Modelling heterogeneous traffic dynamics by considering the influence of V2V safety messages

Tenglong Li, Fei Hui, Xiangmo Zhao, Ce Liu, Dong Ngoduy

Research output: Contribution to journalArticleResearchpeer-review


In recent years, more attention has been paid to the study of modelling heterogeneous traffic flow dynamics consisting of regular vehicles (RVs) and connected vehicles (CVs). However, the establishment of current mixed traffic flow models mostly lacks the support of field experimental data analysis results. In this study, a new heterogeneous car-following model is presented on the basis of data mining results to study the impact of vehicle-to-vehicle (V2V) safety messages on traffic flow stability. The analytically critical stability criterion of this proposed heterogeneous model is obtained by linear stability analysis. Then, the effect of the market penetration rate (MPR) of CVs on heterogeneous traffic stability is investigated. Finally, simulation experiments are carried out under periodic boundary conditions to test the critical stability condition. Both the theoretical derivation and simulation experiment results show that the traffic flow stability can decline despite improvements in traffic safety in the case of considering only the safety information of the nearest-neighbour leading vehicle. However, both the safety and stability of traffic flow can be enhanced by considering the V2V messages of more leading vehicles. Moreover, the heterogeneous traffic flow stability is improved with an increase in the MPR of CVs.

Original languageEnglish
Pages (from-to)220-227
Number of pages8
JournalIET Intelligent Transport Systems
Issue number4
Publication statusPublished - 2020
Externally publishedYes

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