论文标题

信号交叉处的生态驱动:多重信号优化方法

Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

论文作者

Yang, Hao, Almutairi, Fawaz, Rakha, Hesham A.

论文摘要

连续的交通信号交叉点可以增加车辆停车,在动脉道路上产生车辆加速度,并可能增加车辆燃油消耗水平。生态驾驶系统是一种借助车辆连通性提高车辆能效的方法。在本文中,开发了一个生态驾驶系统,该系统计算出燃料优化的车辆轨迹,同时遍历多个信号交叉点。该系统以模块化和可扩展的方式设计,允许它在大型网络中实现,而不会显着提高计算复杂性。所提出的系统利用信号相分和时机(吐口水)数据,这些数据与连接车辆(CVS)以及实时车辆动力学一起计算燃油 - 优越轨迹。所提出的算法纳入了集成微观流量分配和仿真软件中,以对各种变量进行全面的敏感性分析,包括:系统市场渗透率(MPRS),需求水平,相位拆分,偏移量,偏移和交通信号间距在系统性能上。分析表明,在100 \%MPR时,燃料消耗可以降低到13.8 \%。此外,更高的MPR和较短的相长度可节省较大的燃料。存在最佳需求水平和交通信号间距,可最大程度地提高算法的有效性。此外,该研究表明,当交通信号偏移量更接近其最佳值时,该算法的效果降低。最后,该研究强调了需要进一步工作以增强算法以应对过度饱和交通状况的必要性。

Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computational complexity. The proposed system utilizes signal phasing and timing (SPaT) data that are communicated to connected vehicles (CVs) together with real-time vehicle dynamics to compute fuel-optimum trajectories. The proposed algorithm is incorporated in the INTEGRATION microscopic traffic assignment and simulation software to conduct a comprehensive sensitivity analysis of various variables, including: system market penetration rates (MPRs), demand levels, phase splits, offsets and traffic signal spacings on the system performance. The analysis shows that at 100\% MPR, fuel consumption can be reduced by as high as 13.8\%. Moreover, higher MPRs and shorter phase lengths result in larger fuel savings. Optimum demand levels and traffic signal spacings exist that maximize the effectiveness of the algorithm. Furthermore, the study demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value. Finally, the study highlights the need for further work to enhance the algorithm to deal with over-saturated traffic conditions.

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