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A Dynamic Optimization Method for Adaptive Signal Control in a Connected Vehicle Environment
Journal of Intelligent Transportation Systems ( IF 2.8 ) Pub Date : 2019-08-01 , DOI: 10.1080/15472450.2019.1643723
Zhihong Yao 1, 2, 3, 4 , Yangsheng Jiang 1, 2 , Bin Zhao 1, 2 , Xiaoling Luo 1, 2 , Bo Peng 4, 5
Affiliation  

Abstract In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner.

中文翻译:

车联网环境下自适应信号控制的动态优化方法

摘要 在联网的车辆环境中,车辆位置、速度和其他交通信息随时可用;因此,这样的环境为交通信号控制优化提供了新的数据源。现有基于固定检测器的自适应信号控制系统无法直接获取车辆位置和速度信息,因此无法提供实时交通流量变化的准确信息。本研究提出了一种在联网车辆环境中自适应信号控制的动态优化方法。首先,所提出的方法开发了一个动态队列分散模型,通过使用连接的车辆数据来预测车辆到达。然后,以车辆平均延误最小化为优化目标,构建信号配时优化模型,并将每个阶段的绿色持续时间设置为约束。为实现实时自适应信号控制,采用遗传算法通过滚动优化求解优化模型。最后,在 Vissim 中对真实世界的道路网络进行建模以验证所提出的方法。仿真结果表明,与经典的自适应信号控制算法相比,所提出的方法能够将车辆延误和排队长度减少至少50%的渗透率。在 100% 的渗透率下,所提出的方法将平均车辆延误和平均排队长度分别提高了 22.7% 和 24.8%。此外,它以平衡的方式迎合了各个方向。最后,在 Vissim 中对真实世界的道路网络进行建模以验证所提出的方法。仿真结果表明,与经典的自适应信号控制算法相比,所提出的方法能够将车辆延误和排队长度减少至少50%的渗透率。在 100% 的渗透率下,所提出的方法将平均车辆延误和平均排队长度分别提高了 22.7% 和 24.8%。此外,它以平衡的方式迎合了各个方向。最后,在 Vissim 中对真实世界的道路网络进行建模以验证所提出的方法。仿真结果表明,与经典的自适应信号控制算法相比,所提出的方法能够将车辆延误和排队长度减少至少50%的渗透率。在 100% 的渗透率下,所提出的方法将平均车辆延误和平均排队长度分别提高了 22.7% 和 24.8%。此外,它以平衡的方式迎合了各个方向。所提出的方法将平均车辆延误和平均排队长度分别提高了 22.7% 和 24.8%。此外,它以平衡的方式迎合了各个方向。所提出的方法将平均车辆延误和平均排队长度分别提高了 22.7% 和 24.8%。此外,它以平衡的方式迎合了各个方向。
更新日期:2019-08-01
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