Assessment of Mobility Impacts of Adaptive Traffic Control System (ATCS) Using Flexible Regression Models

• Activity Number: 391 - Improving Quality
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Quality and Productivity Section
• Author(s): John Herman Kodi* and Emmanuel Kidando and Angela Kitali and Priyanka Alluri and Thobias Sando
• Companies: Florida International University and Cleveland State University and University of Washington Tacoma and Florida International University and University of North Florida
• Keywords: Adaptive Traffic Control System; Flexible Bayesian Model; Buffer Index; Coefficient of Variation

Abstract

The adaptive traffic control system (ATCS) is a Transportation Systems Management and Operations (TSM&O) strategy that automatically and dynamically adjusts the signal timing parameters to optimize corridor performance based on real-time traffic demand. This study introduces a flexible Bayesian analytical approach for fitting the travel time pattern and ultimately ascertaining the mobility impacts of ATCS based on buffer index (BI) and coefficient of variation (CV) of travel time as the performance measures. This research used Bayesian inference, which overcomes the limitations of traditional methods of assessing the null hypothesis that may result in overestimating the effect and lead to inappropriate interpretations. The analysis was based on a 3.3-mile corridor with ten signalized intersections equipped with SynchroGreen ATCS in Jacksonville, Florida. Overall, the model estimates indicated that ATCS was associated with a lower BI and CV of travel time, thus improving travel time reliability along the corridor. Transportation agencies and other stakeholders could use the finding from this study to justify the deployment and expansion of ATCS at signalized intersections.