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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    Title :

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


    Contributors:
    Liu, Qingchao (author) / Ouyang, Wenjie (author) / Zhao, Jingya (author) / Cai, Yingfeng (author) / Chen, Long (author)

    Publication date :

    2023-01-01


    Remarks:

    Promet - Traffic&Transportation ; ISSN 0353-5320 (Print) ; ISSN 1848-4069 (Online) ; Volume 35 ; Issue 3


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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