APPLICATION OF THE KALMAN FILTER TO THE ARTERIAL TRAVEL TIME PREDICTION: A SPECIAL EVENT CASE STUDY

Abstract

A sudden traffic surge immediately after special events (e.g., conventions, concerts) can create substantial traffic congestion in the area where the events are held. It is desirable that the special events-related traffic flow be monitored and measured so that available methods (e.g., a temporary traffic signal timing adjustment and coordination) can be used to alleviate congestion. The travel time variable is a good operational measure of effectiveness of traffic systems and can be used to detect incidents and quantify congestion. Effective prediction of travel times is crucial to many advanced traveller information and transportation management systems. This paper focuses on the arterial travel time prediction using the Kalman filtering and estimation technique, and a special event is selected as our case study. The global positioning system (GPS) test vehicle technique is used to collect after-events travel time data. Based on the field data collected, a discrete-time Kalman filter is implemented and applied to predict travel time exiting the area under study. The performance is assessed and its effectiveness at the test site is investigated. The available techniques to further improve the accuracy of the prediction error are also discussed. Although this paper presents a case study, the results can be easily applied to large-scale special events held in other metropolitan areas.