Data-Driven Left-Turn Restriction Decision Framework for Urban Networks: A Case Study of Downtown Pittsburgh

Left-turn movements pose significant safety hazards and reduce the operational efficiency of signalized intersections. One effective strategy to mitigate these issues is to restrict conflicting left turns at strategic locations. However, determining the optimal locations for such restrictions in large urban networks is challenging, owing to the complexity of traffic dynamics and the large solution space. The aim in this work is to provide additional insight into the types of intersection most likely to provide a network-wide benefit if a left-turn restriction is applied. A two-stage methodology is proposed, where the first stage applies a bi-level optimization framework combining population-based incremental learning (PBIL) and the Aimsun micro-simulation platform to determine near-optimal left-turn restriction locations. The second stage introduces a binary logit model that is used to explain the traffic parameters influencing these decisions. When applied to the Pittsburgh traffic network, the PBIL algorithm demonstrated up to a 12% reduction in travel time under peak demand, without significantly increasing trip lengths. The logit model, trained on known demand scenarios, indicates that intersections with higher values of left-turning green ratio, flow ratio, and protected green ratio are less likely to benefit from left-turn restrictions. Furthermore, the model’s predictions for unknown demand levels can be used to identify locations of left-turn restrictions that are comparable to PBIL, concerning travel time improvements. This framework provides a data-driven approach for transportation agencies to determine optimal left-turn restriction locations, balancing operational efficiency and network performance.