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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
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