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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
28.06.2023
DOI
https://doi.org/10.7307/ptt.v35i3.179
ISSUE
2023 (Vol 35), Issue 3
LICENSE
Copyright (c) 2024 Qingchao Liu, Wenjie Ouyang, Jingya Zhao, Yingfeng Cai, Long Chen
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

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

Authors:Qingchao Liu, Wenjie Ouyang, Jingya Zhao, Yingfeng Cai, Long Chen

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

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