to be able to dynamically update geometric files that contain such information about the infrastructure. It also developed a statistical approach to monitor lane occupancies and traffic speeds, and detect abnormal traffic patterns, and then estimate traffic lane closures and modification using this information.

37 https://pdfs.semanticscholar.org/5e2c/48339a08bc5b30cf1ab1b17281cff9f251a5.pdf 38 https://mcity.umich.edu/research/dynamic-map-update-using-connected-vehicle-data/

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This research demonstrates various approaches to detect dynamic changes in infrastructure characteristics and to implement a method of conveying this information to connected vehicles.

KEY OUTCOMES

The purpose of this research was to develop a system that would use CV data to automatically and dynamically update geometric intersection descriptions and maps at a lane level. This data would be collected specifically by roadside equipment and could support various C/AV applications. In addition, a central software for traffic management centers was developed to help visualize dynamic maps and incident information.


Lane-level dynamic mapping is assessed to be a TRL 4 because it has been demonstrated in a laboratory environment but there is not yet a high enough market penetration rate of connected vehicles to be able to have demonstrated the ability to implement an operational system in the real world. As discussed in the section on MAP file creation, creating MAP files is still predominately a manual process, so if that research area were to be moved forward it could assist in the development of a dynamic system of MAP file creation that relies upon vehicle-based sensors and other data sources to update MAP files in real time and provide up-to-date information to roadway users, and eventually C/AVs. This system must demonstrate its abilities in order to build public trust and provide travelers and agencies with the confidence to react to the information provided and base their strategies on it, before widespread adoption will be possible.

Table 3-9: Lane-Level Dynamic Mapping Technology Readiness Level



Vehicle-based detection of abnormal roadway conditions has been explored, and there has also been work done on the challenges of work zone mapping. However, research to date has been primarily exploratory and not fully deployable, and furthermore this research area goes further than existing efforts to distinguish between different roadway states and provide accurate roadway geometry and MAP attributes through an iterative approach.

3.10 LANE AVAILABILITY Today, when congestion is caused by an incident occurring upstream on the roadway, drivers have a limited amount of information on where the conflict point may be and on what they could do to continue to travel safely and efficiently. Information on advancing vehicle conflict points would be useful for connected vehicles to allow them to respond with all the information available on conditions they may not be able to see visually. This research area includes both the in-vehicle and infrastructure-based systems and messages needed to convey this information and the management strategies needed to determine when a lane should be open or closed and in which direction.

Information on lane availability could also be applied to evacuation routes, as the ability to convey available lanes would allow more drivers to safely operate on the shoulder or in other lanes of traffic that are not generally available to them. However, this research will also need to consider human factors, especially when considering the needs of the mixed fleet that includes non-equipped vehicles, and the inherent risks of allowing vehicles to operate in travel lanes that are usually reserved for travel in the other direction or for stopped vehicles.

Lane availability provides information on whether or not a signalized lane is accessible due to congestion, traffic incidents, or work zones. For example, under congestion, a left-turn lane or straight-through lane may be occupied by traffic queues and therefore not accessible during the current signal cycle; knowing this information would allow road users to select a different route. This research area is focused on lane accessibility and

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reliability; this distinguishes it from the previous research area entitled lane-level dynamic mapping, which is focused on lane geometry and physical attributes.

3.10.1 HUMAN FACTORS FOR CONNECTED VEHICLES TRANSIT BUS RESEARCH39

USDOT NHTSA, 2019.

RESEARCH OVERVIEW

This report focused on the tasks and demands associated with driving a transit bus, to support the development of design guidelines for future transit safety technologies. One application in the research is intermittent bus lanes, where the system provides a message to the driver when a traffic management center has cleared bus operators to move into the shoulder. However, this is not always a safe option in poor weather conditions and given the number of distractions operators are already faced with. The intermittent lane does not need to be the shoulder and could be an HOV lane or other travel lane instead. This topic has not been widely explored in research to date, though it has been discussed in some situations.


Like this resource, much of the research on dynamic lane availability for CVs has focused on transit applications, wherein there is interest in opening a bus-only lane at certain times when it would most benefit transit while mitigating impacts on other traffic.

KEY OUTCOMES

Because this resource is focused on the human factor’s component, it reminds that any type of intermittent lane must signal availability to the vehicle operator in a way that does not offer additional distractions and reduce safety rather than improving it. Additional research is needed on this interface and the potential impacts.

3.10.2 EFFECTS OF CONNECTED AND AUTONOMOUS VEHICLES ON CONTRAFLOW OPERATIONS FOR EMERGENCY EVACUATION: A MICROSIMULATION STUDY40

University of Central Florida, 2018.

RESEARCH OVERVIEW

Contraflow operations is an evacuation technique that has been tried by some state agencies in the past and could be better enabled by C/AV technologies that allow for better communication of lane availability and current rules, as well as better V2V coordination. This study presents a comprehensive assessment of the contraflow evacuation operation in a C/AV environment. The microsimulation software VISSIM was used to model the roadway network and evacuation traffic data was collected from previous studies. System performance was evaluated for different market penetration rates of C/AVs.


During an evacuation period, the time sensitivity of traffic operations can place significant demand on evacuation routes. Similar to the need voiced by CTCS stakeholders, this report started with the motivation that authorities

39 https://rosap.ntl.bts.gov/view/dot/40286 40 https://trid.trb.org/view/1497439

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desire to deploy the best possible emergency evacuation plans to evacuate the most traffic in a safe and efficient manner. This includes trying new strategies, including those that may be better enabled by C/AVs.

KEY OUTCOMES

This study found significant improvements in system performance for contraflow operations with the presence of C/AVs in evacuation traffic. The results showed that when C/AVs were at least 30% of the total traffic, there could be a significant reduction in total delays and travel times for the evacuation route, enabled by a significant increase in evacuation speeds. This led to the conclusion that higher percentages of C/AVs contribute to efficient operational performance and fast evacuation during a contraflow operation.


Lane availability is assessed to be a TRL 4 because, while methods such as dynamic message signs have been fully proven in operational environments, the connected vehicle component has not yet reached beyond the early development stages. In addition, some technical challenges remain, such as the need to ensure the system is safe for non-equipped vehicles, and additional research is needed to help resolve these remaining technical challenges. In addition, policy constraints will likely continue to have an impact, though it may be easier to generate changes to policy that specifically apply to exemptions such as emergency evacuations.

