Using High-Resolution Signal Controller Data in the Calibration of Signalized Arterial Simulation Models

Calibration of traffic simulation models is a critical component of simulation modeling. The increased complexity of the transportation network and the adoption of emerging vehicle- and infrastructure-based technologies and strategies have motivated the development of new methods and data collection to calibrate the simulation models. This study proposes the use of high-resolution signal controller data, combined with a two-level clustering technique for scenario identifications and a multi-objective optimization technique for simulation model parameter calibration. The evaluation of the calibration parameters resulting from the multi-objective optimization based on travel time and high-resolution signal controller data measures indicate that the simulation model that uses these optimized parameters produces significantly lower errors in the split utilization ratio, green utilization ratio, arrival on green, and travel time compared with a simulation model that uses the software's default parameters. When compared with a simulation model that uses calibration parameters obtained based on the optimization of the single objective of minimizing the travel time, the multi-objective optimization solution produces comparably low travel time errors but with significantly lower errors for the high-resolution signal controller data measures.