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.
[1]
Steven I-Jy Chien,et al.
Dynamic Bus Arrival Time Prediction with Artificial Neural Networks
,
2002
.
[2]
Michael J Demetsky,et al.
SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH
,
1994
.
[3]
Michael J Demetsky,et al.
MODELING SCHEDULE DEVIATIONS OF BUSES USING AUTOMATIC VEHICLE-LOCATION DATA AND ARTIFICIAL NEURAL NETWORKS
,
1995
.
[4]
Amer Shalaby,et al.
BUS TRAVEL TIME PREDICTION MODEL FOR DYNAMIC OPERATIONS CONTROL AND PASSENGER INFORMATION SYSTEMS
,
2003
.
[5]
Fei Xu,et al.
Neural Networks as Alternative to Traditional Factor Approach of Annual Average Daily Traffic Estimation from Traffic Counts
,
1999
.
[6]
Dongjoo Park,et al.
Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network
,
1999
.