Operational performance evaluation of adaptive traffic control systems: A Bayesian modeling approach using real-world GPS and private sector PROBE data

Abstract

This evaluation ascertained the operational impacts of the SUTRAC (Scalable Urban Traffic Control) Adaptive Signal Control Technology (ASCT) in an urban corridor consisting of 23 intersections in Pittsburgh, Pennsylvania. A combination of real-world GPS floating car runs and private sector probe data from INRIX was used to assess the impact of the ASCT. Data were collected with the ASCT active and inactive to determine the operational impacts on the mainline and cross streets. The ASCT was found to produce significant improvements in the number of stops made along the corridor. On Baum and Center, travel times improved during the AM and PM peak in the WB direction. Speeds were also observed to improve significantly during the Midday period on Baum EB and during the AM and PM peak periods on Baum WB. Similarly, statistically significant improvements in speed were observed on Center WB during the AM and PM periods, while a statistically significant decrease in speed was observed during the Midday period. Six months of private sector probe data was used to examine travel time reliability along the corridor, and reliability was also found to have improved. Further, Bayesian models were calibrated to account for variations in speeds and acceleration/deceleration. The Bayesian models revealed that driving was less volatile with the ASCT system in operation over instantaneous periods, which also points towards improved operations. The findings of this study are generally consistent with past evaluations of other ASCTs, indicating that the SURTRAC system is another potential tool for managing congestion on signalized urban arterial networks.