Calibration of Micro Simulation with Heuristic Optimization Methods

Model calibration is a crucial step in building a reliable microscopic traffic simulation application, because it serves as the additional check to ensure the model parameters accurately reflect the local driving environment, such that decisions made based on these results would not be misinformed decisions. Because of its stochastic nature and complexity, the calibration problem, usually formulated as an optimization problem, is often solved using heuristic methods. To-date, calibration is still a timeconsuming task because many of the adopted methods require many simulation runs in search of an optimal solution. Moreover, many aspects of the calibration problem are not fully understood and need further investigation. In this study, we develop another heuristics calibration algorithm based on the simultaneous perturbation stochastic approximation (SPSA) scheme, and applied it to calibration several networks coded in Paramics. Our results indicate that the new heuristic algorithm can reach the same level of accuracy with considerably less iterations and CPU time than other heuristic algorithms such as the genetic algorithm (GA) and the trial-and-error iterative adjustment (IA) algorithm. Applications of all three heuristic methods in a northern California network also reveal that some model parameters affect the simulation results more significantly than others. These findings can help modelers better choose calibration methods and fine tune key parameters. Ma, Dong and Zhang 3 –