Bus arrival time prediction at bus stop with multiple routes
mode - bus, infrastructure - stop, technology - passenger information, technology - automatic vehicle monitoring, technology - intelligent transport systems, place - asia
Bus arrival time prediction, Multiple bus routes, Support vector machine, Artificial neural network, k nearest neighbours algorithm
Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Yu, B., Lam, W.H.K., Tam, M.L. (2011). Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies. Article in Press, Corrected Proof.