Pattern identification based bus arrival time prediction

The provision of accurate bus arrival time information is one of the major components in advanced public transportation systems, which many of the metropolitan cities in developing countries are trying to implement to increase the public transit usage. The effectiveness of such a system largely depends on the reliability of the information provided to the public. For such reliable information to be generated, the prediction technique used should be able to make accurate predictions, which in turn depends on the input data used for prediction. The present study is an attempt to explore these two areas, namely the identification of suitable input data by analysing trip-wise, daily and weekly patterns of bus travel times through valid statistical tests and the development of an accurate bus arrival prediction model using a popular time series technique called exponential smoothing. The performance evaluation using 90 actual bus trip data shows that the use of suitable input data into the prediction model yields better results with mean absolute percentage error of 12, and for 77% of the time the deviation of predicted arrival time with respect to actual arrival time is within the user acceptable range of ± 5 min.