Abstract

One of the main reasons for increasing carbon emissions by the transportation sector is the frequent congestion caused in a traffic network. Congestion in transportation occurs when demand for commuting resources exceeds their capacity and with the increasing use of road vehicles, congestion and thereby emissions will continue to rise if proper actions are not taken. Adoption of intelligent transportation systems like autonomous vehicle technology can help in increasing the efficiency of transportation in terms of time, fuel and carbon footprint. This research proposes a System Level Eco-Driving (SLED) algorithm and compares the results, produced by performing microscopic simulations, with conventional driving and the coordination heuristic (COORD) algorithm. The SLED algorithm is designed based on shortcomings and observations of the COORD algorithm to improve the traffic network efficiency. In the SLED strategy, a trailing autonomous vehicle would only request coordination if it is within a set distance from the preceding autonomous vehicle and coordination requests will be evaluated based on their estimated system level emissions impact. Additionally, the human-driven vehicles will not be allowed to change lanes. Average CO2 emissions per vehicle for SLED showed improvements ranging from 0% to 5% compared to COORD. Additionally, the threshold limit to surpass the conventional driving behavior CO2 emissions at 900 vehicles per hour density reduced to 30% using SLED as compared to 40% using the COORD algorithm. Average wait time per vehicle for the SLED algorithm at 1200 vehicles per hour density increased by one to six seconds as compared to the COORD strategy although reduced up to thirty seconds of wait time compared to the conventional driving behavior. This finding can be helpful for policy makers to switch the algorithms based on the requirement i.e. opt for the SLED algorithm if reducing emissions has a higher priority compared to wait and travel time while opt for the COORD algorithm if reducing wait and travel time has a higher priority compared to emissions.

Library of Congress Subject Headings

Intelligent transportation systems; Traffic congestion--Prevention; Carbon dioxide mitigation; Automated vehicles--Data processing

Publication Date

12-2019

Document Type

Thesis

Student Type

Graduate

Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)

Advisor

Katie McConky

Advisor/Committee Member

Michael E. Kuhl

Campus

RIT – Main Campus

Plan Codes

ISEE-MS

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