Research Article
Nov 1980
Time Series Models for Freeway Incident Detection
Authors: Samir A. Ahmed and Allen R. Cook, M.ASCEAuthor Affiliations
Publication: Transportation Engineering Journal of ASCE
Volume 106, Issue 6
Abstract
An autoregressive integrated moving average model of the form, ARIMA (0,1,3), is presented for describing the stochastic and dynamic behavior of freeway traffic volume and occupancy observations. The model is applied to the detection of freeway capacity-reducing incidents through the sudden and pulsed changes they generate in traffic stream time series data. An incident is detected if the observed value of traffic occupancy lies outside the probability limits constructed two standard errors away from the corresponding point forecast. The developed ARIMA occupancy algorithm has been found superior to the exponential and the California algorithms in terms of detection rate, false alarm rate, and mean time lag to detection. The analysis is based on surveillance data recorded at the Los Angeles, Minneapolis, and Detroit freeway systems during the afternoon peak periods.
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Published In
Transportation Engineering Journal of ASCE
Volume 106 • Issue 6 • November 1980
Pages: 731 - 745
Copyright
© 1980 American Society of Civil Engineers.
History
Published in print: Nov 1980
Published online: Feb 5, 2021
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Authors
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Samir A. Ahmed
Asst. Prof., Dept. of Civ. Engrg., Univ. of Hawaii, Honolulu, Hawaii
Allen R. Cook, M.ASCE
Assoc. Prof., School of Civ. Engrg. and Environmental Science, Univ. of Oklahoma, Norman, Okla.
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.