Use Of Neural Network Dynamic Algorithms To Predict Bus Travel Times Under Congested Conditions
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Use of Neural Network/dynamic Algorithms to Predict Bus Travel Times Under Congested Conditions
In this study, a dynamic model for predicting bus arrival times is developed using data collected by a real-world Automatic Passenger Counter (APC) system. The model consists of two major elements. The first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of-week, and weather condition. The second one is a Kalman filter based dynamic algorithm to adjust the arrival time prediction using up-to-the-minute bus location (operational) information. Test runs show that the developed model is quite powerful in dealing with variations in bus arrival times along the service route.
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