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

ABSTRACT

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.

KEYWORDS:

• Resilience
• accessibility
• crowdsourced data
• empirical study
• natural hazards
• time-geography

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the identifier(s) [doi.10.6084/m9.figshare.10279295.v1].

Additional information

Funding

This article is based on work supported by two research grants from the U.S. National Science Foundation: one under the Coastlines and People (CoPe) Program (Award No. 1940091) and the other under the Methodology, Measurement & Statistics (MMS) Program (Award No. 1853866). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

Notes on contributors

Yi Qiang

Yi Qiang is an Assistant Professor in the Department of Geography and Environment at the University of Hawai'i at Manoa. He holds a Ph.D. in Geography from Ghent University, Belgium. His research areas include in space-time modeling, visual analytics, geocomputation, disaster risk assessment, dynamic modeling of coupled natural and human (CNH) systems.

Jinwen Xu

Jinwen Xu is a Ph.D. student in the Department of Geography and Environment, University of Hawaii at Manoa. He holds a master degree in Urban and Environmental Planning from Arizona State University. He is interested in Geographical Information Science, spatial analysis, social media big data, and natural disaster.