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Optimizing emergency services for road safety using a decomposition method: a case study of Delhi

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  • Shayesta Wajid

    (Indian Institute of Technology Delhi)

  • N. Nezamuddin

    (Indian Institute of Technology Delhi)

Abstract

Road traffic crashes are among the top ten leading causes of deaths in India and emergency medical services play a vital role in reducing fatality after a road crash. Being one of the largest metropolitan areas in the world, Delhi has thousands of fatal crashes each year which makes it challenging to achieve a timely response to crashes. In this study, we demonstrated the efficiency of a district-level decomposition approach for solving various types of ambulance optimization problems (for coverage, for survivability, and by incorporating travel time uncertainty) for the city of Delhi, India. Three scenarios were compared to optimize the ambulance locations: the first scenario (S1) considered only the existing ambulance sites; the second (S2) and third (S3) scenarios considered a larger set of potential ambulance sites and relocated ambulances within districts and across districts, respectively. Results showed that the city-level coverage increased by at least 10% with the reallocation of ambulances across districts. For the coverage and survivability maximization models, the decomposition approach was between two to six times faster than the non-decomposition approach. The decomposition approach was critical in solving a robust coverage model accounting for travel time uncertainty and containing nearly half-a-million integer variables. It solved the robust model on a regular PC with 16 GB RAM in half an hour. The non-decomposition approach needed a workstation with 124 GB RAM to solve the same problem in 5 h.

Suggested Citation

  • Shayesta Wajid & N. Nezamuddin, 2023. "Optimizing emergency services for road safety using a decomposition method: a case study of Delhi," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 155-173, March.
    • Handle: RePEc:spr:opsear:v:60:y:2023:i:1:d:10.1007_s12597-022-00612-1
    DOI: 10.1007/s12597-022-00612-1
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    1. Mark S. Daskin, 1983. "A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution," Transportation Science, INFORMS, vol. 17(1), pages 48-70, February.
    2. Goldberg, Jeffrey & Dietrich, Robert & Ming Chen, Jen & Mitwasi, M. George & Valenzuela, Terry & Criss, Elizabeth, 1990. "Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ," European Journal of Operational Research, Elsevier, vol. 49(3), pages 308-324, December.
    3. Repede, John F. & Bernardo, John J., 1994. "Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky," European Journal of Operational Research, Elsevier, vol. 75(3), pages 567-581, June.
    4. Charles ReVelle & Kathleen Hogan, 1989. "The Maximum Availability Location Problem," Transportation Science, INFORMS, vol. 23(3), pages 192-200, August.
    5. Brotcorne, Luce & Laporte, Gilbert & Semet, Frederic, 2003. "Ambulance location and relocation models," European Journal of Operational Research, Elsevier, vol. 147(3), pages 451-463, June.
    6. Schmid, Verena & Doerner, Karl F., 2010. "Ambulance location and relocation problems with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1293-1303, December.
    7. Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
    8. Constantine Toregas & Ralph Swain & Charles ReVelle & Lawrence Bergman, 1971. "The Location of Emergency Service Facilities," Operations Research, INFORMS, vol. 19(6), pages 1363-1373, October.
    9. A Başar & B Çatay & T Ünlüyurt, 2011. "A multi-period double coverage approach for locating the emergency medical service stations in Istanbul," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 627-637, April.
    10. Kathleen Hogan & Charles ReVelle, 1986. "Concepts and Applications of Backup Coverage," Management Science, INFORMS, vol. 32(11), pages 1434-1444, November.
    11. Dirk Degel & Lara Wiesche & Sebastian Rachuba & Brigitte Werners, 2015. "Time-dependent ambulance allocation considering data-driven empirically required coverage," Health Care Management Science, Springer, vol. 18(4), pages 444-458, December.
    12. Kim, Sung Hoo & Chung, Jin-Hyuk, 2018. "Exploration on origin–destination-based travel time variability: Insights from Seoul metropolitan area," Journal of Transport Geography, Elsevier, vol. 70(C), pages 104-113.
    13. Michael O. Ball & Feng L. Lin, 1993. "A Reliability Model Applied to Emergency Service Vehicle Location," Operations Research, INFORMS, vol. 41(1), pages 18-36, February.
    14. Galvao, Roberto Dieguez & ReVelle, Charles, 1996. "A Lagrangean heuristic for the maximal covering location problem," European Journal of Operational Research, Elsevier, vol. 88(1), pages 114-123, January.
    15. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    16. Mark S. Daskin & Edmund H. Stern, 1981. "A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment," Transportation Science, INFORMS, vol. 15(2), pages 137-152, May.
    17. Wajid, Shayesta & Nezamuddin, N., 2022. "A robust survival model for emergency medical services in Delhi, India," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    18. Fujiwara, Okitsugu & Makjamroen, Thanet & Gupta, Kapil Kumar, 1987. "Ambulance deployment analysis: A case study of Bangkok," European Journal of Operational Research, Elsevier, vol. 31(1), pages 9-18, July.
    19. James A. Fitzsimmons, 1973. "A Methodology for Emergency Ambulance Deployment," Management Science, INFORMS, vol. 19(6), pages 627-636, February.
    20. 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.
    21. David J. Eaton & Mark S. Daskin & Dennis Simmons & Bill Bulloch & Glen Jansma, 1985. "Determining Emergency Medical Service Vehicle Deployment in Austin, Texas," Interfaces, INFORMS, vol. 15(1), pages 96-108, February.
    22. Leknes, Håkon & Aartun, Eirik Skorge & Andersson, Henrik & Christiansen, Marielle & Granberg, Tobias Andersson, 2017. "Strategic ambulance location for heterogeneous regions," European Journal of Operational Research, Elsevier, vol. 260(1), pages 122-133.
    23. Armann Ingolfsson & Susan Budge & Erhan Erkut, 2008. "Optimal ambulance location with random delays and travel times," Health Care Management Science, Springer, vol. 11(3), pages 262-274, September.
    24. Laura McLay, 2009. "A maximum expected covering location model with two types of servers," IISE Transactions, Taylor & Francis Journals, vol. 41(8), pages 730-741.
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