Calibration is an essential prerequisite to scenario evaluations using traffic micro-simulation models (TMMs). In the context of mixed-traffic operations, where different fast and slow moving vehicular modes form a heterogeneous environment, a well-calibrated model needs to give adequate importance to each mode to realistically replicate the complex interactions in the traffic stream. This paper presents a methodology for calibrating TMMs for such mixed-traffic conditions. A combination of vehicle mode-specific travel time distributions is adopted as the performance measure for the calibration. To aid practitioners, each step of the methodology is demonstrated using a VISSIM simulator considering a signalized corridor in the Kolkata metro city, India. The work includes genetic algorithm (GA)-based optimization for obtaining mode-specific parameter sets. The Kolmogorov-Smirnov test is carried out to compare the travel time distributions of different modes. The calibrated model is also validated considering several signalized approaches along the calibrated study corridor. The results show that the methodology is successful in developing a model for non-lane based mixed-traffic operations with vehicle mode-specific optimized parameter sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.