Prediction Model of Bus Arrival Time for Real-Time Applications

Document Type

Journal Article

Publication Date


Subject Area

operations - traffic, infrastructure - vehicle, planning - history, planning - signage/information, ridership - commuting, policy - congestion, mode - bus


Vehicle locating systems, Travel time, Traffic congestion, Schedule maintenance, Regression analysis, Regression, Real time information, Neural networks, Mathematical prediction, Mathematical models, Journey time, Intracity bus transportation, Houston (Texas), History, Gridlock (Traffic), Dwell time, Bus transit, AVL, Automatic vehicle location, Automatic location systems, Artificial neural networks, Arrival time (Bus transit), ANNs (Artificial neural networks), Advanced traveler information systems, Accuracy


Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.