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SHRP 2 Project L04

Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools

Reliability Technical Coordinating CommitteeBriefing

National Academy of Sciences

Irvine - April 8, 2010

in partnership with

&

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Agenda

Project Overview – Methodology

Data and Candidate Networks

Anticipated products of the research

Work Program Discussion

Methodology Framework: Three Components to Incorporate Reliability in Network Simulation Models

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Exogenous sources Input

Scenario managerDemand- Special events- Day-to-day variation- Visitors- Closure of alternative modes

Supply- Incidents- Work zones- Adverse weather

Endogenous sourcesReliability-integrated Simulation model (meso, micro)

Improvements to existing simulation toolsDemand- Heterogeneity in Route Choice and User Responses to Information and Control Measures- Heterogeneity in vehicle type

Supply- Flow breakdown and incidents- Heterogeneity in driver behavior (car following, lane changing…)- Traffic control- Dynamic pricing

Performance measuresOutput

Vehicle trajectory processor- Travel time distribution- Reliability performance indicators- User-centric reliability measures

Integration in Planning Models

Reliability-sensitive network equilibrium models– Reliability affects traveler’s mode,

departure time and route choice.– Reliability measures are produced

from the simulation models and fed back to the demand models.

– Iterate between demand models and network simulation until convergence to UE (or SUE).

– Output performance measures for policy evaluation and network planning/design.

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Stochastic Network Simulation ModelStochastic Network Simulation Model

Reliability MeasuresReliability Measures

Mode, Departure Time and Route ChoiceMode, Departure Time and Route Choice

Traffic AssignmentTraffic Assignment

Model Exogenous Sources: Scenario Manager

Scenario-based approach– Construct discrete scenarios– Conduct single-point estimation to produce results for each

“what if” scenario

Monte Carlo sampling– Randomize demand and/or supply side parameters and

establish the corresponding probability distribution functions.– Conduct Monte Carlo simulation with regard to these random

parameters

Scenarios involving equilibrium traffic assignment– Perform iterative equilibrium assignment for scenarios involving

medium to long term changes in demand or capacity

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Model Endogenous Sources:Route Choice Behavior

Route choice behavior and travel time reliability interact– Reliability is a result of travel decisions– Reliability affects route choice behavior

Reliability in generalized cost function

Heterogeneity in route choice behavior

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rtcGC VORVOT

Value of time of driver n Value of reliability of driver n

Value of time distribution Value of reliability distribution

Travel time, toll and reliability information

Traffic simulation model

Least generalized cost path for driver n

Model Endogenous Sources:Heterogeneity in Driving Behavior

Microscopic simulation models– Vehicle-related parameters, e.g. length, maximum

acceleration/ deceleration, reaction time, safety distance, desired speed, desired acceleration/deceleration, Maximum give-way time

– Link-related characteristics, e.g. speed limits, visibility distance at junctions, maximum turning speed, slope (grade), reaction time variation

– Heterogeneity in car-following and lane-changing behavior, especially in the presence of heavy vehicles

Mesoscopic simulation models– Heterogeneity in vehicle types– Varying and context-dependent impact on traffic

performance

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Model Endogenous Sources:Flow Breakdown and Incidents

Characterize flow breakdown as a collective phenomenon– Probability of

breakdown– Breakdown duration

Characterize flow breakdown and incident through individual decisions– Describe driver

behavior under extreme and incident conditions

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Random number

generator r

Stochastic network simulation model

Prevailing flow rate (q)

Probability of flow breakdown p(q)

r < p(q)?

Flow breakdown in the next time interval

Flow sustain in the next time interval

t=t+1

Yes

No

Breakdown durationHazard model

Model Endogenous Sources:State-Dependent Traffic Control

State-dependent traffic controls - dynamically adjust the control variables based on the prevailing (or predicted) traffic conditions, for more effective management.

State-dependent controls may introduce another source of unreliability/unpredictability to the system.

Actuated signal control Ramp metering Variable message signs Dynamic pricing

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Vehicle Trajectories: Unifying Framework for Micro and Meso Simulation

Vehicle (particle) trajectories in the output of a simulation model enable– construction of the path and O-D level travel time distributions

of interest– extraction of link level distributions

Vehicle trajectories could be obtained from both micro- and meso-level simulation models

Trajectories also obtained from direct measurement in actual networks, enabling consistent theoretical development in connection with empirical validation.

