Real time bus travel time prediction using k-NN classifier

Abstract

Predicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy.