Spatial iterative coordination for parallel simulation-based optimization of large-scale traffic signal control

Abstract

Applying simulation-based optimization to city-scale traffic signal optimization can be challenging due to the large search space resulting in high computational complexity. A divide-and-conquer approach can be used to partition the problem and optimized separately, which leads to faster convergence. However, the lack of coordination among the partial solutions may yield a poor-quality global solution. In this paper, we propose a new method for simulation-based optimization of traffic signal control, called spatially iterative coordination for parallel optimization (SICPO), to improve coordination among the partial solutions and reduce synchronization between the partitioned regions. The traffic scenario is simulated to obtain the interactions, which is used to spatially decompose the scenario into regions and identify interdependencies between the regions. Based on the regions, the problem is divided into subproblems which are optimized separately. To coordinate between the subproblems, the interactions between partial solutions are synchronized in two ways. First, multiple iterations of the optimization process can be executed to coordinate the partial solutions at the end of each optimization process. Second, the partial solutions can also be coordinated among the regions by synchronizing the trips across the regions. To reduce computational complexity, parallelism can be applied on two levels: each region is optimized concurrently, and each solution for a region is evaluated in parallel. We demonstrate our method on a real-world road network of Singapore, where SICPO converges to an average travel time 21.6% faster than global optimization at 62.8× shorter wall-clock time.