Abstract
Predicting and providing information about bus arrival time to passengers accurately is a very important aspect of advanced public transportation systems (APTS), a major functional area of intelligent transportation systems. However, the information provided to passengers should be reliable. The reliability of such information provided to passengers depends on the prediction method used, which in turns depends on the input data used in the method. This means that identifying the most significant/appropriate input data and using them in the method are important. So, in the present study, travel time pattern analysis was carried out to find the most significant inputs by performing statistical tests for each day of the week separately. Also, a model-based Kalman filtering algorithm was developed to predict bus travel time by using the identified patterns effectively based on temporal discretization under heterogeneous traffic conditions. The performance of the proposed algorithm showed a clear improvement in prediction accuracy when compared with a prediction method using space discretization.
Published Version
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