Comparison of Model Based and Machine Learning Approaches for

The provision of accurate bus arrival information is critical to encourage more people to use public transport and alleviate traffic congestion. Developing a prediction scheme for bus travel times can provide such information. Prediction schemes can be data driven or may use a mathematical model that is usually less data intensive. This paper compares the performance of two methods – one being the data driven Artificial Neural Network (ANN) method and the other being the model based Kalman filter method, with regards to predicting bus travel time. The performances of both methods are evaluated using data collected from the field. It was found that the ANN based method performed slightly better in the presence of a large database but the Kalman filter method will be more advantageous when such a database is not available.

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