Accurate travel time information of public transport will help operators to effectively manage and implement their operating strategies and help passengers by reducing the uncertainty about arrival time of buses at bus stops. The reliability of such information provided to passengers greatly depends on the prediction technique used, which in turn, depends on the quality of the input used in the prediction technique. In other words, identifying and using the correct input in the appropriate prediction technique is important. Prediction techniques can be data driven or less data intensive. The first part of this paper presents a systematic statistical approach for identifying the significant inputs for travel time prediction. The second part compares the performance of two popular prediction methods, one being the data driven Artificial Neural Network (ANN) method and the other being a model based approach using the Kalman Filter Technique (KFT) that is less data intensive, to predict bus travel time. The performances of both methods were evaluated using the data obtained from the field. It was found that ANN outperformed KFT in terms of prediction error, if a good database is available, and in case of limited data availability, KFT will be more advantag eous.