Real-Time Group Route Guidance Based on an Integrated Demand-Diversion Prediction Model: Case Study

摘要

This study incorporates drivers' route diversion behavior (DRDB) into a dynamic origin destination (OD) demand estimation and prediction (DODE) model and establishes a group route guidance model based on an integrated demand-diversion prediction model. Test results of the case study show that the integrated demand-diversion prediction model can consider time-varying OD demand, time-varying traffic characteristics, and dynamic DRDB under information provision. The maximum deviation between the real OD volumes and DODE model under information provision is approximately 11.46%, MAPE is 4.53%, and normalized RMSE is 5.29%. Under the scenarios with fixed time-dependent OD demand and fixed compliance rates, the effectiveness of group route guidance is significantly reduced compared with that under the scenario with real time-dependent OD demand and real compliance rate. The proposed model can accurately estimate and predict the possible DRDB and the effects of traffic information on OD demand prediction by using real-time traffic detected data. Moreover, the proposed model can enhance the accuracy of OD demand and traffic state prediction under information provision, thereby increasing the effectiveness of the proposed network route guidance strategies.