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Vehicle Trajectory Processor

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Travel time distribution

Performance indicators:•Travel time variance•95th percentile travel time•Buffer index•Planning time index•Frequency that congestion exceeds some threshold

Vehicle trajectories

Travel time by lane, link, path and trip (O-D)

User-centric measures:•Probability of on time arrival•Schedule delay•Volatility

Experienced vehicle travel time and actual departure time

Preferred arrival time

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Modeling Platform Requirements – Model Types & Roles

Type of Model Role in Framework

Planning(demand forecasting models)

• Provide traffic demand input to simulation models

• Demonstrate the use of reliability measures for route / mode choices (and potentially departure time choice) in an integrated demand-supply framework

Operations(meso/micro-simulation models)

• Incorporate parameters affecting travel time variability at operations level (supply side)

• Interface with Scenario Manager to obtain input based on exogenous sources / parameters

• Generate (trajectory-based) travel time output for reliability assessment

• Interface with Trajectory Processor to provide output for development of travel time distributions, reliability performance indicators & user-centric measures

Planning Model Requirements

Ability of planning model to use quantitative measures of travel time variability in demand forecasting processes (i.e., beyond the common practice of using average travel time and cost)

– expected travel time– schedule delay– travel time standard deviation (inferred vs experienced)

Ability to achieve at least some consistency between simulation-generated reliability measures and those used in mode / route / departure time choice models

Preference for activity-based planning models in order to incorporate schedule delay and other micro-level, reliability-related measures

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Operations (simulation) Model Requirements

Ability to address most typical urban/suburban type of traffic conditions – vehicle/particle-based computational approach & fidelity– uninterrupted and interrupted flow with various types of facilities (incl.

managed lanes) and control (signalized, stop/yield, etc.)– multi-vehicle classes (auto, truck, bus), preferably with varying

characteristics – multi-simulation periods

Ability of underlying submodels (route choice, lane choice, etc.) to “endogenize” certain variability sources *– route choice and driver behavior heterogeneity– incident and flow breakdown characteristics– state-dependent traffic control

Ability to generate vehicle/particle-based trajectories

* may require open-source models or access to code

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Software Code Access / Modification Requirements

Ability to access / tweak programming code for endogenizing time variability sources / factors

– some software developers would be keen to assist (depending on level of effort involved)

Open source sub-models (e.g., NGSim-developed lane change and other models)

– already available in some software packages (Dynasmart, Aimsun, Vissim)

Various forms of intervention through programming tools (API)

– available for most commonly used simulation platforms in North America(Paramics, Vissim, Aimsun, Transmodeler, Dynasmart, Dynameq, Vista, etc.)

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Data Requirements

Traffic data for model adaptation / re-validation

Ancillary data for parameterization of time variability sources (endogenous & exogenous)e.g., special events, incidents, weather …

Travel time data for

– reliability analysis / concept confirmation

– model output verification / checking

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Travel Time Data

Trajectory-based by vehicle trip(X, Y coordinates and time stamp)

Capturing both recurring and non-recurring congestion on a range of road facilities (from freeways to arterial roads and possibly managed lanes)

Sufficient sampling and time-series to allow statistically meaningful analysis

Ability to tie travel time data to “ancillary data” for time variability sources (to allow parameterization for simulation testing purposes)

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Potential Data Sources / Inquiries made to date

GPS- and Cell-probe data provide most promising prospects for large scale spatial and temporal coverage

INRIX (national)NAVTEQ (national)MyGistics (Chicago region)Google (national) -no response

ITIS and FCD for validation (Missouri) Calmar truck data (California, New York, etc.)Intellione (Toronto) -prelim. tests undertaken

major navigation services provider -prelim. tests undertaken

Preliminary Data Tests to date

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Cell Probe Raw Data Format Trajectory

