Research on the Stability Analysis of Car Following Model Influenced by Multiple Factors in the Internet of Vehicles Environment

DOI

10.2991/978-94-6463-516-4_13How to use a DOI?

Keywords

Car following model; Multi front vehicle information; Traffic flow; IoV

Abstract

In the context of Internet of Vehicles (IoV) environment, although drivers can timely obtain information that affects their driving behavior through the IoV technology, but manual driving will still be necessary for a long time in the future under the current trend of technological development. Therefore, in the actual driving process, the driver not only retains memory of the status information of the front and rear vehicles themselves, but also can obtain more vehicle information through the IoV. Therefore, based on existing car following models, this paper establishes a car following model that considers the dual memory effect of the following vehicle and the information of multiple preceding vehicles, and conducts stability analysis on the model. The results show that the model can effectively improve the stability of traffic flow.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiacheng Xiao
AU  - Yuxing Yin
AU  - Yanzun Zhang
AU  - Yang Dai
AU  - Wei He
PY  - 2024
DA  - 2024/09/17
TI  - Research on the Stability Analysis of Car Following Model Influenced by Multiple Factors in the Internet of Vehicles Environment
BT  - Proceedings of the 2024 5th International Conference on Urban Construction and Management Engineering (ICUCME 2024)
PB  - Atlantis Press
SP  - 103
EP  - 112
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-516-4_13
DO  - 10.2991/978-94-6463-516-4_13
ID  - Xiao2024
ER  -