Grid-based Traffic Vulnerability Analysis by Using Betweenness Centrality
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Grid-based Traffic Vulnerability Analysis by Using Betweenness Centrality Seungkwon Jung and Seungwoon Lee International Center for Urban Water Hydroinformatics Research & Innovation, Incheon 21999, Korea
Okyu Kwon National Institute for Mathematical Sciences, Daejeon 34047, Korea
Byungsik Kim∗ Department of Urban Environment & Disaster Management School of Disaster Prevention, Kangwon National University, Samcheok 25931, Korea (Received 24 June 2020; revised 14 July 2020; accepted 23 July 2020) We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the city of Seoul, which is the capital of South Korea with a population of about ten million. Based on the actual road network in Seoul, we construct an abstract road network using 1 km × 1 km grid cells. The link weight of the abstract road network is re-adjusted considering traffic volume measured at several survey points. On the modified abstract road network, we evaluate the traffic vulnerability by calculating a network measure of betweenness centrality for every single grid cell. This study analyzes traffic impacts caused by road dysfunction due to heavy rainfall in urban areas. Keywords: Vulnerability, Road network, Betweenness centrality, Heavy rainfall, Road impact DOI: 10.3938/jkps.77.538
I. INTRODUCTION Until now, weather forecasts have provided quantitative data such as the amount of precipitation, the temperature, fog, and the number of sunshine hours, however, information on how weather disasters affect real life has been limited. In a 2015 report, the World Meteorological Organization reported that a lack of understanding of weather caused worldwide human and property losses, despite timely proper forecasts and news reports. If the impact of meteorological disasters on society and human life, along with the traditional weather forecasts for meteorological phenomena are to be provided, the those impact forecasts must provide precautions that need to be taken against damages and behaviors. Increased torrential rain due to climate change causes not only flooding, mudslides and property damage, but also traffic congestion and isolation. In the road section, drivers are forced to enter danger zones became they are not aware of the disaster information, so damage spreads over time. Road systems are critical infrastructures that provide significant convenience to the population of modern society. Society relies on road systems not only for daily mobility of people and transport of goods, but also as a life∗ E-mail:
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pISSN:0374-4884/eISSN:1976-8524
line for recovery and restoration when other infrastructure systems fail. Therefore, analyzing and forecasting the predicted vulnerabilities of road transportation systems following a disaster will help people develop strategies to minimize the damage. Network vulnerability analyses enable the development of strategies to mitigate the impacts of natural disasters [1]. Since Berdica [2] reviewed the road vulnerabili
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