Estimating the Mobility Benefits of Adaptive Signal Control Technology Using a Bayesian Switch-Point Regression Model

Abstract

The adaptive signal control technology (ASCT) is a traffic management strategy that adjusts signal timing parameters to optimize corridor performance based on actual traffic demand. This study used a Bayesian switch-point regression model (BSR) to estimate the mobility benefits of the ASCT. A 5.3-km (3.3-mi) corridor of Mayport Road in Jacksonville, Florida, was used as the case study. The results indicated that the ASCT improved travel speeds by 4% on midweekdays (Tuesday, Wednesday, and Thursday) in the northbound direction. However, in the southbound direction, mixed results were observed that may be attributed to higher driveway density and congestion. Moreover, the BSR model results revealed that there is a significant difference in the operating characteristics between with and without ASCT scenarios. Transportation agencies could use the findings of this study to justify and plan the future deployment of the ASCT.