The success of this research area, as with many of the research areas, is highly dependent on a high fleet penetration of connected vehicles. Until that point, it may be hard to say that the technology has been “proven in an operational environment,” though some aspects (such as infrastructure-based communication or communication through smartphone applications) could demonstrate the ability to eventually deploy an operational CV-based solution.

Table 3-10: Lane Availability Technology Readiness Level



Communication of lane availability has been demonstrated through many different types of technologies, but the ability to do so using CVs has only been shown in simulation and not in any operational environments.

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4 CONCLUSIONS AND NEXT STEPS There is a considerable range of TRLs across the top 10 research areas of interest. These TRLs are summarized in Table 4-1 and have been sorted by TRL level from highest to lowest. All research areas, whether they are categorized as “enabling technologies” or “applications,” would benefit from additional research in order to reach a higher level of technical maturity as well as deployment readiness. While a research area with a TRL of 8 is usually beyond the need for further research, this may not be the case if other factors precluding deployment readiness are still in place.

Table 4-1: Technology Readiness Level Summary

Research Area TRL

MAP File Creation 8

Pedestrian Safety 6

Data Sharing 6

Queue Length Detection 5

Imminent Safety at Rail Crossings 5

Position Correction 5

Lane-Level Dynamic Mapping 4

Lane Availability 4

Real-Time Signal Optimization for Groups 3

Virtual Detection 3

Only one of the priority-ranked research areas was assessed as a level 8 or higher, MAP File Creation. This is a high-priority research area, and further work may include more guidance (on how to implement a tool that is more user-friendly, on how to encourage stakeholders to consistently use a tool, on how to make a tool more readily available and applicable in more environments and conditions, etc.) versus more research. An enabling technology to meet the needs of this research area is technically feasible, but there are other factors that need to be assessed and resolved before the needs of this research area can truly be met.

As mentioned previously, the stakeholder group is most interested in pursuing research areas that could be deployed quickly and add to the state of the practice. Ideally, this would mean that some research, testing, and piloting has already been done (in either the public or private sector or both), and that there would be value the public sector in particular could add to continue to move the research area forward. These priorities could be reflected in research areas at varying TRLs, due to the unique work done to date, gaps in existing research, and expectations for future developments. For example, a research need for a research area at a relatively high TRL may be to fund and evaluate an actual real-world deployment of the technology, or to deploy in more complex and/or different conditions than has been done previously (such as surface/arterial roads, congested conditions, or multiple seasons). A research need for a research area at a relatively low TRL may be instead to fund additional research, simulation, or a small-scale pilot in controlled conditions. Alternatively, for research areas at various TRLs, a research need may be to integrate C/AV technology more fully into an existing deployment that relies on another type of technology to validate that C/AV technology can provide a similar or better solution.

Building on this report and prior project tasks, the project team will next develop a CTCS research plan, which will identify gaps in existing research in the prioritized research areas. Under the guidance of the CV PFS, the

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CTCS research plan will define a path from concept to deployment for each research area, identifying early opportunities to incrementally deploy newly developed technologies. This will include a ranking based on consideration of technology readiness, feasibility, deployment interest, and development effort as the key decision factors.

The project team will then develop Concept of Operations documents for high-priority research areas thought to best demonstrate success, and that are feasible within the short-term time frame. This will bring forward some of the prioritized high-priority research areas identified in this report, as well as the associated research concepts identified in the CTCS research plan.

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5 BIBLIOGRAPHY 1. Federal Highway Administration Research and Technology, “What is a TRL?,” September 2017,

https://www.fhwa.dot.gov/publications/research/ear/17047/001.cfm.

2. Gao, K., F. Han, P. Dong, N. Xing, & R. Du, Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections, Sensors, 2019, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538986/.

3. Liu, H, X. Wu, W. Ma, and H. Hu, Real-time Queue Length Estimation for Congested Signalized Intersections, August 2009, https://www.researchgate.net/publication/222662104_Real-time_queue_length_estimation_for_congested_signalized_intersections.

4. USDOT, Connected Vehicle Pilot Deployment Program Independent Evaluation: Comprehensive Evaluation Plan — Tampa, April 8, 2019, https://rosap.ntl.bts.gov/view/dot/40144.

5. USDOT, Connected Vehicle Pilot Deployment Program, Driving Towards Deployment: Lessons Learned from the Design/Build/Test Phase, FHWA-JPO-18-712, December 13, 2018, https://rosap.ntl.bts.gov/view/dot/37681/dot_37681_DS1.pdf?.

6. Tudela A., J.E. Argote, and A. Skabardonis, “Queue Spillback Detection and Control Strategies based on Connected Vehicle Technology in a Congested Network,” paper 14-5565, 93rd TRB Annual Meeting, Washington DC, January 2014, amonline.trb.org/1.2481044.

7. USDOT, Connected Vehicles: Tool Library Using SAE J2735 3/2016, copyright 2016, https://webapp.connectedvcs.com/.

8. SPaT Challenge Webinar Series, Webinar #4: J2735 MAP Creator Tool Demonstration, April 24, 2018, https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20-%20J2735%20MAP%20Tool%20Demo%20-%20NOCoE%20-%20Overall%20Slide%20Deck.pdf.

9. Southwest Research Institute (prepared for Connected Vehicle Pooled Fund Study), Basic Infrastructure Message Development and Standards Support For Connected Vehicles Applications, April 16, 2018, http://www.cts.virginia.edu/wp-content/uploads/2018/12/Whitepaper2-MAP-20180425_Final.pdf.

10. SWARCO, Standardisation of SPaT and MAP, 2014, https://www.collaborative-team.eu/downloads/get/SWARCO%20standardisation%20activities%20in%20cooperative%20systems.pdf.

11. Carnegie Mellon University, Robotics Institute. Accessible Transportation Technologies Research Initiative (ATTRI) State of the Practice Scan Final Report. April 10, 2017, https://www.ri.cmu.edu/wp-content/uploads/2017/04/1_ATTRI_SOP_2017-04.pdf.

