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http://dx.doi.org/10.12815/kits.2021.20.6.100

Dentifying and Clustering the Flood Impacted Areas for Strategic Information Provision  

Park, Eun Mi (Datawiz Inc, Deepartment of Urban Engineering Mokwon University)
Bilal, Muhammad (Business Intelligence team, DP World, Inc)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.6, 2021 , pp. 100-109 More about this Journal
Abstract
Flooding usually brings in disruptions and aggravated congestions to the roadway network. Hence, right information should be provided to road users to avoid the flood-impacted areas and for city officials to recover the network. However, the information about individual link congestion may not be conveyed to roadway users and city officials because too many links are congested at the same time. Therefore, more significant information may be desired, especially in a disastrous situation. This information may include 1) which places to avoid during flooding 2) which places are feasible to drive avoiding flooding. Hence, this paper aims to develop a framework to identify the flood-impacted areas in a roadway network and their criticality. Various impacted clusters and their spatiotemporal properties were identified with field data. From this data, roadway users can reroute their trips, and city officials can take the right actions to recover the affected areas. The information resulting from the developed framework would be significant enough for roadway users and city officials to cope with flooding.
Keywords
Congested links; DBSCAN; Disruptive network; Flood Impacted Areas; Link clustering;
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