Real-time and accurate travel time information of transit vehicles is valuable as it allows passengers to plan their trips to minimize waiting times. The objective of this research was to develop a dynamic artificial neural network (ANN) model that can provide accurate prediction of bus travel times to give real-time information at a given downstream bus stop using only global positioning system (GPS) data. The ANN model is trained off-line but can be used to provide real-time travel time information. To achieve this, care was taken in selecting a unique set of input-output combinations for prediction. The results obtained from the case study are promising to implement an Advanced Public Transportation System (APTS). The performance of the proposed ANN model was compared with a historical average model under two criteria: prediction accuracy and robustness. It was shown that the ANN outperformed the average approach in both aspects.
Gurmu, Zegeye K & Fan, Wei (.
Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data.
Journal of Public Transportation, 17 (2): 45-65.
Available at: https://scholarcommons.usf.edu/jpt/vol17/iss2/3