12. USDOT ITS JPO, ATTRI, https://www.its.dot.gov/research_areas/attri/index.htm.

13. Pedestrian and Bicycle Information Center, Discussion Guide for Automated and Connected Vehicles, Pedestrians, and Bicyclists, August 2017, http://www.pedbikeinfo.org/cms/downloads/PBIC_AV_Discussion_Guide.pdf.

14. NYC Connected Vehicle Project, CV Safety Apps, copyright 2019, https://cvp.nyc/cv-safety-apps.

15. Savari, Smartphone, 2016-2019, http://savari.net/solutions/smart-phone/.

16. University of Arizona, University of California PATH Program, Savari Networks, Inc., and Econolite, Multi-Modal Intelligent Traffic Signal System– Phase II: System Development, Deployment and Field Test: Final Report, September 2016, http://www.cts.virginia.edu/wp-content/uploads/2014/04/53-MMITSS-Phase-2-Final-Report-FINAL-092520161.pdf.

17. Khosravi, Sara & Beak, Byungho & Head, Larry & Saleem, Faisal. Assistive System to Improve Pedestrians’ Safety and Mobility in a Connected Vehicle Technology Environment. Transportation Research Record: Journal of the Transportation Research Board. July 11, 2018, https://doi.org/10.1177/0361198118783598.

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https://www.researchgate.net/publication/222662104_Real-time_queue_length_estimation_for_congested_signalized_intersections

https://www.researchgate.net/publication/222662104_Real-time_queue_length_estimation_for_congested_signalized_intersections

https://rosap.ntl.bts.gov/view/dot/40144

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https://wsponline-my.sharepoint.com/personal/katharina_mclaughlin_wsp_com/Documents/Work%20Files/Projects/CAV/CTCS/amonline.trb.org/1.2481044

https://webapp.connectedvcs.com/

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https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20-%20J2735%20MAP%20Tool%20Demo%20-%20NOCoE%20-%20Overall%20Slide%20Deck.pdf

http://www.cts.virginia.edu/wp-content/uploads/2018/12/Whitepaper2-MAP-20180425_Final.pdf

https://www.collaborative-team.eu/downloads/get/SWARCO%20standardisation%20activities%20in%20cooperative%20systems.pdf

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https://www.ri.cmu.edu/wp-content/uploads/2017/04/1_ATTRI_SOP_2017-04.pdf

https://www.ri.cmu.edu/wp-content/uploads/2017/04/1_ATTRI_SOP_2017-04.pdf

https://www.its.dot.gov/research_areas/attri/index.htm

http://www.pedbikeinfo.org/cms/downloads/PBIC_AV_Discussion_Guide.pdf

https://cvp.nyc/cv-safety-apps

http://savari.net/solutions/smart-phone/

http://www.cts.virginia.edu/wp-content/uploads/2014/04/53-MMITSS-Phase-2-Final-Report-FINAL-092520161.pdf

http://www.cts.virginia.edu/wp-content/uploads/2014/04/53-MMITSS-Phase-2-Final-Report-FINAL-092520161.pdf

https://doi.org/10.1177/0361198118783598

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18. Zohdy, I. and H. Rakha, Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept, 2014, https://www.tandfonline.com/doi/abs/10.1080/15472450.2014.889918.

19. Weber, A. and A. Winckler, California PATH Program, Institute of Transportation Studies, Advanced Traffic Signal Control Algorithms, Appendix A: Exploratory Advanced Research Project: BMW Final Report, 2013, https://trid.trb.org/view/1323846.

20. USDOT ITS JPO, Applications for the Environment: Real-Time Information Synthesis (AERIS) Program, 2015, https://www.its.dot.gov/research_archives/aeris/glidepath.htm.

21. Day, C.M., H. Li, L.M. Richardson, J. Howard, T. Platte, J.R. Sturdevant, and D.M. Bullock, Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data, Transportation Research Record 2620, 2017, https://pdfs.semanticscholar.org/afbd/b92407ff9b64fc8a0e6483a689d510fba354.pdf.

22. Li, H., C.M. Day, and D.M. Bullock, Virtual Detection at Intersections using Connected Vehicle Trajectory Data, 2016, https://ieeexplore.ieee.org/document/7795969.

23. Federal Railroad Administration, Office of Safety Analysis, updated June 13, 2019, https://safetydata.fra.dot.gov/OfficeofSafety/Default.aspx.

24. Federal Railroad Administration, Highway-Rail Grade Crossing Resource Guide, February 20, 2015, https://www.fra.dot.gov/Elib/Document/14371.

25. TRAINFO: Safe and Seamless Mobility at Railroad Crossings, http://trainfo.ca/.

26. Transportation Talk: Canadian Institute of Transportation Engineers Quarterly Newsletter, Winter 2017-2018, https://issuu.com/cite7/docs/tt39.4-winter201718/33.

27. USDOT ITS JPO, Connected Vehicle Safety for Rail Research Plan, https://www.its.dot.gov/research_archives/safety/safety_rail_plan.htm.

28. Federal Railroad Administration, 2017 Grade Crossing Research Needs Workshop, August 15-17, 2017, https://rosap.ntl.bts.gov/view/dot/39204.

29. RITIS, Introduction, copyright 2019, https://www.ritis.org/intro.

30. CATT Lab, RITIS, copyright 2019, https://www.cattlab.umd.edu/?portfolio=ritis.

31. California Department of Transportation (prepared for the Federal Highway Administration, ICM Deployment Planning – The I-210 Connected Corridors Pilot), https://connected-corridors.berkeley.edu/sites/default/files/ICM%20Deployment%20Planning%20Grant%20for%20I-210%20Pilot.pdf.

32. TRANSCOM (Transportation Operations Coordinating Committee), Regional Transportation Management, https://www.xcm.org/XCMWebSite/Index.aspx.

33. TRANSCOM, Systems Concept of Operations, October 15, 2015, http://www.infosenseglobal.com/wp-content/uploads/2018/04/transcom-systems.pdf.

34. USDOT, Connected Vehicle Pilot Positioning and Timing Report, Summary of Positioning and Timing Approaches in CV Pilot Sites, January 25, 2018, https://rosap.ntl.bts.gov/view/dot/35427/dot_35427_DS1.pdf.

