Modeling Evaluation of Eco–Cooperative Adaptive Cruise Control in Vicinity of Signalized Intersections

Author:

Ala Mani Venkat1,Yang Hao2,Rakha Hesham13

Affiliation:

1. Charles E. Via, Jr., Department of Civil and Environmental Engineering, College of Engineering, 200 Patton Hall, Blacksburg, VA 24061

2. Department of Civil and Environmental Engineering, College of Engineering, Lamar University, 2622 Cherry Engineering Building, Beaumont, TX 77710

3. Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, 3500 Transportation Research Plaza, Blacksburg, VA 24061

Abstract

Vehicle stops caused by traffic signals reduce vehicle fuel economy ratings along arterial roadways. Eco–cooperative adaptive cruise control (eco-CACC) systems are being developed in an attempt to improve vehicle fuel efficiency in the vicinity of signalized intersections. These eco-CACC systems utilize traffic signal phasing and timing data received through vehicle-to-infrastructure communication, together with vehicle queue predictions, to compute fuel-optimum vehicle trajectories that are continuously updated as the vehicle travels in the vicinity of signalized intersections. The algorithm computes a desired speed for the vehicle that is either displayed to the driver or directly integrated into the vehicle’s adaptive cruise control system. In this paper, the INTEGRATION microscopic traffic assignment and simulation software is used to evaluate the performance of a proposed eco-CACC algorithm to assess its networkwide energy and environmental impacts. A simulation sensitivity analysis demonstrates that as the market penetration rate of CACC-equipped vehicles increases, the energy and environmental benefits also increase, and that the overall savings in fuel consumption are as high as 19% when the market penetration rate is 100%. On multilane roads, the algorithm may produce networkwide increases in the fuel consumption level when the market penetration rate is less than 30%. The analysis also demonstrates that the length of control segments, the signal phasing and timing plan, and the traffic demand levels significantly affect the algorithm performance. The study further demonstrates that the algorithm may produce increases in fuel consumption levels when the network is oversaturated; thus, further work is needed to enhance the algorithm for these conditions.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

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