Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic
Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic
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DOI：
10.1016/j.trc.2016.01.011
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发表时间：
2016-08
影响因子：
8.3
通讯作者：
Nianfeng Wan;A. Vahidi;André Luckow
中科院分区：
文献类型：
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作者：
Nianfeng Wan;A. Vahidi;André Luckow
配备速度咨询系统（SAS）的互联车辆（CV）可以获取并利用即将到来的交通信号信息来提前管理其速度，降低油耗，并通过减少红灯时的怠速来提高乘坐舒适性。本文提出了一种用于预定时交通信号的SAS，并得到了燃料最小化驾驶策略作为燃料消耗最小化问题的解析解。我们表明，最少的燃料驱动策略可能会违背一些人的直觉，因为它交替之间的最大加速，发动机关闭，有时恒定的速度，在最优控制称为砰-奇异-砰控制。在提出这种燃料最小化问题的分析解决方案后，我们采用了次优的解决方案，使驾驶性能不被牺牲，并显示燃油经济性仍然显着提高。此外，本文评估的影响，与SAS的车辆对整个干线交通的微观模拟。结果表明，装配SAS系统的车辆不仅提高了自身的燃油经济性，而且对其他常规车辆也有一定的促进作用，车队的燃油消耗量随着车辆装配SAS系统比例的增加而降低。我们表明，这种改善燃油经济性的平均交通流量和旅行时间的一点妥协。
Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre-timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang-singular-bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub-optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro-simulations. The results show that SAS-equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS-equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.