Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data

Climate change and natural hazards pose great threats to road transport systems which are 'lifelines' of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical ap...

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Vydáno v:International journal of geographical information science : IJGIS Ročník 34; číslo 12; s. 2434 - 2450
Hlavní autoři: Qiang, Yi, Xu, Jinwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.12.2020
Taylor & Francis LLC
Témata:
Accessibility
Case studies
Climate change
crowdsourced data
Crowdsourcing
Disaster management
Economic conditions
Economic models
Emergency preparedness
empirical study
Hazard assessment
Metropolitan areas
natural hazards
Resilience
Road transportation
Roads & highways
Spatial variations
time-geography
Traffic information
Transportation networks
Transportation planning
Transportation systems
Travel time
Winter storms
ISSN:1365-8816, 1362-3087, 1365-8824
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Shrnutí:Climate change and natural hazards pose great threats to road transport systems which are 'lifelines' of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical approach to evaluate road network resilience using crowdsourced traffic data in Google Maps. Based on the conceptualization of resilience and the Hansen accessibility index, resilience of road network is measured from accumulated accessibility reduction over time during a hazard. The utility of this approach is demonstrated in a case study of the Cleveland metropolitan area (Ohio) in Winter Storm Harper. The results reveal strong spatial variations of the disturbance and recovery rate of road network performance during the hazard. The major findings of the case study are: (1) longer distance travels have higher increasing ratios of travel time during the hazard; (2) communities with low accessibility at the normal condition have lower road network resilience; (3) spatial clusters of low resilience are identified, including communities with low socio-economic capacities. The introduced approach provides ground-truth validation for existing quantitative models and supports disaster management and transportation planning to reduce hazard impacts on road network.
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ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2019.1694681