Oscillation Growth in Mixed Traffic of Human-Driven and Automated Vehicles in Both Experimental Study and Simulation
Shiteng Zheng, Rui Jiang, Michael Zhang, Junfang Tian, Ruidong Yan, Bin Jia, Ziyou Gao
Beijing Jiaotong University University of California, Davis Tianjin University
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This paper aims to study how automated vehicles (AVs) impact traffic oscillation growth in a mixed platoon of human-driven vehicles (HVs) and AVs. To this end, we perform an experimental investigation complemented by extended simulation studies. In the experiment, the leading vehicle moves with a constant speed as a moving bottleneck, whereas the following vehicles consist of six programmable AVs implementing a constant-time-gap car-following policy, uniformly distributed among various numbers of HVs. Thus, the market penetration rate (MPR) of AVs decreases as the platoon size increases. The experimental results indicate that at high MPRs, AVs effectively suppress the growth of oscillations. However, the dampening effect diminishes abruptly and almost vanishes as the MPR decreases from 67% to 50%. In contrast, traffic throughput exhibits an approximately linear relationship with MPR. A simulation study is conducted to reproduce these findings. A good agreement with the experimental results validates the simulation study. The simulation study is then extended to a broader range of scenarios, yielding several insights: (i) the position of AVs within mixed platoons has subtle effects on the overall flow rate but significantly impacts oscillation growth, (ii) fine-tuning upper-level control parameters can potentially reduce oscillations while also enhancing throughput, and (iii) the synergy between automated driving and vehicle-to-vehicle communication has the potential to further attenuate traffic oscillations. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72288101, 72222021, W2411064, 72401022, and 71931002], the Fellowship of China National Postdoctoral Program for Innovative Talents [Grant BX20240033], and the Beijing Natural Science Foundation [Grants 9242013 and G2024210009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0377 .
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工程Traffic control and management
Autonomous Vehicle Technology and Safety · Transportation Planning and Optimization
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