A Bayesian Neural Network-Based Method to Calibrate Microscopic Traffic Simulators

Calibrating the microsimulation model is essential to enhance its ability to capture reality. The paper proposes a Bayesian neural network (BNN)-based method to calibrate parameters of microscopic traffic simulators, which reduces repeated running of simulations in the calibration and thus significantly improves the calibration efficiency. We use BNN with probability distributions on the weights to quickly predict the simulation results according to the inputs of the parameters to be calibrated. Based on the BNN model with the best performance, heuristic algorithms (HAs) are performed to seek the optimal values of the parameters to be calibrated with the minimum difference between the predicted results of BNN and the field-measured values. Three HAs are considered, including genetic algorithm (GA), evolutionary strategy (ES), and bat algorithm (BA). A TransModeler case of highway links in Shanghai, China, indicates the validity of the proposed calibration method in terms of error and efficiency. The results demonstrate that the BNN model is able to accurately predict the simulation and adequately capture the uncertainty of the simulation. We also find that the BNN-BA model produces the best calibration efficiency, while the BNN-ES model offers the best performance in calibration accuracy.