Citation

Laflamme EM, Ossenbruggen PJ. J. Traffic Transp. Eng. Engl. Ed. 2017; 4(1): 31-40.

Copyright

(Copyright © 2017, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2016.08.004

PMID

unavailable

Abstract

This work uses regression models to analyze two characteristics of recurrent congestion: breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichotomous categorical variable for time-of-day (AM- or PM-rush hours) is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time-of-day (AM- or PM-rush hours) and day-of-the-week (Monday-Thursday or Friday) are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on I-93 in Salem, NH, over a period of 9 months.

RESULTS from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM- and PM-rush periods.

RESULTS from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the-week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.

Language: en