35. Yin, E., P. Li, J. Fang, and T. Qiu, Evaluation of Vehicle Positioning Accuracy by Using GPS-Enabled Smartphones, Transportation Research Board, 2014, http://pubsindex.trb.org/view/2014/C/1287871.

36. Larson, Greg, Utilizing RTCM Corrections via DSRC to Improve Vehicle Localization, Caltrans Division of Research, Innovation and System Information (DRISI), April 17, 2018, https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20%233%20-%20NOCoE%20-%20Vehicle%20Position%20Correction%20Need%20and%20Solutions.pdf.

37. Jomrich, F, A. Sharma, T. Ruckelt, D. Burgstahler, and D. Bohnstedt, Dynamic Map Update Protocol for Highly Automated Driving Vehicles, 2017, https://pdfs.semanticscholar.org/5e2c/48339a08bc5b30cf1ab1b17281cff9f251a5.pdf.

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https://trid.trb.org/view/1323846

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https://ieeexplore.ieee.org/document/7795969

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https://www.xcm.org/XCMWebSite/Index.aspx

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https://transops.s3.amazonaws.com/uploaded_files/SPaT%20Webinar%20%233%20-%20NOCoE%20-%20Vehicle%20Position%20Correction%20Need%20and%20Solutions.pdf

https://pdfs.semanticscholar.org/5e2c/48339a08bc5b30cf1ab1b17281cff9f251a5.pdf

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38. Liu, H., Dynamic Map Update Using Connected Vehicle Data, 2016, https://mcity.umich.edu/research/dynamic-map-update-using-connected-vehicle-data/.

39. Graving, J., P. Bacon-Abdelmoteleb, and J. Campbell, Human Factors for Connected Vehicles Transit Bus Research, May 1, 2019, https://rosap.ntl.bts.gov/view/dot/40286.

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6 APPENDIX A: STAKEHOLDERS LIST The following is a list of stakeholders contacted and included as part of the outreach for this task. This list, while comprehensive, may not include all stakeholders.

Name Organization Name Organization

Gummada Murthy AASHTO Barry Pekilis National Research Council (Canada)

Walter Espinoza Alberta, Canada Clyde Neel Naztec

Angel Preston Alliance of Automobile Manufacturers Nadereh Moini

New Jersey Sports and Exposition Authority

Cathie Curtis AMVA Kody McCarthy NHDOT

Jeff Hiott APTA Michael Servetas NHDOT

Marty Lauber Arizona DOT Susan Klasen NHDOT

Dean Wise BNSF Railway Roy Gowdy Nissan

Ann Hildebrandt Bosch Jeevanjot Singh NJDOT

Brian Simi Caltrans Jonathan Martinez NJDOT

Greg Larson Caltrans Kelly McVeigh NJDOT

Gurprit Hansra Caltrans Susan Catlett NJDOT

Melissa Clark Caltrans Wasif Mirza NJDOT

Benjamin Acimovic CDOT Barbara Staples Noblis

Kyle Conner Cisco James Chang Noblis

Jim Dale City of Austin, Texas Peiwei Wang Noblis

Emmanuel Posades City of Gainesville, Florida Eric Hemphill North Texas Tollway Authority

Yang Tao City of Madison, Wisconsin Tom Bamonte North Texas Tollway Authority

Fred Dock City of Pasadena, California John Bassett NYDOT

Rich Montanex City of Philadelphia, Pennsylvania Bryan Comer Ohio DOT

Ashley Nylen Colorado DOT Cynthia Jones Ohio DOT

Nitin Deshpande Colorado DOT Jason Yeray Ohio DOT

Wes Maurer Colorado DOT John Macadam Ohio DOT

Kevin Danh Connecticut DOT Nick Hegemier Ohio DOT

Peter Calcaterra Connecticut DOT Zona Kahkonen-Keppler Ohio DOT

Tim Haile Contra Costa Transportation Authority Lawrence K. Wenko

Operation Lifesaver, Union Pacific Railroad

Deidre Gleason DelDOT Steve Boyd Peloton

Gene Donaldson DelDOT Jerome Frederick PennDOT

Jared Kauffman DelDOT Mark Kopko PennDOT

Jennifer Duval DelDOT Bill Kahn Peterbilt

Michael Duross DelDOT Onno Tool Rijkswaterstaat (Netherlands)

Roger Berg Denso Tom Alkim Rijkswaterstaat (Netherlands)

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Name Organization Name Organization

Eric Raamot Econolite Ahmad Jawad Road Commission for Oakland County

Fred Heery FDOT Danielle Deneau Road Commission for Oakland County

Raj Ponnuluri FDOT Gary Piotrowicz Road Commission for Oakland County

Debora Curtis FHWA Bill Gouse SAE

Gene McHale FHWA Peter Thompson SANDAG

Govind Vadakpat FHWA Dave Miller Siemens

Mike Shulman Ford Peter Polit Sirius/XM

Alan Davis GDOT Phillip Freeze TDOT

Andrew Heath GDOT Said El Said TDOT

John Hibbard GDOT Veda Nguyen TDOT

Scott Giesler General Motors Hideki Hada Toyota

Steve Gehring Global Automakers Association Christine Peyrot Transdev North America

Bill Malkes Grid Smart Ken Moshi Transport Canada

Terri Johnson HERE Matthew Krech Transport Canada

Sue Bai Honda Pierre Rasoldier Transport Canada

Ned Parrish Idaho DOT Lev Pinelis Transurban

Jeff Lindley ITE Mark Normal TRB

Shailen Bhatt ITS America Ray Derr TRB

Robert Sheehan ITS JPO Kiel Ova TTS

Erin Coombs Jacobs Alex Power TxDOT

Andreas Mai Keolis North America Henry Wickes TxDOT

Jane White LA County Jianming Ma TxDOT

Paul Maeda Lear Blaine Leonard UDOT Los Angeles Region's Coalition for Transportation Technology Chuck Felice UDOT

