A population-based incremental learning algorithm to identify optimal location of left-turn restrictions in urban grid networks

The treatment of left turns at signalized intersections drives the development of signal phasing and timing plans and also plays an important role in overall traffic network operations. Accommodating left turns allows for the most direct routeing but reduces intersection capacity, whereas restricting left turns improves capacity but requires some vehicles to travel longer distances. This paper proposes a population-based incremental learning (PBIL) algorithm to determine at which intersections left-turn restrictions should be enacted to maximize a network's operational performance. The performance of each configuration is tested in a micro-simulation environment on both perfect and imperfect square grid networks. Comparison with a partial enumeration of feasible options reveals that the PBIL algorithm is effective at identifying a near-optimal configuration of left-turn restrictions. The resulting configurations suggest that left turns should be generally restricted at intersections that carry the most flow. These intersections typically occur in the central portion of the network when demands are relatively uniform. Doing so helps to provide additional intersection capacity at the locations where it is most necessary, while minimizing the additional travel distance that is incurred due to detours caused by the left-turn restrictions. These provide insight as to how urban traffic networks might be managed to improve network efficiency by only enacting left-turn restrictions at a subset of locations.