Multi-Objective Calibration Of Microscopic Traffic Simulation For Highway Traffic Safety

Microscopic traffic simulation has become an important tool to investigate traffic efficiency and road safety. In order to produce meaningful results, driver behaviour models need to be carefully calibrated to represent real world conditions. If this type of simulations are to be used to evaluate safety features of traffic, on top of macroscopic relationships such as the speed-density diagram, they should also adequately represent the average risk of accidents occurring on the road. In this paper, we present a two-stage computationally feasible multi-objective calibration process. The first stage performs a parameter sensitivity analysis to select only parameters with considerable effect on the respective objective functions. The second stage employs a multi-objective genetic algorithm utilizing only few influential parameters that produces a front of Pareto optimal solutions with respect to the conflicting objective functions. Compared to traditional methods which focus on only one objective while sacrificing the accuracy of the other, our method achieves a high degree of realism for both traffic flow and average risk.