Modeling Duration of Urban Traffic Congestion
Publication: Journal of Transportation Engineering
Volume 128, Issue 6
Abstract
Research on short-term traffic conditions prediction has been largely concerned with parameters such as flow, occupancy, and speed, ignoring at the same time predictions during congestion, a period when predictions are needed the most. Stemming from the practical need to predict traffic parameters during congested periods, this paper uses the principles of duration modeling to address an important question: given the onset of congestion, how long will it last? As such, the goal of this paper is to propose an approach for estimating the duration of congestion on a given road section and the probability that, given its onset, congestion will end during the following time period. The results indicate that the Loglogistic functional form best describes congestion duration, and that the probability of congestion ending within a specified time period is likely if it has lasted up to approximately 12 min (with a peak at 6 min). Further, it was found that if congestion lasted over 21 min it was probably caused by something external to the traffic system events.
Get full access to this article
View all available purchase options and get full access to this article.
References
Bhat, C. R. (2000). “Duration modeling.” Handbook of transport modelling, D. A. Hensher and K. J. Button, eds., Elsevier Science, London, 91–112.
Cottrell, W. D. (1998). “Estimating the probability of recurring freeway congestion.” Transportation Research Record, 1634, Transportation Research Board, Washington, D.C., 19–27.
Davis, G. A., and Nihan, N. L.(1991). “Nonparametric regression and short-term freeway traffic forecasting.” J. Transp. Eng., 117(2), 178–188.
Hamed, M. M., Al-Masaeid, H. R., and Said, Z. M. B.(1995). “Short-term prediction of traffic volume in urban arterials.” J. Transp. Eng., 121(3), 249–254.
Hensher, D. A., and Mannering, F. L.(1994). “Hazard-based duration models and their application to transport analysis.” Transp. Rev., 14(1), 63–82.
Kalbfleish, J. D., and Prentice, R. L. (1980). The statistical analysis of failure time data, Wiley, New York.
Lancaster, T. (1994). The econometric analysis of transition data, Econometric Society Monographs, Cambridge University Press, Cambridge, U.K.
Park, D., Rilett, L. R., and Han, G.(1999). “Spectral basis neural networks for real-time travel time forecasting.” J. Transp. Eng., 125(6), 515–523.
Sisiopiku, V. P., and Rouphail, N. M. (1995). “Toward the use of detector output for arterial link travel time estimation: A literature review.” Transportation Research Record 1457, Transportation Research Board, Washington, D.C., 158–165.
Smith, B. L., and Demetsky, M. J.(1997). “Traffic flow forecasting: Comparison of modeling approaches.” J. Transp. Eng., 123(4), 261–266.
Stathopoulos, A., and Karlaftis, M. G. (2001a). “Spectral and cross-spectral analysis of urban traffic flows.” Proc., 2001 IEEE Intelligent Transportation Systems Conf., IEEE, New York, 821–826.
Stathopoulos, A., and Karlaftis, M. G. (2001b). “Temporal and spatial variations of real-time traffic data in urban areas.” Transportation Research Record, Transportation Research Board, Washington, D.C., in press.
Stathopoulos, A., and Karlaftis, M. G. (2002). “A multivariate state-space approach for urban traffic flow modeling and prediction.” Trans. Res., in press.
Washington, S., Karlaftis, M. G., and Mannering, F. L. (2002). Statistical and econometric methods for transportation data analysis, Chapman & Hall/CRC, Boca Raton, Fla., in press.
Zhang, H. M.(2000). “Recursive prediction of traffic conditions with neural network models.” J. Transp. Eng., 126(6), 472–481.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 128 • Issue 6 • November 2002
Pages: 587 - 590
Copyright
Copyright © 2002 American Society of Civil Engineers.
History
Received: Jun 26, 2001
Accepted: Jan 31, 2002
Published online: Oct 15, 2002
Published in print: Nov 2002
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.