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Improving traffic flow at a 2-to-1 lane reduction with wirelessly connected, adaptive cruise control vehicles

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  • Davis, L.C.

Abstract

Wirelessly connected vehicles that exchange information about traffic conditions can reduce delays caused by congestion. At a 2-to-1 lane reduction, the improvement in flow past a bottleneck due to traffic with a random mixture of 40% connected vehicles is found to be 52%. Control is based on connected-vehicle-reported velocities near the bottleneck. In response to indications of congestion the connected vehicles, which are also adaptive cruise control vehicles, reduce their speed in slowdown regions. Early lane changes of manually driven vehicles from the terminated lane to the continuous lane are induced by the slowing connected vehicles. Self-organized congestion at the bottleneck is thus delayed or eliminated, depending upon the incoming flow magnitude. For the large majority of vehicles, travel times past the bottleneck are substantially reduced. Control is responsible for delaying the onset of congestion as the incoming flow increases. Adaptive cruise control increases the flow out of the congested state at the bottleneck. The nature of the congested state, when it occurs, appears to be similar under a variety of conditions. Typically 80–100 vehicles are approximately equally distributed between the lanes in the 500 m region prior to the end of the terminated lane. Without the adaptive cruise control capability, connected vehicles can delay the onset of congestion but do not increase the asymptotic flow past the bottleneck. Calculations are done using the Kerner–Klenov three-phase theory, stochastic discrete-time model for manual vehicles. The dynamics of the connected vehicles is given by a conventional adaptive cruise control algorithm plus commanded deceleration. Because time in the model for manual vehicles is discrete (one-second intervals), it is assumed that the acceleration of any vehicle immediately in front of a connected vehicle is constant during the time interval, thereby preserving the computational simplicity and speed of a discrete-time model.

Suggested Citation

  • Davis, L.C., 2016. "Improving traffic flow at a 2-to-1 lane reduction with wirelessly connected, adaptive cruise control vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 320-332.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:320-332
    DOI: 10.1016/j.physa.2016.01.093
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    References listed on IDEAS

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    3. 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.
    4. Liu, Huaqing & Jiang, Rui, 2021. "Improving comfort level in traffic flow of CACC vehicles at lane drop on two-lane highways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    5. Davis, L.C., 2017. "Dynamic origin-to-destination routing of wirelessly connected, autonomous vehicles on a congested network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 93-102.
    6. Davis, L.C., 2018. "Dynamics of a long platoon of cooperative adaptive cruise control vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 818-834.
    7. Xu, Ting & Jiang, Ruisen & Wen, Changlei & Liu, Meijun & Zhou, Jiehan, 2019. "A hybrid model for lane change prediction with V2X-based driver assistance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. 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).

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