Herb Thomson MaineDOT Elina Zlotchenko UDSOT

April Wire Maricopa County Mo Poorsartep Valeo

Faisal Saleem Maricopa County Ken Earnest VDOT

Aaron Jones Maryland DOT Michael Clements VDOT

Hua Xiang Maryland DOT Michael Fontaine VDOT

Richard Woo Maryland DOT Noah Goodall VDOT

Mike Schagrin McCain Vanloan Nguyen VDOT

Collin Castle Michigan DOT Skip Yeakel Volvo Truck

Elise Feldpausch Michigan DOT Anne Reshadi WisDOT

Robbie Vance Mississippi DOT Morgan Balogh WSDOT

Ray Starr MnDOT Ted Bailey WSDOT

Steve Latoski Mohave County Vince Garcia WYDOT

Robert Rich MTC

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7 APPENDIX B: STAKEHOLDER NEEDS WORKSHOP MEETING NOTES

An in-person workshop was conducted with CTCS stakeholders on April 30, 2019 from 10 AM to 4 PM in Ann Arbor, Michigan. The following is a summary of the topics discussed at the workshop. Summary of Project

• CTCS is the next generation of MMITSS o Goes beyond just the signal controller and takes a broader view o Project has been underway for almost a year, currently in task 4 o Report on task 3 will be published publicly soon

• The purpose of today’s meeting is to infuse a dose of reality into the research work by discussing the findings with stakeholders (pooled fund study members)

o Next steps aren’t important to FHWA unless they are important to this group – it’s important to see where the stakeholder community wants to go in the future

• What will be discussed today is independent of the DSRC or C-V2X decision o Focus instead on standards, applications, strategies, etc.

• Many themes/gaps will be recurring across the six use cases

Application Scenario 1: Railroad Crossings

• About half of the room deals with at-grade crossings as infrastructure owner/operators • The number of incidents is consistent across the years, and seems significant • Most of room is familiar with TRLs

o Railroad crossings are proposed to be level 4 – validated in a laboratory environment • Magnitude of the problem/effectiveness of the solution

o Question on whether an incident means a collision or a fatality – think it’s collision ▪ Worth noting that almost all collisions with rail vehicles are serious ▪ Many states have a zero fatalities goal – every fatality is one too many ▪ Some collisions are at crossings, some are not at crossings, some are suicides

o There are also issues with delays and with communication between agencies (for example if an intersection needs to be closed for upgrades, this is not always well communicated)

• In urban areas, need to especially consider light rail crossings o Many light rail collisions are with pedestrians

• There are sometimes traffic signals that are tied into the crossings, but usually signalized crossings are just flashing lights and gates

o Sometimes the rail company cannot provide the needed warning time to adjust signal timing o AVL could be used for in-vehicle notifications – at a certain threshold, the car is much better

able to stop than the train ▪ V2V or T2V – train (to infrastructure) to vehicle ▪ Issue that the train probably won’t deploy the same type of technology as the vehicle,

which is why communicating through infrastructure may be better • Should include people from the rail industry in these meetings – did invite them and they showed

interest but couldn’t come today • Do DOTs own the signals at the crossings or is it the railroad companies?

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o It’s usually the railroad companies o DOT may invest in a bridge or overpass, and be responsible for that

• How do you resolve noncompliance? o Knowing the amount of time you are waiting would help – sometimes when you’re waiting you

don’t know how long it will be until you are able to cross o What if you knew before you got off the freeway that a train will be blocking the crossing, and

could choose then to take a different exit o Issues with lack of information and lack of ability to communicate information with the

vehicle/driver • From experience having worked on advance warning systems, a lot of railroads say their data is private

and that they don’t want to share it for security reasons o More of a policy-level issue, technology is there o Ask the railroads to offer up some of the data standards

• Has FRA ever been engaged in this conversation? o Some engagement through the ITS JPO o Some of the information is dated

• Suggest putting together a list of potential constraints on each use case – for example, these issues may be enough of a roadblock to hamper this as low-hanging fruit

o TCIP is a possible interface for standardization – forging a link between the two agencies at a standards level would be useful

o APTA could also be an option • A light rail (public) transit organization may be easier to deal with than (private) railroad companies.

Same with heavy commuter rail. • In the traditional rail scenario, if we cannot get data from them, we could use radar or another technique

to assess speed and location • There is a company in Canada (Trainfo) that has been deploying a system with DSRC, so TRL may be

closer to 6 or 7 • Major needs:

o Advance information to travelers so they can make decisions on where to get off a main road and on how much longer they may need to wait to reduce frustration

o Longer term: access to information for traffic controllers to improve flow in approaches and further upstream

o If someone has already ignored the information, need for response in a safety-critical crash-imminent scenario

o Requests from emergency responders on whether a train coming and what the duration of the blockage will be is already done, and has been easier with light rail than with freight rail

Application Scenario 2: ICM

• San Diego, Dallas, and other cities have already done ICM o Benefit-cost ratio of 10:1, 20:1

• Interagency coordination is the biggest consideration for this one – without that nothing else is possible • Help travelers make informed decisions (not just management) • Data on travel time would be better than data on congestion

o CVs can make it easier to get travel time on arterials • How do ICM strategies change with more data from CVs?

o Institutional issues o Data management issues: refer to data warehousing needs for an ICM strategy in Northern

Virginia • Suggest bringing in IT stakeholders – GIS and ArcGIS have applications to enable visualization and

communication in real time

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• If there is an incident on a freeway and traffic is moved to an arterial, traffic management system can trigger a different signal timing plan

o Example with CDOT and Panasonic in Colorado – but the problem there is that now CDOT is a Panasonic state managed by a proprietary, closed data monopoly

• Suggest a more modernized approach to provide clean and accessible data o Set policies around data governance o Balance the breadth of the data with the ability to manage it o Sometimes feel stuck in the 90’s in terms of architecture and mindset – need to make a large

leap in policy and standards o Need a process to migrate and transition current systems (want to be able to upgrade without

abandoning) ▪ For example, to add the necessary interfaces for CV ▪ Conventions have to be prepared for what’s coming – language disconnect

• There have been issues with real-time traffic information applications like Google and Waze pushing traffic onto neighborhood roads

o Need to define your vulnerable users – don’t want to push people to drive near schools, or in areas with many pedestrians

o Need to consider human factor aspects o Think of where the traffic will be pushed to and how this impacts land use, safety, etc.

• Lessons learned from an existing project o Other agencies and DOTs will ask about the process in general – such as what the steps you need

to go through are (contracting process, simulation model for the scenarios) o ICM is not a product so it is important to consider the many scenarios and local

needs/constraints o Is there an opportunity for CVs to help these strategies?

