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

Uvodnik

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

Puni tekst: engleski pdf 15.862 Kb

str. 331-348

preuzimanja: 98

citiraj

Sažetak

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.

Ključne riječi

CAV; traffic accident; fuel consumption prediction; energy saving

Hrčak ID:

304865

URI

https://hrcak.srce.hr/304865

Datum izdavanja:

28.6.2023.

Posjeta: 207 *