Calibration of Microsimulation Models for Nonlane-Based Heterogeneous Traffic at Signalized Intersections

A significant part of the world, especially in most of the Asian countries, has heterogeneous traffic characterized by diverse vehicles, changing composition, lack of lane discipline, etc., resulting in a very complex behavior. Microsimulation is, therefore, highly suited to model such traffic. However, these models need to be calibrated before their application. Although several studies have been reported in the literature on the methodologies for calibration, all of them have focused on homogeneous traffic conditions having good lane discipline. In highly heterogeneous traffic, several other factors such as traffic composition and static and dynamic characteristics of vehicles have to be considered in the calibration process. Moreover, side-by-side stacking of vehicles across the road width occurring in the absence of lane discipline should also be modeled. Hence, a methodology for representing nonlane-based driving behavior and calibrating a microsimulation model for highly heterogeneous traffic at signalized intersection is proposed. Calibration parameters were identified using sensitivity analysis, and the optimum values for these parameters were obtained by minimizing the error between the simulated and field delay using genetic algorithm. The proposed methodology is illustrated using Verkehr in Staedten simulation, a widely used psychophysical car-following model based microsimulation software. Signalized intersections having diverse traffic and geometric characteristics from two cities of India are taken as a case study.