ProbeID LinkID Direction of travel

Longitude Start Latitude Start Longitude End Latitude End Link class-ification

Time since 111970 sec

Travel time sec Speed msec

35948502253992 743407983 2 -80.6850899966042 43.1339800097549 -80.6858600225125 43.1325600021493 0 1256842898 7.93010162457449 21.4452066187493 35948502253992 743407983 2 -80.6858600225125 43.1325600021493 -80.6868200003515 43.1312000168074 0 1256842906 7.94944228245065 21.4452066187493 35932201116385 743407985 1 -80.6849100124862 43.1339500247966 -80.684290005487 43.1354399869108 0 1256842867 5.29451989243751 32.7420969216286 35932201116385 743407985 1 -80.684290005487 43.1354399869108 -80.6837300172132 43.1369500002555 0 1256842873 5.31951772465551 32.7420969216286 10304139942743222 743407985 1 -80.6866899788012 43.1311199711812 -80.6856799844734 43.1324999825377 0 1256842889 4.29094891357159 40.6440341080312 10304139942743222 743407985 1 -80.6856799844734 43.1324999825377 -80.6849100124862 43.1339500247966 0 1256842893 4.26237531203243 40.6440341080312 10304139942743222 743407985 1 -80.6849100124862 43.1339500247966 -80.684290005487 43.1354399869108 0 1256842897 4.26516922535072 40.6440341080312 10304139942743222 743407985 1 -80.684290005487 43.1354399869108 -80.6837300172132 43.1369500002555 0 1256842901 4.28530702572597 40.6440341080312 35948502253992 743411775 2 -80.6814100238178 43.1440699972467 -80.6819700013037 43.1425399935343 0 1256842832 4.91146210334099 35.8957867940915 35948502253992 743411775 2 -80.6819700013037 43.1425399935343 -80.6825300178688 43.1410200225322 0 1256842837 4.88143914381964 35.8957867940915 35948502253992 743411775 2 -80.6814100238178 43.1440699972467 -80.6819700013037 43.1425399935343 0 1256842844 8.22098847742744 21.4452066187493 35948502253992 743411775 2 -80.6819700013037 43.1425399935343 -80.6825300178688 43.1410200225322 0 1256842852 8.17073492785502 21.4452066187493 35948502253992 743411777 2 -80.6825899919003 43.1407399960802 -80.6830700233299 43.1394600295052 0 1256842842 4.1156666382753 35.8957867940915 35948502253992 743411777 2 -80.6830700233299 43.1394600295052 -80.68353000075 43.1381900087434 0 1256842846 4.07434404815423 35.8957867940915 35948502253992 743411777 2 -80.68353000075 43.1381900087434 -80.6840000090416 43.1369199950256 0 1256842850 4.0799424280729 35.8957867940915 35948502253992 743411777 2 -80.6825899919003 43.1407399960802 -80.6830700233299 43.1394600295052 0 1256842861 6.88895634299567 21.4452066187493 35948502253992 743411777 2 -80.6830700233299 43.1394600295052 -80.68353000075 43.1381900087434 0 1256842867 6.81978905022318 21.4452066187493 35948502253992 743411777 2 -80.68353000075 43.1381900087434 -80.6840000090416 43.1369199950256 0 1256842874 6.82915982736353 21.4452066187493 35932201116385 743411778 1 -80.6837300172132 43.1369500002555 -80.6832900085453 43.1382199696614 0 1256842877 4.45394634674168 32.7420969216286 35932201116385 743411778 1 -80.6832900085453 43.1382199696614 -80.6828399801028 43.1394899987922 0 1256842882 4.46037755637271 32.7420969216286 35932201116385 743411778 1 -80.6828399801028 43.1394899987922 -80.682389981059 43.1407600261243 0 1256842886 4.46026431115965 32.7420969216286 35932201116385 743417546 1 -80.689979996436 43.1275700193056 -80.6897100112995 43.1277999760038 0 1256842832 0.876549764185665 38.461606856942 10304139942743222 743417546 1 -80.689979996436 43.1275700193056 -80.6897100112995 43.1277999760038 0 1256842873 0.829482435996493 40.6440341080312 35932201116385 743417547 1 -80.6897100112995 43.1277999760038 -80.6886700032165 43.1288900126855 0 1256842836 3.84500182950044 38.461606856942 35932201116385 743417547 1 -80.6886700032165 43.1288900126855 -80.6876499783595 43.1299900225328 0 1256842839 3.84458566202761 38.461606856942 35932201116385 743417547 1 -80.6876499783595 43.1299900225328 -80.6866899788012 43.1311199711812 0 1256842843 3.84839424900487 38.461606856942

PND (GPS) Raw Data Format Analysis20090401_raw

ProbeID Distance Traveltimesince111970 Traveltimesec 374958593 742.1 1238612029 5.070857 374958593 797.5 1238612034 5.8658824 374958593 827.6 1238612040 3.1870587 374958593 882.3 1238612043 5.4700003 374958593 892.6 1238612048 0.9507692 374958593 905.19995 1238612049 1.1630769 374958593 918.69995 1238612050 1.2461538 374958593 930.6 1238612052 1.0984614 374958593 984.5 1238612053 5.2443247 374958593 1017.3 1238612058 3.3737142 374958593 1041 1238612061 2.5094118 374958593 1055.3 1238612064 1.5141176 374958593 1093.8 1238612065 5.5439997 374958593 1100.5 1238612071 0.70941174 374958593 1169.6 1238612072 6.546315 374958593 1186.6 1238612078 1.36 374958593 1232.7 1238612079 3.607826 374958593 1265.7999 1238612083 2.5353189 374958593 1281.4999 1238612086 0.88312495 374958593 1302.2999 1238612087 0.49499997 374958593 1357.9999 1238612088 3.1828573 374958593 1399.2999 1238612091 2.8592308 374958593 1422.7999 1238612094 1.6269231 374958593 1434.7999 1238612095 0.91914886 374958593 1461.6 1238612096 2.144 374958593 1513.2999 1238612098 4.7723074 374958593 1634.4999 1238612103 11.482104 392146108 4856.8003 1238582081 1.9574999 392146108 4877.0005 1238582083 9.09 392146108 4885.9004 1238582092 2.0024998 392146108 4894.6006 1238582094 10.44 392146108 4943.9004 1238582106 6.6959996

