Abstract

Traffic demands at intersections vary across various periods of a day and from day to day. Generally, fixed time traffic signals are designed considering the average traffic flows across multiple days over a predetermined time interval. This approach overlooks the day to day variability in traffic demand, leading to inefficient and unreliable signal control performance. A signal plan should be robust such that it is less sensitive to demand variations and can maintain near-optimal performance during varying traffic demand. To address this need, the paper presents a new offline scenario-based framework, named Metaheuristic Robust plan Approach (MHRA), that identifies a robust plan for fixed time signals. MHRA includes a heuristic that considers optimum signal plan for various demand scenarios and corresponding costs to find a robust solution. The numerical experiments are performed using realistic traffic demand scenarios on an arterial corridor to verify the MHRA framework. The outcomes concluded that the framework produces a robust signal plan that outperforms a nominal signal plan based on average traffic demand and maintains stable performance under varying demand. Benchmarking MHRA with other scenario-based approaches proposed in the literature such as mean-variance optimization and conditional value at risk minimization confirms better efficiency for MHRA.

摘要

十字路口的交通需求在一天中的不同时段以及每天都在变化。通常，固定时间交通信号的设计考虑了预定时间间隔内多天的平均交通流量。这种方法忽略了交通需求的日常变化，导致低效和不可靠的信号控制性能。信号计划应该是稳健的，这样它对需求变化不太敏感，并且可以在变化的交通需求期间保持接近最佳的性能。为了解决这一需求，本文提出了一种新的基于离线场景的框架，名为Metaheuristic Robust plan Approach(MHRA)，它确定了固定时间信号的稳健计划。MHRA 包含一个启发式算法，它考虑各种需求场景的最佳信号计划和相应的成本，以找到稳健的解决方案。数值实验是在主干道上使用现实的交通需求场景进行的，以验证 MHRA 框架。结果得出结论，该框架产生了一个强大的信号计划，该计划优于基于平均交通需求的标称信号计划，并在变化的需求下保持稳定的性能。将 MHRA 与文献中提出的其他基于情景的方法进行基准测试，例如均值方差优化和风险最小化条件值，证实了 MHRA 的效率更高。