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Improved Car-Following Model for Connected Vehicles Considering Backward-Looking Effect and Motion Information of Multiple Vehicles
| 题名： | Improved Car-Following Model for Connected Vehicles Considering Backward-Looking Effect and Motion Information of Multiple Vehicles |
| 正文语种： | eng |
| 作者： | Minghui Ma;Wenjie Wang;Shidong Liang;Jiacheng Xiao;Chaoteng Wu |
| 作者单位： | School of Mechanical and Automotive Engineering Shanghai Univ. of Engineering Science No. 333 Longteng Rd. Songjiang District Shanghai 201620 China;School of Mechanical and Automotive Engineering Shanghai Univ. of Engineering Science No. 333 Longteng Rd. Songjiang District Shanghai 201620 China;Business School Univ. of Shanghai for Science and Technology No. 516 Jungong Rd. Yangpu District Shanghai 200093 China;School of Mechanical and Automotive Engineering Shanghai Univ. of Engineering Science No. 333 Longteng Rd. Songjiang District Shanghai 201620 China;Shanghai Intelligent System Co. Ltd. No. 505 Wuning Rd. Putuo District Shanghai 200063 China |
| 关键词： | Car-following model; Backward looking; Connected vehicles; Multiple vehicles |
| 摘要： | To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 2 |
| 页码： | 04022148.1-04022148.13 |
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