Estimation of road network reliability on resiliency
Past research shows that a better understanding of reliability and identification of ways to improve it can help a system's response to a disaster, leading to increased transportation system resilience. This study focuses on the quantification of improved reliability, which reduces the time of...
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| Format: | article |
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2015
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| Online Access: | http://hdl.handle.net/10725/3091 http://dx.doi.org/10.1016/j.ijdrr.2015.10.005 http://www.sciencedirect.com/science/article/pii/S2212420915301151 |
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| _version_ | 1864513460122943488 |
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| author | Soltani-Sobh, Ali |
| author2 | Heaslip, Kevin El Khoury, John |
| author2_role | author author |
| author_facet | Soltani-Sobh, Ali Heaslip, Kevin El Khoury, John |
| author_role | author |
| dc.creator.none.fl_str_mv | Soltani-Sobh, Ali Heaslip, Kevin El Khoury, John |
| dc.date.none.fl_str_mv | 2015 2016-02-16T11:55:03Z 2016-02-16T11:55:03Z 2017-04-11 |
| dc.identifier.none.fl_str_mv | 2212-4209 http://hdl.handle.net/10725/3091 http://dx.doi.org/10.1016/j.ijdrr.2015.10.005 Soltani-Sobh, A., Heaslip, K., & El Khoury, J. (2015). Estimation of road network reliability on resiliency: An uncertain based model. International Journal of Disaster Risk Reduction, 14, 536-544. http://www.sciencedirect.com/science/article/pii/S2212420915301151 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International Journal of Disaster Risk Reduction |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Estimation of road network reliability on resiliency An uncertain based model |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Past research shows that a better understanding of reliability and identification of ways to improve it can help a system's response to a disaster, leading to increased transportation system resilience. This study focuses on the quantification of improved reliability, which reduces the time of annealing and recovery post uncertain disruption. A reliability model is presented by using three performance functions that estimate the total travel time, flow, and consumer surplus. Network reliability is estimated by considering uncertainties in link-capacity and demand sensitivity with respect to travel time, following a disaster. Sensitivity and uncertainty analyses are conducted to identify the most crucial links in the transportation network, for which resistance should be increased to mitigate disaster risk. The simulation results show that the model provides accurate predictions of the system performance, and that a reliability model that accounts for uncertainty yields better results than a deterministic (no uncertainty) model. With higher accuracy models, planners are able to make informed decisions in disaster mitigation planning. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_7a5b930ac22ce87890be87a6ace809ff |
| identifier_str_mv | 2212-4209 Soltani-Sobh, A., Heaslip, K., & El Khoury, J. (2015). Estimation of road network reliability on resiliency: An uncertain based model. International Journal of Disaster Risk Reduction, 14, 536-544. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/3091 |
| publishDate | 2015 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Estimation of road network reliability on resiliencyAn uncertain based modelSoltani-Sobh, AliHeaslip, KevinEl Khoury, JohnPast research shows that a better understanding of reliability and identification of ways to improve it can help a system's response to a disaster, leading to increased transportation system resilience. This study focuses on the quantification of improved reliability, which reduces the time of annealing and recovery post uncertain disruption. A reliability model is presented by using three performance functions that estimate the total travel time, flow, and consumer surplus. Network reliability is estimated by considering uncertainties in link-capacity and demand sensitivity with respect to travel time, following a disaster. Sensitivity and uncertainty analyses are conducted to identify the most crucial links in the transportation network, for which resistance should be increased to mitigate disaster risk. The simulation results show that the model provides accurate predictions of the system performance, and that a reliability model that accounts for uncertainty yields better results than a deterministic (no uncertainty) model. With higher accuracy models, planners are able to make informed decisions in disaster mitigation planning.PublishedN/A2016-02-16T11:55:03Z2016-02-16T11:55:03Z20152017-04-11Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2212-4209http://hdl.handle.net/10725/3091http://dx.doi.org/10.1016/j.ijdrr.2015.10.005Soltani-Sobh, A., Heaslip, K., & El Khoury, J. (2015). Estimation of road network reliability on resiliency: An uncertain based model. International Journal of Disaster Risk Reduction, 14, 536-544.http://www.sciencedirect.com/science/article/pii/S2212420915301151enInternational Journal of Disaster Risk Reductioninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/30912021-03-19T09:59:50Z |
| spellingShingle | Estimation of road network reliability on resiliency Soltani-Sobh, Ali |
| status_str | publishedVersion |
| title | Estimation of road network reliability on resiliency |
| title_full | Estimation of road network reliability on resiliency |
| title_fullStr | Estimation of road network reliability on resiliency |
| title_full_unstemmed | Estimation of road network reliability on resiliency |
| title_short | Estimation of road network reliability on resiliency |
| title_sort | Estimation of road network reliability on resiliency |
| url | http://hdl.handle.net/10725/3091 http://dx.doi.org/10.1016/j.ijdrr.2015.10.005 http://www.sciencedirect.com/science/article/pii/S2212420915301151 |