Dynamic Eco-Driving Speed Guidance at Signalized Intersections: Multivehicle Driving Simulator Based Experimental Study

Author:

Chen Peng12ORCID,Yan Cong1,Sun Jian3ORCID,Wang Yunpeng12,Chen Shenyang4,Li Keping3

Affiliation:

1. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Xue Yuan Road No. 37, Hai Dian District, Beijing 100191, China

2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Xue Yuan Road No. 37, Hai Dian District, Beijing 100191, China

3. Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China

4. Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA

Abstract

Variations in vehicle fuel consumption and gas emissions are usually associated with changes in cruise speed and the aggressiveness of drivers’ acceleration/deceleration, especially at traffic signals. In an attempt to enhance vehicle fuel efficiency on arterials, this study developed a dynamic eco-driving speed guidance strategy (DESGS) using real-time signal timing and vehicle positioning information in a connected vehicle (CV) environment. DESGS mainly aims to optimize the fuel/emission speed profiles for vehicles approaching signalized intersections. An optimization-based rolling horizon and a dynamic programming approach were proposed to track the optimal guided velocity for individual vehicles along the travel segment. In addition, a vehicle specific power (VSP) based approach was integrated into DESGS to estimate the fuel consumption and CO2 emissions. To evaluate the effectiveness of the overall strategy, 15 experienced drivers were recruited to participate in interactive speed guidance experiments using multivehicle driving simulators. It was found that compared to vehicles without speed guidance, those with DESGS had a significantly reduced number of stops and approximately 25% less fuel consumption and CO2 emissions.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering

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