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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KeAi Communications Co., Ltd.
2015-10-01
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Series: | Journal of Traffic and Transportation Engineering (English ed. Online) |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2095756415000653 |
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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 |
work_keys_str_mv |
AT mansourehjeihani trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation AT petronellajames trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation AT anthonyasaka trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation AT anamardeshiri trafficrecoverytimeestimationunderdifferentflowregimesintrafficsimulation |
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