IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v60y2023i1d10.1007_s12597-022-00612-1.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-022-00612-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12597-022-00612-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. Kathleen Hogan & Charles ReVelle, 1986. "Concepts and Applications of Backup Coverage," Management Science, INFORMS, vol. 32(11), pages 1434-1444, November.
    6. 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.
    7. 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.
    8. Wajid, Shayesta & Nezamuddin, N., 2022. "A robust survival model for emergency medical services in Delhi, India," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Charles ReVelle & Kathleen Hogan, 1989. "The Maximum Availability Location Problem," Transportation Science, INFORMS, vol. 23(3), pages 192-200, August.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    21. 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.
    22. James A. Fitzsimmons, 1973. "A Methodology for Emergency Ambulance Deployment," Management Science, INFORMS, vol. 19(6), pages 627-636, February.
    23. 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.
    24. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    2. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    3. Wajid, Shayesta & Nezamuddin, N., 2023. "Capturing delays in response of emergency services in Delhi," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    4. Xueping Li & Zhaoxia Zhao & Xiaoyan Zhu & Tami Wyatt, 2011. "Covering models and optimization techniques for emergency response facility location and planning: a review," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 281-310, December.
    5. Wajid, Shayesta & Nezamuddin, N., 2022. "A robust survival model for emergency medical services in Delhi, India," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    6. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    7. 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.
    8. Sorensen, Paul & Church, Richard, 2010. "Integrating expected coverage and local reliability for emergency medical services location problems," Socio-Economic Planning Sciences, Elsevier, vol. 44(1), pages 8-18, March.
    9. Sun Hoon Kim & Young Hoon Lee, 2016. "Iterative optimization algorithm with parameter estimation for the ambulance location problem," Health Care Management Science, Springer, vol. 19(4), pages 362-382, December.
    10. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    11. Ibrahim Çapar & Sharif H Melouk & Burcu B Keskin, 2017. "Alternative metrics to measure EMS system performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 792-808, July.
    12. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    13. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    14. Carvalho, A.S. & Captivo, M.E. & Marques, I., 2020. "Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1064-1080.
    15. Shariat-Mohaymany, Afshin & Babaei, Mohsen & Moadi, Saeed & Amiripour, Sayyed Mahdi, 2012. "Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads," European Journal of Operational Research, Elsevier, vol. 221(1), pages 263-272.
    16. M Gendreau & G Laporte & F Semet, 2006. "The maximal expected coverage relocation problem for emergency vehicles," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 22-28, January.
    17. Cheng, Yung-Hsiang & Liang, Zheng-Xian, 2014. "A strategic planning model for the railway system accident rescue problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 75-96.
    18. Boyacı, Burak & Geroliminis, Nikolas, 2015. "Approximation methods for large-scale spatial queueing systems," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 151-181.
    19. 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.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opsear:v:60:y:2023:i:1:d:10.1007_s12597-022-00612-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.