Bus travel time prediction based on state recognition

Bus running state is always influenced by traffic flow. Traffic flow state evolution directly leads to the variation of bus speed, thereby affecting bus progress. Some investigations implied that there is a certain relationship between the bus speed and other traffic vehicle speed on the road. However, the relationship is different due to individual forms of road cross-sections, the number of lanes, and the traffic flow characteristics. Based on this mechanism and the forecast of road traffic flow data provided by intelligent transportation system (ITS), a state recognition-based model for travel time prediction is proposed, and three running states are introduced in the model. Finally, the bus travel time predicted by the proposed model are assessed with real-world data collected from bus route number 53 in Wuxi city. Results show that the proposed model outperforms the history data-based model, especially when the bus running state falls unstable; the model achieves satisfactory performance.