Traffic density on corridors subject to incidents: models for long-term congestion management
| Autor: | Pedro Cesar Lopes Gerum, Melike Baykal-Gürsoy, Andrew Reed Benton |
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| Rok vydání: | 2019 |
| Předmět: |
Estimation
Queueing theory Service (systems architecture) Operations research Computer science 030503 health policy & services 05 social sciences Poison control 050109 social psychology Transportation Management Science and Operations Research Term (time) 03 medical and health sciences Traffic congestion Modeling and Simulation Probability mass function Thunderstorm 0501 psychology and cognitive sciences 0305 other medical science |
| Zdroj: | EURO Journal on Transportation and Logistics. 8:795-831 |
| ISSN: | 2192-4376 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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