Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems

Automatic Vehicle Location (AVL) and Automatic Passengers Counters (APC) systems have been increasingly implemented by transit agencies for the real time monitoring of transit vehicles and automatic counting of passengers boarding and alighting at bus stops. As a result, a vast amount of potentially online data related to transit operation could be obtained from these systems. The emphasis of this research effort was on using AVL and APC dynamic data to develop a bus travel time model capable of providing real time information on bus arrival times to passengers, via traveler information services and to transit controllers for the application of proactive control strategies. The developed model is based on 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 developed prediction model was tested using “hold out” data and other data from microsimulation representing different scenarios of bus operation along the investigated route using the “VISSIM” microsimulation software package. The Kalman Filter algorithm outperformed all other developed 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. A user-interactive system was developed to provide continuous information on the expected arrival time of buses at downstream stops, hence the expected deviations from schedule. The system enables the user to assess in real time transit stop-based control actions to avoid such deviations before their occurrence, hence allowing for proactive control, as opposed to the traditional reactive control, which attempts to recover the schedule after deviations occur.