Calibration method for microscopic traffic simulation considering lane difference

Lane-level differences in traffic conditions on urban roads are becoming increasingly significant. To remedy this problem, this study proposes a method for the microscopic traffic simulation calibration problem that considers the complexity of traffic conditions on-road sections and the differences in operating states between lanes. A simulation model was established by collecting actual data. Calibration parameters were determined using sensitivity analysis. A calibration model was built to minimize the relative errors of the roadway efficiency and lane differential indicators. The values of these parameters were obtained using a genetic algorithm (GA). The calibration processes were automated using programming. To assess the reliability of the proposed method, we conducted five sets of comparative experiments focusing on two aspects: calibration methods and algorithm utilization. Results indicate that the proposed method significantly enhances simulation accuracy, particularly in lane-level traffic simulations. In comparison to approaches considering only section-level traffic characteristics and default application software parameters, the proposed method yielded reductions in errors by 3.7%, 5.8%, 6.6%, and 3.2% for simulating lane occupancy rate and cross-section flow. The proposed method demonstrated a simulation error of approximately 5%, while the artificial neural network method was about 7%, validating the effectiveness of the algorithms employed. It can play a crucial role in multilane traffic flow, intelligent driving tests, vehicle–road cooperation, and other related study areas.