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Promet - Traffic&Transportation, Vol. 35 No. 3, 2023.

Editorial

https://doi.org/10.7307/ptt.v35i3.179

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Qingchao Liu ; Automotive Engineering Research Institute, Jiangsu University; School of Mechanical and Aerospace Engineering, Nanyang Technological University; Jiangsu University Research Institute of Engineering Technology
Wenjie Ouyang ; Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology
Jingya Zhao ; Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology
Yingfeng Cai ; Automotive Engineering Research Institute, Jiangsu University
Long Chen ; Automotive Engineering Research Institute, Jiangsu University

Full text: english pdf 15.862 Kb

page 331-348

downloads: 98

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Abstract

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers.

Keywords

CAV; traffic accident; fuel consumption prediction; energy saving

Hrčak ID:

304865

URI

https://hrcak.srce.hr/304865

Publication date:

28.6.2023.

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