DepartureDateAndTime

ProbeID Speedkmhr TripDistance TripDistance (Km)

TripTime TravelTimePerKm Mean Travel Time per km

Standard Deviation

3/31/2009 22:00 306470529

90.5685738 12639.304 12.639304 502.3982657

39.74888694 106.41854 133.27384

3/31/2009 22:00 283599116

8.912220883

332.599 0.332599 134.3499466

403.9397191

3/31/2009 22:08 273865000

80.23956396

1412.7998 1.4127998 63.38617795

44.8656476

3/31/2009 22:11 282496095

88.59363052

1412.7998 1.4127998 57.40908517

40.63497544

3/31/2009 22:14 365972473

77.45389316

4353.602 4.353602 202.3522196

46.47926467

3/31/2009 22:15 367107696

57.28584946

862.801 0.862801 54.22078278

62.84274448

3/31/2009 22:17 281886175

79.44899171

1873.7998 1.8737998 84.90578842

45.31209173 46.093075 3.4917907

3/31/2009 22:18 311575247

86.18855291

5911.5014 5.9115014 246.916839 41.76888784

3/31/2009 22:22 343648593

82.86898355

1412.7998 1.4127998 61.37494467

43.44206778

3/31/2009 22:32 308357902

69.93134694

3689.2004 3.6892004 189.9165685

51.47905993

3/31/2009 22:33 399448740

74.2830656 3088.7001 3.0887001 149.6884959

48.46326644

3/31/2009 22:38 288143742

81.11162794

1412.7998 1.4127998 62.70468747

44.38327884 37.891933 5.8302098

3/31/2009 22:40 277752736

119.0336387

7421.6004 7.4216004 224.455555 30.24355165

3/31/2009 22:45 437584074

92.19193632

5459.101 5.459101 213.172262 39.04896831

3/31/2009 22:47 277605390

66.87158299

2040.301 2.040301 109.8386381

53.83452641 47.358861 17.770679

3/31/2009 22:50 354126967

42.66791046

101.9994 0.1019994 8.6059485 84.37254043

3/31/2009 22:52 340626798

97.90911118

4529.6994 4.5296994 166.5515869

36.76879462

3/31/2009 22:54 278026645

92.39961952

1412.7998 1.4127998 55.04437471

38.96119939

3/31/2009 22:56 275939671

102.5995166

9186.4011 9.1864011 322.3313818

35.08788462

3/31/2009 22:59 313665722

102.4816982

1412.7998 1.4127998 49.62914717

35.12822352

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Demo Site Selection Considerations

large urban/suburban area – typical congestion-related travel time variability characteristics

existing models that meet L04 technical approach / simulation functional requirements

– network size /configuration for meaningful measurement of time variability – vehicle trajectories / time distributions

data availability – primarily trajectory travel times

other considerations– willingness of jurisdictional authority to participate in the project and/or provide data

and base model– familiarity of research team staff with candidate network, data and model…

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Potential Sites - (best candidates so far noted with *)

Atlanta(trajectory data availability concerns)

Baltimore - Washington DC area California (San Francisco / Bay Area) Chicago

(cost considerations may be prohibitive)

New York City / Metro Area * * (most model requirements already met, wide-area GPS data from various sources)

Toronto *(most models already in place or close to completion, wide-area GPS & cell probe data)

Montreal(models in place, GPS data can be arranged, institutional/jurisdictional concerns)

other areas (Seattle, Phoenix, Detroit, Austin)

Project Products

Reports– Phase I reviews in detail fundamental approach, includes

supporting data, candidate networks, reliability measures– Phase II reports the results of model calibration and validation,

includes guidelines and materials for full replication of phase II– Phase III report incorporates reliability into travel models

Outreach– Pilot demonstrations of the simulation model– Brochure, website, “how to” CD– Information sessions and demonstrations– Visualization tools

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Product Audience for SHRP 2 L04

Practitioners and researchers Software vendors and developers Operations managers, planners in transportation

agencies interested in practical implications

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