• It will change how data is collected and how it is disseminated o For example, for ramp metering, the data can go back and forth and enable a vehicle to slow

down and then go straight through the signal rather than having to stop at the signal (speed harmonization)

• Google works better than DMS signs for getting people to take an alternate route o Too often, DMS signs are not specific enough o Apps can show exactly where the disruption is – the data is granular and even if it may be wrong

at times people trust that more o There is research that suggests that people are more responsive if information is provided in

their dashboard than on a sign outside o Similar to how when people see a 35-mph static speed limit sign, they probably won’t change

their speed, but if it’s a dynamic sign that says they are going 37 in a 35 zone and flashes, they will probably slow down

▪ Opportunity for CVs and what they could provide – somewhat customized information for each driver/vehicle

• TRL is specifically for adding CVs into an ICM system o Some of those in the room who have deployed ICM are considering adding in CVs

▪ For example, in Tennessee, DSRC is one of the tools in the box but not the main factor ▪ Problem that information is not always specific enough – doesn’t give instructions or

orders (just says traffic ahead vs. get into the left lane) • Two aspects of ICM that seem important:

o Advance notice and guidance to influence demand o Control to influence driver efficiency and compliance

• Apps (Google, Waze) are more willing to be wrong than agencies are – this is institutional, they don’t need to have press conferences if something goes wrong

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o Some agencies believe they cannot fail and cannot provide information that they aren’t totally sure is always accurate – but the problem is the sensors aren’t accurate enough yet to enable that level of confidence

▪ In some ways this is an antiquated way of doing things • Could also manage traveler behavior and decisions, including pricing components (carpooling, managed

lanes, etc.) • There has been a fundamental shift with dynamic navigation systems – it used to be that traffic

managers were able to tell people where to go. Now it is just private companies using shortest path algorithms.

o Not a bidirectional relationship o Can lead to worst case scenarios when people are guided through a neighborhood o The questions for agencies comes down to: how do we create a better product? Or should we

create a better product? ▪ Many different goals to manage: mobility, safety

o Do you just provide the raw information? Or do you try to guide decisions? ▪ In traffic control management, control is slipping to a bottom-up approach ▪ Need to keep safety in mind ▪ A couple cities in New Jersey passed ordinances that said that you can’t travel on

neighborhood roads if you don’t live there during certain hours of the day. These cities were called into court by the state government because those laws weren’t valid.

▪ Routing through neighborhoods is a safety issue, and could be a geometry issue as well (such as in San Francisco, where trucks got stuck between hills)

• City governments have different goals that Google or Waze does o The City of Austin works with Waze to offer information to people on nighttime road closures –

this is a way to try out these strategies before CVs are widespread ▪ People know the message is relevant to them

o Do not necessarily see this as a competition – think it makes sense to use the existing “mousetrap” to better distribute the information we already have

• There is a different type of accountability – Waze and Google used to direct people down neighborhood streets more than they do now (they’ve adapted to feedback)

• Make sure we’re giving the information to everybody (not just Waze) o Have to share the data to everyone equally, but they each want their product to be the best

product o Want to help enhance their product but not favor one company over another o They are ahead of the public sector in some respects but they don’t have all the information that

the government has • Google and Waze have partnership workshops – they want both socially engineered data and their

algorithms as well as partnerships with agencies for the information they have • Pushing data to Google and Waze could help with low penetration rates of CVs • Elements of ICM strategies: work zone management, transit operations, signal priority, etc.

o CV is one tool that could support ICM, but ICM is broader than that o Load balancing o ICM gets brought up in problem areas with recurring congestion, not areas with non-recurring

congestion – how could CVs support that?

Application Scenario 3: Connectivity and Early Automation

• This is the only section today that will really touch on AVs • Most companies have separated their CV and AV sides • For some applications, you can have 100% AV/CV penetration pretty easily because they are fleet-based

(transit, maintenance, trucks, etc.)

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• In the THEA pilot, they are repurposing vehicle detection equipment to blend that information in to what is provided by and to CVs

• For roadway capacity, there are many statements going out there and the general consensus is that capacity will increase with AVs and CVs. However, clarity needs to be brought out, that only at high penetration rates will these benefits be seen.

• This could be connected back to the TSMO strategies o Add capacity using technology, not just by widening roadways o In many states, they want to leverage any funding that is available, and they would probably be

willing to try new alternatives • May be a TRL level 6 on freeways but this use case is more focused on arterials where level 4 probably

makes more sense o Want to stay focused on the arterials

• Timing of deployment and of widespread market penetration o Using infrastructure-based detection to help with that issue will be key because if you can show

people with connected vehicles the true safety benefits, they will be early adopters as well as strong advocates for the technology

• There is some thinking that if you have a few vehicles that are connected and automated, and you control their speed profile, they will have an influence on other vehicles on the roadway

• It will be interesting to see in the next couple of months, the results of the USDOT ADS demonstration grant program

o What the nature of those programs are, and whether they are arterial based, may improve our understanding of this use case significantly

o There may be some data there about how effective that will be • Most people have smartphones, could leverage that to communicate to vehicles in the short term • There could be issues if some AVs are using different digital maps than others

o Standardization of map creation and how far things should be away from each other o SPaT and MAP standardization tools already exists

▪ There is a standard for what the MAP message should look like, but not on all aspects of it

o What level of accuracy do AVs require? o How will you temporarily define a MAP if a lane at an intersection is closed? o Need more detail in those messages

▪ The level of detail can be an application-based requirement ▪ There is no application today on how to split a MAP into multiple requirements

• For smart work zones, any MAP creation should move towards real-time knowledge o From an industry perspective, are given static information on work zones but want to know

when activities are present • Most maps are Google-based because there are not sub-centimeter maps available

o That would require a $5-10K survey • OEMs need lane-level accuracy to improve corrections, positioning, etc.

o The SPaT verification document has looked at position correction o This system may not need to be in use everywhere

▪ There are intersections where it may make sense, others where it may not ▪ More important on corridors with a sequence of intersections ▪ More important at complex intersections, where corrections are needed

• What is the signal going to do? For example, will it always keep together a platoon by adjusting signal timing?

o Perhaps at some point in the future, but that’s not where we’re going to go with the near-term research

