The emphasis of this research effort was on using automatic vehicle location (AVL) and Automatic Passenger Counter (APC) dynamic data to develop a bus travel time model capable of providing real time information on bus arrival and departure times to passengers and to transit controllers for the application of proactive control strategies. The developed model is comprised of two Kalman filter algorithms for the prediction of running times and dwell times alternately in an integrated framework. The AVL and APC data used were obtained for a specific bus route in downtown Toronto. The performance of the prediction model was tested using "hold out" data and other data from a microsimulation model representing different scenarios of bus operation along the investigated route using the VISSIM microsimulation software package. The Kalman filter-based model outperformed other conventional models in terms of accuracy, demonstrating the dynamic ability to update itself based on new data that reflected the changing characteristics of the transit operating environment.
Abstract