A data-driven methodology for prioritizing traffic signal retiming operations

Signal retiming is one of the chief responsibilities of municipal transportation agencies and is an important means for reducing congestion and improving transportation quality and reliability. Many agencies conduct signal retiming and adjustment in a schedule-based manner. However, leveraging a data-driven, need-based approach to the prioritization of signal retiming operations could better optimize use of agency resources. Additionally, the growing availability of probe vehicle data has made it an increasingly popular tool for use in roadway performance measurement. This thesis presents a methodology for utilizing segment-level probe-based speed data to rank the performance of traffic signal corridors for retiming purposes. This methodology is then demonstrated in an analysis of 79 traffic signal corridors maintained by the City of Austin, Texas. The analysis considers 15-minute speed records for all weekdays in September 2016 and September 2017 to compute metrics and rank corridors based on their relative performance across time periods. The results show that the ranking methodology compares corridors equitably despite differences in road length, functional class, and traffic signal density. Additionally, results indicate that the corridors prioritized by the ranking methodology represent a much greater potential for improving travel time than the corridors selected under the schedule-based approach. This methodology is then packaged into a web-based tool for integration into agency decision-making. Finally, consideration is given to how this methodology might be used to identify candidate corridors for implementing adaptive signal control techniques.