Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control

Gangyan Xu, Junwei Wang, George Q. Huang, Chun Hsien Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)

Abstract

Emergency fleet management has become one of the determinant success factors for post-disaster responses in urban flood control. However, it is challenging as multiple types of emergency vehicles are involved, and its performance is frequently threatened by the fluctuation of rescue demands and fleet capacity. Aiming at coping with the imbalances between rescue demands and vehicle supplies, and maintaining required service level of fleet management after flood occurs, this paper proposes a data-driven resilient fleet management solution under the context of cloud asset-enabled urban flood control. First, the problem of resilient fleet management is quantitatively defined, and then a data-driven dynamic management mechanism is proposed, which is highly effective on realizing resilient fleet management. Furthermore, considering the cooperation among different types of emergency vehicles, a greedy-based algorithm is proposed for resilient vehicle dispatching based on real-time scenarios. Finally, a simulation case is also conducted to verify the effectiveness and performance of the proposed solution.

Original languageEnglish
Pages (from-to)1827-1838
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume19
Issue number6
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

Keywords

  • cloud asset
  • data-driven applications
  • emergency fleet management
  • Resilience engineering
  • urban flood control

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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