Traffic recovery time estimation under different flow regimes in traffic simulation

Incident occurrence and recovery are critical to the smooth and efficient operations of freeways. Although many studies have been performed on incident detection, clearance, and management, travelers and traffic managers are unable to accurately predict the length of time required for full traffic r...

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Main Authors: Mansoureh Jeihani, Petronella James, Anthony A. Saka, Anam Ardeshiri
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2015-10-01
Series:Journal of Traffic and Transportation Engineering (English ed. Online)
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095756415000653
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spelling doaj-4cf869428ef148dcafe6fd1910522f6d2021-02-02T03:01:28ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642015-10-012529130010.1016/j.jtte.2015.08.001Traffic recovery time estimation under different flow regimes in traffic simulationMansoureh JeihaniPetronella JamesAnthony A. SakaAnam ArdeshiriIncident occurrence and recovery are critical to the smooth and efficient operations of freeways. Although many studies have been performed on incident detection, clearance, and management, travelers and traffic managers are unable to accurately predict the length of time required for full traffic recovery after an incident occurs. This is because there are no practical studies available to estimate post-incident recovery time. This paper estimates post-incident traffic recovery time along an urban freeway using traffic simulation and compares the simulation results with shockwave theory calculations. The simulation model is calibrated and validated using a freeway segment in Baltimore, MD. The model explores different flow regimes (traffic intensity) and incident duration for different incident severity, and their effects on recovery time. A total of 726 simulations are completed using VISSIM software. Finally, the impact of congestion and incident delay on the highway network is quantified by a regression formula to predict traffic recovery time. The developed regression model predicts post-incident traffic recovery time based on traffic intensity, incident duration, and incident severity (ratio of lanes closure). In addition, three regression models are developed for different flow regimes of near-capacity, moderate, and low-traffic intensity. The model is validated by collected field data on two different urban freeways.http://www.sciencedirect.com/science/article/pii/S2095756415000653Traffic simulationIncident delayTraffic safetyNon-recurring incidentShockwave analysisRegression analysis
collection DOAJ
language English
format Article
sources DOAJ
author Mansoureh Jeihani
Petronella James
Anthony A. Saka
Anam Ardeshiri
spellingShingle Mansoureh Jeihani
Petronella James
Anthony A. Saka
Anam Ardeshiri
Traffic recovery time estimation under different flow regimes in traffic simulation
Journal of Traffic and Transportation Engineering (English ed. Online)
Traffic simulation
Incident delay
Traffic safety
Non-recurring incident
Shockwave analysis
Regression analysis
author_facet Mansoureh Jeihani
Petronella James
Anthony A. Saka
Anam Ardeshiri
author_sort Mansoureh Jeihani
title Traffic recovery time estimation under different flow regimes in traffic simulation
title_short Traffic recovery time estimation under different flow regimes in traffic simulation
title_full Traffic recovery time estimation under different flow regimes in traffic simulation
title_fullStr Traffic recovery time estimation under different flow regimes in traffic simulation
title_full_unstemmed Traffic recovery time estimation under different flow regimes in traffic simulation
title_sort traffic recovery time estimation under different flow regimes in traffic simulation
publisher KeAi Communications Co., Ltd.
series Journal of Traffic and Transportation Engineering (English ed. Online)
issn 2095-7564
publishDate 2015-10-01
description Incident occurrence and recovery are critical to the smooth and efficient operations of freeways. Although many studies have been performed on incident detection, clearance, and management, travelers and traffic managers are unable to accurately predict the length of time required for full traffic recovery after an incident occurs. This is because there are no practical studies available to estimate post-incident recovery time. This paper estimates post-incident traffic recovery time along an urban freeway using traffic simulation and compares the simulation results with shockwave theory calculations. The simulation model is calibrated and validated using a freeway segment in Baltimore, MD. The model explores different flow regimes (traffic intensity) and incident duration for different incident severity, and their effects on recovery time. A total of 726 simulations are completed using VISSIM software. Finally, the impact of congestion and incident delay on the highway network is quantified by a regression formula to predict traffic recovery time. The developed regression model predicts post-incident traffic recovery time based on traffic intensity, incident duration, and incident severity (ratio of lanes closure). In addition, three regression models are developed for different flow regimes of near-capacity, moderate, and low-traffic intensity. The model is validated by collected field data on two different urban freeways.
topic Traffic simulation
Incident delay
Traffic safety
Non-recurring incident
Shockwave analysis
Regression analysis
url http://www.sciencedirect.com/science/article/pii/S2095756415000653
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AT anthonyasaka trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation
AT anamardeshiri trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation
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