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Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp

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  • Davis, L.C.

Abstract

Mixed traffic flow consisting of vehicles equipped with adaptive cruise control (ACC) and manually driven vehicles is analyzed using car-following simulations. Simulations of merging from an on-ramp onto a freeway reported in the literature have not thus far demonstrated a substantial positive impact of ACC. In this paper cooperative merging for ACC vehicles is proposed to improve throughput and increase distance traveled in a fixed time. In such a system an ACC vehicle senses not only the preceding vehicle in the same lane but also the vehicle immediately in front in the other lane. Prior to reaching the merge region, the ACC vehicle adjusts its velocity to ensure that a safe gap for merging is obtained. If on-ramp demand is moderate, cooperative merging produces significant improvement in throughput (20%) and increases up to 3.6km in distance traveled in 600s for 50% ACC mixed flow relative to the flow of all-manual vehicles. For large demand, it is shown that autonomous merging with cooperation in the flow of all ACC vehicles leads to throughput limited only by the downstream capacity, which is determined by speed limit and headway time.

Suggested Citation

  • Davis, L.C., 2007. "Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 274-290.
    • Handle: RePEc:eee:phsmap:v:379:y:2007:i:1:p:274-290
    DOI: 10.1016/j.physa.2006.12.017
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    Citations

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    Cited by:

    1. Davis, L.C., 2012. "Mitigation of congestion at a traffic bottleneck with diversion and lane restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1679-1691.
    2. Quan Yu & Linlong Lei & Yuqi Bao & Li Wang, 2022. "Research on Safety and Traffic Efficiency of Mixed Traffic Flows in the Converging Section of a Super-Freeway Ramp," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    3. Yang, Da & Jin, Peter (Jing) & Pu, Yun & Ran, Bin, 2014. "Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 371-383.
    4. Guo, Lantian & Zhao, Xiangmo & Yu, Shaowei & Li, Xiuhai & Shi, Zhongke, 2017. "An improved car-following model with multiple preceding cars’ velocity fluctuation feedback," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 436-444.
    5. Zhang, Peng & Zhu, Huibing & Zhou, Yijiang, 2022. "Modeling cooperative driving strategies of automated vehicles considering trucks’ behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    6. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    7. Diakaki, Christina & Papageorgiou, Markos & Papamichail, Ioannis & Nikolos, Ioannis, 2015. "Overview and analysis of Vehicle Automation and Communication Systems from a motorway traffic management perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 147-165.

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