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Article

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions
Qingchao Liu, Wenjie Ouyang, Jingya Zhao, Yingfeng Cai, Long Chen
Keywords:CAV, traffic accident, fuel consumption prediction, energy saving

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.

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Published
28.06.2023
Copyright (c) 2023 Qingchao Liu, Wenjie Ouyang, Jingya Zhao, Yingfeng Cai, Long Chen

Published by
University of Zagreb, Faculty of Transport and Traffic Sciences
Online ISSN
1848-4069
Print ISSN
0353-5320
SCImago Journal & Country Rank
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