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• Gap termination model is such a safety critical element – research results are needed before many DOTs would be willing to try it

o How do you terminate in the middle of a platoon (if you have to due to a rail crossing, for example)?

o Breaking up platoons may not be optimal but it may sometimes be necessary • We have tools now that we didn’t have a few years ago – CV data (V2I) going back and forth and more

signal data o High resolution data o Performance metrics are captured in real time, automatically o Next step will be to take vehicles that are platooning and make those adjustments in real time

• Platoons and strings are different – string has a TRL 4, platoon maybe does not o A platoon is a command and control condition, a group of vehicles that moves together

▪ For example, for a cooperative adaptive cruise control platoon, communication is limited to the vehicles in the platoon

o A string doesn’t care how long it is or what the car behind it is doing o There is logic in each vehicle on whether or not it wants to break its link with other vehicles –

platoons will not break up, but a string will o Truck platoons are designed as strings because you have to allow for cut throughs

• Need to demonstrate benefits – want to better understand the required infrastructure investment to determine what the funding needs are

Application Scenario 4: Lane Management

• Focus on arterial lanes – bus only lanes, variable signs, etc. o Most of what was presented seems freeway-based, but there are some arterial applications o For example, a local arterial could have reversible lanes (i.e., 6 lane road split with 4 in one

direction and 2 in the direction, where the middle two lanes can shift) ▪ Turn lane makes signaling even more complicated ▪ Could move from time of day response of the system to demand based but concerned

about switching around too much and that being confusing to drivers • Could have exceptions, such as in response to a crash

▪ Consider the emotional impact on drivers – from experience, many didn’t like reversible lanes at first but they get used to it. But that may only be true if they are used to what it should be at certain times of day.

• They had too many traffic signals at first, especially since they were red and green like the traffic lights. So, they removed lane management signals from the outside lanes because those are always the same.

• TRL 4 might be too high • For rural applications, would value information on incidents ahead

o Not really lane management • Example with a dynamic switch between a 1 and 2 lane turn lane • Many examples with lanes where you can’t park during peak times

o They sometimes turn into bus lanes • Where can lane management be really useful is for emergency evacuation

o In Florida, they see major queueing at gas stations, leading to queues on arterials o It is only used once in a while, but it is an application that cannot be ignored o Would lane restriction or lane management be more relevant here?

• Bus-only lanes • The intent of this use case to use CV data to make decisions as well as to give information to vehicles

o Need to identify the benefits for the traveling public • Time and education are necessary to get people to use dynamic lane changes

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• Worry less about possible challenges and be more inquisitive about possible opportunities • Are already seeing a change in curbside management • Static signs have some limited adherence, dynamic signs have increased adherence • Big gap is the dynamic nature of lanes and the way that is managed and communicated

o SPaT standard doesn’t address that • In snow conditions, you essentially already go from 3 lanes to 2 when the snow gets deep enough

o Basically an ad hoc lane management situation o Could you draw new pavement markings based on that? For example, if there are bad sight

lines, you could see that from where people are stopping. But there are other countermeasures you could take before drawing a new line.

• Draw in crowd sourcing o Could use the crowd sourced CV data to bring in vehicle-based data o More research and more standards work is needed here

• Just due to readiness, this use case may not float up to the top • Consider addressing pedestrians in this use case • Just as the number of lanes may go down due to weather at some points, the weather could also cause the

number of lanes to go up o Such as in a hurricane evacuation when drivers are allowed to use the shoulders o During Hurricane Michael, got many complaints that people were driving in the shoulder

because others weren’t aware that the shoulder could be used during that time ▪ Communicated through Waze to let people know that was allowed

• There are examples of shoulder use on arterials • To address lane departures in rural areas – C/AV penetration may be pretty low in rural areas, so

communicating through Waze may be more effective

Application Scenario 5: Multi-Modal Aspects

• Is it time to talk about shared mobility as one of the modes and not just an outlier? o When do you treat a 4-passenger car differently than a bus with 4 passengers in it? o Not just TNCs, but the future of last mile services – automated shuttles, microtransit etc. o Options for park and ride lots

• This looks very open ended o A subsection of this could be an entire use case in itself o Many of these are suites of use cases

• Trend towards bicycles and pedestrians, discuss CV apps that are relevant to them o Bicyclists and pedestrians could carry devices

• Have a federally funded project in Gainesville o Found that it is relatively easy to detect a pedestrian, but it is much harder to detect a bicyclist

especially in mixed traffic o Similarly, getting the information to the pedestrian is easier than getting the information to the

bicyclist • For automated shuttles, there are remaining challenges such as how a transit vehicle would know there

is no right turn on red, or when it is safe to turn right on red o More than mobility, safety will be the driver

• Freight could be the next major application o Signal priority o Truck platooning o Last mile delivery

• Don’t forget scooters and skateboards, especially at university campuses • Transit is not just transit, it may mean many different things • Number of VMT by UHL, DHS, etc. is going through the roof due to more online deliveries

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o They will likely be early adopters of AVs, because it eliminates the cost of the driver which makes the investment more worth it for them

• FMLM for freight – curb management, smart curbs • Bicyclist and pedestrian movements can be random and are hard to predict

o They are also hard to track – but with the way technology is moving so quickly, this may be much more feasible in a few years than it is today

• Suggest looking into piloting connectivity for delivery robots o They tend to cross the road when other pedestrians do

• It is difficult to capture demand, especially for individual users • Current infrastructure is designed for human travelers

o Are there ways we could redesign infrastructure to be better adaptable for AVs? o Right now, we consider it cheaper to design AVs for infrastructure but as new infrastructure is

built maybe that will change • Major barrier to low speed automated shuttles is the ability to interact in intersections

o One pilot is using OBUs and that has helped • Research tends to focus on the best case scenarios, but most pedestrian fatalities happen in the worst

case – dark, jaywalking, under the influence, outside of the crosswalk o Need to consider this so that applications help more than 5% of the time

• Cars should know if there is a walk light on and that they legally have to yield to pedestrians o Not clear in the SPaT standard o We are not yet taking advantage of some of the tools we have o Focus on SPaT has been very narrow to date

• Refer to Ped Safe project in Gainesville

Application Scenario 6: Arterial/Surface Streets with Traffic Control and Ramp Meters

