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Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method
Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the cra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295271/ https://www.ncbi.nlm.nih.gov/pubmed/28036009 http://dx.doi.org/10.3390/ijerph14010020 |
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author | Chen, Can Li, Tienan Sun, Jian Chen, Feng |
author_facet | Chen, Can Li, Tienan Sun, Jian Chen, Feng |
author_sort | Chen, Can |
collection | PubMed |
description | Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the crash occurrence probability for all exposure vehicles is estimated based on the empirical Bayesian method. As for the consequences of crashes, crashes may not only cause direct losses (e.g., occupant injuries and property damages) but also result in indirect losses. The indirect losses are expressed by the extra delays calculated using the deterministic queuing diagram method. The direct losses and indirect losses are uniformly monetized to be considered as the consequences of this risk. The potential costs of crashes, as a criterion to rank high-risk sites, can be explicitly expressed as the sum of the crash probability for all passing vehicles and the corresponding consequences of crashes. A case study on the urban expressways of Shanghai is presented. The results show that the new QRA method for HSID enables the identification of a set of high-risk sites that truly reveal the potential crash costs to society. |
format | Online Article Text |
id | pubmed-5295271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-52952712017-02-07 Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method Chen, Can Li, Tienan Sun, Jian Chen, Feng Int J Environ Res Public Health Article Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the crash occurrence probability for all exposure vehicles is estimated based on the empirical Bayesian method. As for the consequences of crashes, crashes may not only cause direct losses (e.g., occupant injuries and property damages) but also result in indirect losses. The indirect losses are expressed by the extra delays calculated using the deterministic queuing diagram method. The direct losses and indirect losses are uniformly monetized to be considered as the consequences of this risk. The potential costs of crashes, as a criterion to rank high-risk sites, can be explicitly expressed as the sum of the crash probability for all passing vehicles and the corresponding consequences of crashes. A case study on the urban expressways of Shanghai is presented. The results show that the new QRA method for HSID enables the identification of a set of high-risk sites that truly reveal the potential crash costs to society. MDPI 2016-12-27 2017-01 /pmc/articles/PMC5295271/ /pubmed/28036009 http://dx.doi.org/10.3390/ijerph14010020 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Can Li, Tienan Sun, Jian Chen, Feng Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title | Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title_full | Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title_fullStr | Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title_full_unstemmed | Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title_short | Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method |
title_sort | hotspot identification for shanghai expressways using the quantitative risk assessment method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295271/ https://www.ncbi.nlm.nih.gov/pubmed/28036009 http://dx.doi.org/10.3390/ijerph14010020 |
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