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Erratum: A Stochastic Modeling Approach to Real–Time Prediction of Queue Overflows

Institute of Traffic and Transportation, National Chiao Tung University, 4F, 114 Chung Hsiao W. Road, Sec. 1, Taipei, Taiwan 10012
jbsheu{at}mail.netu.edu.tw

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management System (ATMS). Conventional queue prediction methods, however, are limited to incident–free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue–overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue–overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete–time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue–overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane–changing maneuvers during queue–overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real–time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real–time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue–overflow occurrence, but also in developing related advanced technologies such as real–time road traffic congestion control and management systems.

History: Received: January 2001; revised: August 2001; accepted: October 2001.