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A spatial queuing model for the emergency vehicle districting and location problem

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  • Geroliminis, Nikolas
  • Karlaftis, Matthew G.
  • Skabardonis, Alexander

Abstract

Emergency response systems in urban areas should be located to ensure adequate coverage and rapid response time. We develop a model for locating emergency vehicles on urban networks considering both spatial and temporal demand characteristics such as the probability that a server is not available when required. We also consider that service rates are not identical but may vary among servers and are dependent upon incident characteristics; corresponding districting and dispatching problems are also integrated in the location model. The model is applied using real data for locating freeway service patrol vehicles and results are compared with existing coverage and median models. Results show improvements in the mean response time particularly in cases of high demand for intervention when compared to 'traditional' models.

Suggested Citation

  • Geroliminis, Nikolas & Karlaftis, Matthew G. & Skabardonis, Alexander, 2009. "A spatial queuing model for the emergency vehicle districting and location problem," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 798-811, August.
  • Handle: RePEc:eee:transb:v:43:y:2009:i:7:p:798-811
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    References listed on IDEAS

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    8. Skabardonis, Alexander & Petty, Karl & Varaiya, Pravin & Bertini, Robert, 1998. "Evaluation Of The Freeway Service Patrol ( F S P ) In Los Angeles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3920p806, Institute of Transportation Studies, UC Berkeley.
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    Citations

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    Cited by:

    1. Iannoni, Ana Paula & Chiyoshi, Fernando & Morabito, Reinaldo, 2015. "A spatially distributed queuing model considering dispatching policies with server reservation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 49-66.
    2. Bell, Michael G.H. & Fonzone, Achille & Polyzoni, Chrisanthi, 2014. "Depot location in degradable transport networks," Transportation Research Part B: Methodological, Elsevier, vol. 66(C), pages 148-161.
    3. Boyac─▒, Burak & Geroliminis, Nikolas, 2015. "Approximation methods for large-scale spatial queueing systems," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 151-181.
    4. Iannoni, Ana Paula & Morabito, Reinaldo & Saydam, Cem, 2011. "Optimizing large-scale emergency medical system operations on highways using the hypercube queuing model," Socio-Economic Planning Sciences, Elsevier, vol. 45(3), pages 105-117, September.
    5. Ai, Yun-fei & Lu, Jing & Zhang, Li-li, 2015. "The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 170-188.
    6. repec:eee:transe:v:103:y:2017:i:c:p:143-157 is not listed on IDEAS
    7. Geroliminis, Nikolas & Kepaptsoglou, Konstantinos & Karlaftis, Matthew G., 2011. "A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network," European Journal of Operational Research, Elsevier, vol. 210(2), pages 287-300, April.
    8. Chen, Albert Y. & Yu, Ting-Yi, 2016. "Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 408-423.
    9. Su, Qiang & Luo, Qinyi & Huang, Samuel H., 2015. "Cost-effective analyses for emergency medical services deployment: A case study in Shanghai," International Journal of Production Economics, Elsevier, vol. 163(C), pages 112-123.

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