BUS TRAVEL TIME PREDICTION MODEL FOR DYNAMIC OPERATIONS CONTROL AND PASSENGER INFORMATION SYSTEMS

Abstract

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.

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