- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LIANG Jun, GENG Haoran, CHEN Long, YU Bin, LU Guangquan. Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon[J]. Journal of Southwest Jiaotong University, 2023, 58(5): 1090-1099. doi: 10.3969/j.issn.0258-2724.20220313
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In order to investigate the heterogeneous traffic flow characteristics formed by the combination of connected autonomous vehicles (CAVs) and human-pilot vehicles (HPVs), as well as the impact of bus driving behavior on this environment, four following modes in heterogeneous traffic flow are initially analyzed: human-pilot car following, human-pilot bus following, adaptive cruise control (ACC) following, and cooperative adaptive cruise control (CACC) following. Subsequently, based on the characteristics of each following model, a cellular automaton model for vehicle following and lane-changing is constructed, which comprehensively considers the characteristics of CAV platoons, the response time characteristics of drivers and CAVs, and the cutting-in behavior of HPVs. By setting the following mode judgment parameters, different following mode characteristics are integrated to achieve a unified model representation. Lastly, through simulation experiments, the queuing intensity of CAVs at different penetration rates and the impact of bus lane-changing behavior on traffic flow are analyzed. The results indicate that promoting CAVs to form platoons at a certain penetration rate is more effective in improving road traffic efficiency than merely increasing the CAV penetration rate; a moderate amount of bus lane-changing contributes to the full utilization of road traffic capacity, while excessive bus lane-changing hinders normal traffic. The traffic efficiency attenuation caused by bus lane-changing decreases as the CAV penetration rate increases; under synchronized flow conditions, the execution rate of human-pilot cars is negatively correlated with road traffic efficiency; however, under congested flow conditions, the impact of human-pilot car execution rate on traffic efficiency is negligible.
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