Sustainable Transportation Systems: Indicators,

Approaches & Case Studies

Adel W. Sadek

Professor, Civil, Structural and Environmental Engineering

Director, Institute for Sustainable Transportation & Logistics

Director, Transportation Informatics University Transportation Center

University at Buffalo - SUNY

SUNY Conservations in the Disciplines

Binghamton University

Binghamton, NY

April 1, 2014

Presentation Outline

• Indicators of Sustainable Transportation

• Approaches to Sustainable Transportation

• Two Case Studies:

• Green Routing

• Motivation

• Methodology

• Results

• Conclusions

• Eco-signals and Cyber Transportation Systems

Applications

The Transportation Profession

• Historically focused on the goals of mobility & safety

• Recent environmental, economic & social objectives:

• Protect natural resources

• Improve public health

• Strengthen energy security

• Expand the economy

• Disadvantaged mobility

Sustainable Transportation

• Two aspects:

• Transport’s critical role in our planet’s sustainability

• Transportation systems need to be sustained to ensure

future generations have access to their economic and

social benefits

• A widely accepted definition of sustainable transportation

systems still does not exist

Sustainable Transportation

• “Allows basic access and development

needs to be met safely and in a manner

consistent with human and ecosystem

health, and promotes equity within and

between successive generations”

• “Is affordable, operates fairly and efficiently,

offers a choice of transport mode, supports

a competitive economy”

• “Limits air, water, and noise emissions,

waste, and resource use.”

Sustainable Transportation Strategies

• Vehicle/Fuel Technological Change • Improved efficiency of existing vehicles

• Alternative Vehicles/Fuels

• Demand Management • Modal Substitutions

• Telecommunications Substitutions

• Pricing Incentives/Disincentives

• Land use-transportation strategies

• Roadway & Vehicle Operations Efficiencies • Conventional Improvements

• Intelligent Transportation Systems

• Improved Logistics and Fleet Management

Green Routing Case Study

“Green” Routing

• The topic has been attracting attention since the 1990s

• Previous studies with few exceptions:

• Used simplified emissions models to evaluate benefits

• Were limited to small networks

• Did not evaluate the impact of market penetration

Study Objectives

• A realistic assessment, using a real-world case study,

of the likely environmental benefits

• Uses the latest state-of-the-art models: TRANSIMS &

EPA’s MOVES2010

• Assess the impact of market penetration

• Propose a “targeted” traveler selection strategy

• Approximate “green” user equilibrium

TRANSIMS

• Initially developed at Los Alamos

National Lab as representing the next

generation of transportation models

• A person-based simulator which

combines detailed modeling of traffic

flow dynamics with the ability to model

traveler behavior

Synthetic Popula tion

Genera tor

Activity Generator

Router

Micro-simulator

Emissions Estimator

Input Data

Fe

ed

ba

ck C

on

tro

ller

TRANSIMS

• Allows for:

• Activity-based modeling approach

• Trip-based approach

• Uses Cellular Automata (CA) for modeling traffic

EPA’s MOVES2010

• MOVES2010 designed to allow for project-level

analysis and based on PEMS measurements

• Three options for calculating emissions:

• Average speed & default driving cycles

• A “link drive schedule” – a second-by-second

speed profile for vehicles traversing a link

• Using an operating mode approach

Buffalo-Niagara TRANSIMS Model

A medium-sized metropolitan - population of 1.2

million

Includes a major tourist attraction (Niagara Falls),

several congested border crossings, and the I-90/I-

190/I-290 corridor within the U.S.

The full network has 7,798 road links

Subarea network (blue) includes 2,605 links

TRANSIMS micro-simulation was only executed

within the subarea network

Integrated TRANSIMS-MOVES Model

General Concept of Green Routing

An Emission Production Factor

(EPF) and Fuel Consumption

Factor (FCF) for each link

EPF and FCF are time-dependent

“Green assignment” considers the

link-based EPF or FCF as the

measure of a link’s travel cost

CO emissions/link within the subarea

(8am – 9am)

Market Penetration

• Two Strategies Evaluated:

• Random Market Penetration:

• Travelers who choose to use the green routing guidance

system are randomly distributed in the study area

• Different percentages simulated (10%, 20%, 30%, etc.)

