TY - JOUR
T1 - Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control
AU - Xu, Gangyan
AU - Wang, Junwei
AU - Huang, George Q.
AU - Chen, Chun Hsien
N1 - Funding Information:
Manuscript received January 18, 2016; revised January 12, 2017 and July 20, 2017; accepted August 13, 2017. Date of publication September 11, 2017; date of current version May 29, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 71571156, in part by the Research Grants Council of Hong Kong, China under Project T32-101/15-R, in part by the University of Hong Kong through the Seed Funding Programme for basic research under Grant 201511159252 and Grant 201611159213, in part by the open project funded by State Key Laboratory of Synthetical Automation for Process Industries under Grant PAL-N201505, in part by the Singapore Maritime Institute Research under Project SMI-2014-MA-06, in part by the Zhejiang Provincial, in part by Hangzhou Municipal, and in part by the Lin’an City Governments. The Associate Editor for this paper was Chelsea C. White. (Corresponding author: Gangyan Xu.) G. Xu and C.-H. Chen are with the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
KW - cloud asset
KW - data-driven applications
KW - emergency fleet management
KW - Resilience engineering
KW - urban flood control
UR - http://www.scopus.com/inward/record.url?scp=85047094764&partnerID=8YFLogxK
U2 - 10.1109/TITS.2017.2740438
DO - 10.1109/TITS.2017.2740438
M3 - Journal article
AN - SCOPUS:85047094764
SN - 1524-9050
VL - 19
SP - 1827
EP - 1838
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 6
ER -
