Calibration of Departure Time and Route Choice Parameters in Microsimulation with Macro Measurements and Genetic Algorithm

Microscopic traffic simulation has been seeing more and more applications in the formulation and assessment of transportation policy alternatives and Intelligent Transportation System (ITS) measures. Calibration of Micro-simulation is a vital step towards a successful application because it serves as an additional check of the model's validity and ensures model parameters reflect local conditions. In this study, a five-step procedure for microsimulation calibration is established that differentiates the driving behavior and departure time and route choice (D-R choice) behavior parameters explicitly. The procedure divides the calibration problem into successive, and sometimes iterative, subproblems and solves the subproblems one by one in a much smaller scale. This paper focuses on the calibration of D-R choice behavior parameters, developing a genetic algorithm tool to optimize the parameters in D-R choice. To incorporate users' knowledge about the local network into the calibration process, the tool applies a flexibly expandable structure to increase the algorithm computation efficiency. With other algorithmic improvements, the tool has been tested in both a trial network and a medium-size southern California network. Calibration results show good convergence and better performance in terms of matching the macro measurements (link counts and trip travel times).