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Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment

机译:互联车辆环境中车辆到达时间和信号正时的同时优化

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摘要

Most existing signal timing plans are optimized given vehicles’ arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. “Speed guidance model at a red light” and “speed guidance model at a green light” are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions.
机译:给定车辆的到达时间(即即将到来的车辆到达停车线的时间)作为外来输入,大多数现有的信号计时计划都得到了优化。在本文中,基于互联车辆(CV)技术,可以将车辆视为移动传感器,并根据当前信号状态和交通状况通过速度引导来动态调整其到达时间。因此,本研究提出了一种综合交通控制模型,以同时优化车辆的到达时间(或行驶速度)和信号定时。提出了“红灯下的速度引导模型”和“绿灯下的速度引导模型”以模拟行进速度和信号定时之间的影响。然后,提出了对车辆到达时间,车辆延迟和停车次数建模的方法。包括控制延迟,排队延迟和信号延迟在内的总延迟被用作所提出模型的目标。决策变量包括车辆到达时间,绿色启动时间以及每个阶段的绿色持续时间。采用滑动时间窗口来动态解决问题。与经典驱动信号控制方法和基于固定时间的速度引导模型进行优化后的结果相比,该模型可以显着减少旅行延误,并提高交通控制的灵活性和机动性。通过通信距离,连接车辆的市场渗透率和速度引导的符合率进行敏感性分析,进一步证明了该模型在各种交通条件下的潜力。

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