• There is definitely overlap between this use case and ICM, but this one has a narrower scope • Queue length is queue length for a ramp meter

o SPaT has back of queue/length but is not always populated o Queue length is generally calculated from BSMs

• Coordination between ramp meters and nearby arterials is relatively rare • Application today does not have the partner to fill in the gap to get the queue length

o Lack midblock detection in most cases o Video-based systems could probably capture the queue length in most situations, but there isn’t

a method for disseminating that data • Can be a challenge to get travel times for arterials, though there are options, primarily for purchase,

based on crowd sourced data (such as Inrix) • What level of penetration do you need in order to reliably obtain the end of queue length from BSMs? • Queue detection will vary by scenario (midblock, ramp, work zone, etc.)

o Incremental process o Can already tell when there is going to be a backup at a ramp because they know the timing plan

will be necessary o It is all about the edge cases

• Having variable metering rates based on the type of vehicle o Truck, heavy freight, transit, snowplow – allow that to get through more quickly (to reduce

environmental impacts) • Should a ramp meter be delivering SPaT?

o No, unless it’s for queue length, but that would probably be a different application o Virtual detection is of interest – how can you create a virtual detection scenario from CVs? o If a ramp meter isn’t putting out a SPaT message, how will an AV know (other than visual

detection)?

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▪ Then it may be that you should deliver a SPaT message from an AV use case ▪ Currently, ramp meter is either on or off, but hope to see more cooperative adaptive

ramp metering in the future • Interactions with platoons – using ramp metering to avoid separating a platoon • Going to deploy cooperative adaptive ramp metering tomorrow in Arizona • Focus of 5.9 GHz technology is real-time safety but this may be more of a smart city application

o Updates every 1 to 2 minutes, or updates that are a few seconds late rather than real-time, would likely be fine for this scenario

o But in some ways, this is really the other way around – you already have the real-time information so you can use it for this application

o All of this falls on the cellular backhaul component o In addition, just because something isn’t provided in real time doesn’t mean you can’t use the

data standards o From a latency standpoint, this isn’t DSRC necessary, but DSRC could be used

• TRL might even be higher than a 6, except for the CV aspects

Remaining Gaps

• Refresher on how the group decided on these six use cases o In the original solicitation from the pooled fund, there were more use cases, but in the face-to-

face meeting last May the team and the panel narrowed it down to these 6 use cases o Most of the uses cases that were removed were focused on freeways (such as speed

harmonization) • Today’s focus has been on the use cases, but there are some things that are consistent across all use

cases, such as mapping – do we need to discuss those sorts of things more? o Is that within the scope? It is within the pooled fund study scope, but not necessarily the CTCS

scope o The purpose of going into these use cases is to determine where research funding should go o This will definitely be in the research road map, but not necessarily in the ConOps as that will be

more application focused • Gaps in SPaT and MAP applications

o When they use Bing Maps, they are not always updated on all roads, so some errors get programmed in. A lot of energy is spent in the field for fine tuning.

o Need midblock, ramps, etc. in addition to intersections o Are also doing mapping for work zones o GPS correction – haven’t had enough of a discussion on how to get to that centimeter-level

accuracy • How is an intersection map created?

o Assumed it was a survey, but it is actually using Bing Maps and zooming in and manually putting dots in to label the lanes and such

o In Ann Arbor and in Minnesota they use LiDAR surveys o Strategy is to find something like a manhole cover or a curb cut that doesn’t move and use it to

label where other things are o Federal government’s tool doesn’t include pedestrians o Average time to create a MAP message is 12-14 minutes (Audi)

▪ Have their own tool that uses online tools and then field surveys to validate o There is a desire to used more updated maps with the federal tool but that is not currently an

option o Recently worked with a roadway that was under construction so no map was accurate – had to

use other files o Overall, there is no real standardization

• Virtual detection vs. infrastructure based detection

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o Vehicle perception data/detection – using incoming vehicles as probes ▪ Vehicles that are equipped might be able to sense the unequipped vehicles and could

share that data with a system (vehicles in front of, behind, and possibly next to the equipped vehicle)

• CV in a static traffic control environment – lane departures o CVs could be used to advance vehicle conflict avoidance

Prioritization Exercise

• Research roadmap will include everything, but there needs to be some semblance of prioritization within that

o Keep in mind that pooled fund members are mostly in the public sector and are not inclined to fund things the private sector would do on its own

• Rail crossing o Advanced information for travelers o Advanced information for traffic engineers o Imminent safety

• ICM o Interagency coordination o Data sharing

• Early automation o MAPS o Position correction o Real time signal optimization for groups

• Lane management. o MAP – lane level/dynamic mapping o Lane availability

• Multi-modal (consider that the challenges for pedestrian and bike safety are quite different) o Pedestrian safety o Bike safety

• Ramp/arterial (consider that IOO’s have control over infrastructure-based detection but not over vehicle-based detection)

o Queue length detection/BoQ o Virtual detection

• Results o 30 people voted in the first Mentimeter exercise and generally did not really prioritize between

the three actions they provided, so they were all weighted equally in the following graph o 13 of the 90 suggestions were just the application scenario and not an action – these are

included at the bottom of the graph

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• The second exercise was to prioritize the six use cases, which led to the following results:

• Takeaways:

o Railroad was probably low because the CV aspect is less clear ▪ In addition, railroad crossing may not be in the traffic engineer’s control

o Lane management and ICM are low, as expected

0 2 4 6 8 10 12 14

Queue length detection

MAP file creation

Pedestrian safety

Real-time signal optimization for groups

Virtual detection

Imminent safety at rail crossings

Data sharing

Position correction

Lane-level dynamic mapping

Lane availability

Interagency coordination

Bicyclist safety

Advanced information for travelers

Advanced information for traffic engineers

Early Automation

Multi-Modal

ICM

Railroad Crossing

Ramp/Arterial

Lane Management

12

34

56

78

91

01

11

21

31

4-

Prioritized Research Areas

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o Had a tendency to rank based on whether they had voted for actions under that use case in the previous list

o Participants picked things they could work on right away and that leverage previous projects ▪ Things that could likely be implemented and where you would expect to see results

pretty quickly

Connected Traffic Control System (CTCS): Research Planning and …€¦ · Task 3 concluded in a...

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