• “Targeted” Market Penetration

• V2I & V2V environment to support selecting travelers who

have the greatest potential for emissions reductions

• Selection based on likely emissions or fuel savings

Individual Traveler’s Routing Results

Differences between shortest

path and a Green Routes:

Green routing: a 15%

reduction in CO emission, at

the expense of a 6.6%

increase in travel time.

Tradeoff between Optimization Objectives

Least Travel Time Assignment Green Routing Assignment (*Objective) % Savings

Pollutant or

Fuel Type

Average Emission/Fuel

Consumption per Traveller

(gm or gallon /traveller)

Pollutant/ Fuel

Objective being

Minimized

Emission/Fuel

Consumption per

Traveller

CO 64.330 CO* 56.205

* 12.63%

*

NOx 7.986 6.90%

Gasoline 0.326 0.70%

CO 59.258 7.89%

NOx 8.578 NOx* 7.688

* 10.37%

*

Gasoline 0.367 -11.90%

CO 58.955 8.36%

NOx 7.873 8.22%

Gasoline 0.328 Gasoline* 0.308

* 6.07%

*

Accompanying Increase in Travel Time

Least Travel Time

Assignment

Least CO

Assignment

Least NOx

Assignment

Least Gasoline

Assignment

Average Travel

Time (min) 6.786 7.327 7.698 7.384

% Increase 7.99% 13.46% 8.82%

Market Penetration Results

Market Penetration Results

Study’s Contributions

• Adapting MOVES to work with microscopic traffic

simulation models

• Developing an Integrated TRANSIMS-MOVES model

• A realistic assessment of likely system-wide impacts of

green routing on a real-world case study

• Pointing out the promise of intelligently selecting

travelers for re-routing

Conclusions • Shortest path may not always correspond to the green route

• Least emissions routing may not correspond to least fuel

consumption route

• “Targeted” market penetration has the potential to yield

significant environmental benefits at relative low market

penetration

• Reductions in emissions come at the expense of a modest

increase in travel time

Convergence of Technologies

• The vision of automated highways is not new, but what is

really new and exciting is the convergence of technologies

that is making this possible

• Advances in:

• Sensing technologies

• Communications

• Computing

• Mapping and Navigation (GPS)

• Ubiquitous computing and Big Data Analytics

• Connected and Automated vehicle technologies are truly

“disruptive” or “transformative” technologies

An Integrated Traffic-Driving-Networking Simulator (ITDNS)

1. Eco-signal Concept

• If drivers of approaching vehicles have accurate

information about the upcoming signal status (e.g. the

time remaining for the green phase or when the green

phase is to start), the vehicle speed may be adjusted

accordingly so as to avoid idling and/or hard

deceleration and acceleration maneuvers

• The need for human-in-the-loop testing capability

Eco-signal Algorithm

Eco-signal Application • Advisory Speed Panel

• Orange dial rotates to reflect the

current driving speed

• Number in the lower box reflects the

recommended speed that the driver

is advised to maintain

• Box background color references

three statuses:

(a) green: speed up

(b) red: slow down

(c) yellow: maintain current speed

Experimental Setup • ITDNS used to quantify the likely fuel and emissions savings of

eco-signals, while accounting for human driver response to speed

advisories

• 5 human subjects “virtually” drove, in ITDNS, a 2.5-mile long

segment of an arterial corridor with 10 signals, with the eco-signal

application “on” and “off”

• Fuel consumption & emissions calculated using MOVES2010

Preliminary Results • Average 9% Energy saving, 25% NOx and 18% CO

Comparing speed & acceleration profiles

Eco On Eco Off Savings

Max Accel. (mphps) 5.0 7.8 36%

Max Decel. (mphps) -8.9 -13.7 35%

Max Speed (mph) 36.4 45.0 19%

Contact Information:

Adel W. Sadek, Ph.D.

Professor

University at Buffalo – SUNY

Phone: (716) 645-4367

E-mail: asadek@buffalo.edu

ISTL website: http://www.buffalo.edu/istl

TransInfo: http://www.buffalo.edu/transinfo