| The planning of transit services, assessment of operational strategies, and evaluation of service changes can benefit tremendously from high-fidelity transit network models. Traditional microsimulation models are infeasible for large networks due to onerous model construction and calibration and prohibitive computational requirements. They are typically only used to model individual corridors or small sub-networks. This study presents a data-driven mesoscopic simulation method that models surface transit movement based on open data and machine learning. After a comprehensive comparison of running speed models using multiple linear regression, support vector machine, linear mixed effect model, regression tree and random forest, the random forest running speed models and lognormal dwell time distribution models were used to perform stop-to-stop mesoscopic simulations. The model results adequately replicated variation in headways, delays, and dwell times. Validation at the stop level and the route level demonstrated the need to capture passenger demand and congestion variations in future studies. |
