Automatic Vehicle Location (AVL) systems are increasingly being used by transit agencies for the real time monitoring of their vehicles. AVL systems can be used to improve the service given to passengers by using informat8ion on the current position of buses to maintain headways or increase reliability by improved operational control and provide an estimate of as to the arrival time of the next bus at the stop. In real-time bus tracking systems, some positional uncertainty is usually associated with eh location of buses in service that are tracked using a locational device such as a Global Positioning System (GPS) receiver. Rather than raw coordinates, the location is usually better understood in terms of the landmarks along the route, particularly the named stop. Three prediction models have been implemented to estimate the location of vehicles on bus routes. Analysis indicates that one based on mining historical data for patterns gives more accurate results than regression and Kalman filter models when travel is disrupted by one-off events.