Using Genetic Algorithms to Design Signal Coordination for Oversaturated Networks
Using Genetic Algorithms to Design Signal Coordination for Oversaturated Networks
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DOI：
10.1080/15472450490435340
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发表时间：
2004-04
影响因子：
3.6
通讯作者：
Montty Girianna;R. Benekohal
中科院分区：
文献类型：
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作者：
Montty Girianna;R. Benekohal
本文提出了一种生成最优(实时)信号配时的算法，该算法将队列分布在多个信号交叉口和任何信号交叉口上的多个周期上。将离散时间信号协调模型描述为动态优化问题，并用遗传算法(GA)进行求解。网络中所有交叉口在拥堵期间的信号定时是决策变量，并且表示在各个GA候选解中。该算法被应用于一个有20个信号交叉口的单向主干道网络。该算法根据交通需求的变化和关键信号的位置，智能地产生沿个别主干道的最优信号配时(偏移量)。如果关键信号位于出口点，则该算法设置最佳信号定时，以保护它们不会变得过度负荷。如果关键信号位于入口点，该算法确保在释放的排到达下游信号系统之前减少或清除队列。本文采用多历元的简单遗传算法(SGA)来解决信号协调问题。当串行SGA用于解决交通控制问题时，其在计算时间方面的性能随着信号网络规模的增加或拥塞持续时间的延长而降低。然后使用在并行计算机上执行的主从式SGA来减少执行时间。我们发现，在主从并行的情况下，SGA可以在显著加速的情况下高效执行，从而有机会在实时信号控制系统上实现该算法。
This article presents an algorithm to generate optimal (real-time) signal timings that distribute queues over a number of signalized intersections and over a number of cycles on any signalized intersection. A discrete-time signal-coordination model is formulated as a dynamic optimization problem and solved using Genetic Algorithms (GA). Signal timings for all intersections in the network during congested periods are decision variables and are represented in the individual GA candidate solutions. The algorithm is applied to a one-way arterial network with 20 signalized intersections. Depending on the traffic demand's variation and the position of critical signals, the algorithm intelligently generates optimal signal timing (offsets) along individual arterials. If critical signals are located at the exit points, the algorithm sets the optimal signal timing that protects them from becoming excessively loaded. If critical signals are located at the entry points, the algorithm ensures that queues are reduced or cleared before released platoons arrive at a downstream signal system. In this article, the simple genetic algorithm (SGA) with multiple epochs is used to solve the signal coordination problem. When a serial SGA is applied to solve traffic control problems, its performance in terms of computation time diminishes as the size of signal networks increases, or the duration of congestion lengthens. Master-slave SGA executed in a parallel computing machine is then used to reduce the execution time. We found that with a master-slave parallelism, SGA can be efficiently executed with significant speed-up, allowing the opportunity to implement the algorithm on real-time signal control systems.