A methodology for calibration of traffic micro-simulator for urban heterogeneous traffic operations

Abstract

Traffic micro-simulation is a widely accepted tool in many countries for the evaluation and assessment of alternative design schemes. However, for several developing countries, replicating heterogeneous, non-lane based traffic in a micro-simulation framework is gaining increased importance and still remains a challenge due to its complexity. The present study demonstrates a methodology to calibrate a traffic micro-simulation model giving due consideration to vehicle-class specific driver behavior in an urban Indian scenario for a midblock section and an intersection approach in Kolkata. The sensitive parameters affecting the driver behavior were identified for every vehicle type using Latin Hypercube design, taking vehicle class specific travel time as a performance measure. Linear regression models were developed for each vehicle class considering the sensitive driving behavior parameters. The models highlight that the dependency of measure of effectiveness (MOE) of one vehicle type is not only limited to its own driver behavior but also on parameters of other vehicle classes. A genetic algorithm based optimization was adopted to obtain optimal parameter sets for different vehicle classes. The optimum values were found to vary significantly across all vehicle classes at a 95% confidence level. Single and multi-criteria calibration principles are also implemented to yield much more realistic results and subsequently minimizing weighted error for all vehicle classes. • A traffic micro-simulation calibration methodology for heterogeneous traffic is proposed. • Linear regression models using sensitive parameters were developed. • Genetic algorithm based optimization was adopted to obtain optimal parameter values. • Different sets of optimal driving behavior parameter for different vehicle classes are suggested. • The optimal parameter sets were found to vary significantly across vehicle classes.