A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

• Main Authors: Hu Zhang, Shidong Liang, Yin Han, Minghui Ma, Rongmeng Leng
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Public transit; bus arrival time; signalized intersection; bus speed; traffic density
• Online Access: https://ieeexplore.ieee.org/document/9125903/

The arrival time of bus at the stop is critical data in design of bus operational strategies. Especially for real-time control strategies (i.e., signal priority system), bus arrival time is usually predicted to assess the next bus operation condition in the future (i.e., bus bunching and reliability of transit service) and then can be used as a decision basis of current control actions. The signalized intersection and surrounding traffic are the key factors in bus travel time prediction, but most previous approaches focus on the impact of signal control on bus delay only. This paper proposes a prediction model for arrival time at bus stop under the influence of both upstream signalized intersection and surrounding traffic flow. Considering the affected range of signalized intersection and the dynamic variation of bus speed, bus running processes are evaluated separately (including processes from a given detection point to stop line, through intersection, and from intersection to bus stop). In the proposed models, bus speed is deduced according to the change of traffic density at different locations to reflect the micro-impact of surrounding traffic flow on bus operation. The observed bus travel time is collected from actual investigations at two bus stops incorporating signalized intersection in Jinan City and compared with the predicted travel time in the proposed model. The results show that the proposed model has a low mean relative error. In addition, through the analysis of the maximum relative error, it also can be seen that vehicle queuing with random arrival of vehicles at stop line makes a gap between the prediction and the actual situation, which will be the focus of further research.