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Location selection for ambulance stations: a data-driven approach

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Zhiguo GongAuthors Info & Claims

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 85, Pages 1 - 4

https://doi.org/10.1145/2820783.2820876

Published: 03 November 2015 Publication History

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Abstract

Emergency medical service provides a variety of services for those in need of emergency care. One of the major challenges encountered by emergency service providers is selecting the appropriate locations for ambulance stations. Prior works measure spatial proximity under Euclidean space or static road network. In this paper, we focus on locating the ambulance stations by using the real traffic information so as to minimize the average travel-time to reach the emergency requests. To this end, we estimate the travel-time of road segments using real GPS trajectories and propose an efficient PAM-based refinement for the location problem. We conduct extensive experimental evaluations using real emergency requests collected from Tianjin, and the result shows that the proposed solution can reduce the travel-time to reach the emergency requests by 29.9% when compared to the original locations of ambulance stations.

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Li XZhao XWang ZDuan YZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimal Transport Enhanced Cross-City Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657757(1441-1451)Online publication date: 10-Jul-2024
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Zhang YGu SLi QZeng H(2024)EVCSeer: An Exploratory Study on Electric Vehicle Charging Stations Utilization via Visual AnalyticsIEEE Computer Graphics and Applications10.1109/MCG.2024.339645144:3(54-68)Online publication date: 1-May-2024
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Index Terms

Location selection for ambulance stations: a data-driven approach
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2015

646 pages

ISBN:9781450339674

DOI:10.1145/2820783

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Yan Huang
University of North Texas
,
Program Chairs:
Michael Gertz
Heidelberg University, Germany
,
Matthias Renz
LMU Munich, Germany
,
Jagan Sankaranarayanan
NEC Labs

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Published: 03 November 2015

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Macau FDCT
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SIGSPATIAL

SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

Washington, Seattle

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SIGSPATIAL '15 Paper Acceptance Rate 38 of 212 submissions, 18%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Li XZhao XWang YLiu YChen CLong CZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)OpenSiteRec: An Open Dataset for Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657875(1483-1493)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657875
Li XZhao XWang ZDuan YZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimal Transport Enhanced Cross-City Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657757(1441-1451)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657757
Zhang YGu SLi QZeng H(2024)EVCSeer: An Exploratory Study on Electric Vehicle Charging Stations Utilization via Visual AnalyticsIEEE Computer Graphics and Applications10.1109/MCG.2024.339645144:3(54-68)Online publication date: 1-May-2024
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Wang HLi HWang MCui J(2023)Toward Balancing the Efficiency and Effectiveness in k-Facility Relocation ProblemACM Transactions on Intelligent Systems and Technology10.1145/358703914:3(1-24)Online publication date: 13-Apr-2023
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Hou CCao BRuan SFan J(2021)TLDS: A Transfer-Learning-Based Delivery Station Location Selection PipelineACM Transactions on Intelligent Systems and Technology10.1145/346908412:4(1-24)Online publication date: 12-Aug-2021
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Wang HLi HWang MCui JDemartini GZuccon GCulpepper JHuang ZTong H(2021)Addressing the Hardness of k-Facility Relocation ProblemProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482411(1919-1928)Online publication date: 26-Oct-2021
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Automated Incident Location Identification for EMS from Ambulance Geospatial Data

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Automated Incident Location Identification for EMS from Ambulance Geospatial Data

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