Traffic density on corridors subject to incidents: models for long-term congestion management

Pedro Cesar Lopes Gerum (), Andrew Reed Benton () and Melike Baykal-Gürsoy ()
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Pedro Cesar Lopes Gerum: Rutgers University
Andrew Reed Benton: Rutgers University
Melike Baykal-Gürsoy: Rutgers University

EURO Journal on Transportation and Logistics, 2019, vol. 8, issue 5, No 13, 795-831

Abstract: Abstract The purpose of this research is to provide a faster and more efficient method to determine traffic density behavior for long-term congestion management using minimal statistical information. Applications include road work, road improvements, and route choice. To this end, this paper adapts and generalizes two analytical models (for non-peak and peak hours) for the probability mass function of traffic density for a major highway. It then validates the model against real data. The studied corridor has a total of 36 sensors, 18 in each direction, and the traffic experiences randomly occurring service deterioration due to accidents and inclement weather such as snow and thunderstorms. We base the models on queuing theory, and we compare the fundamental diagram with the data. This paper supports the validity of the models for each traffic condition under certain assumptions on the distributional properties of the associated random parameters. It discusses why these assumptions are needed and how they are determined. Furthermore, once the models are validated, different scenarios are presented to demonstrate traffic congestion behavior under various deterioration levels, as well as the estimation of traffic breakdown. These models, which account for non-recurrent congestion, can improve decision making without the need for extensive datasets or time-consuming simulations.

Keywords: Random queues; Traffic density; Recurrent congestion; Non-recurrent congestion; Traffic breakdown (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13676-019-00149-2

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