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

Shayesta Wajid () and N. Nezamuddin ()
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Shayesta Wajid: Indian Institute of Technology Delhi
N. Nezamuddin: Indian Institute of Technology Delhi

OPSEARCH, 2023, vol. 60, issue 1, No 6, 155-173

Abstract: 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.

Keywords: Emergency medical services; Double standard model; Mixed integer linear programming; Decomposition method; Facility location (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12597-022-00612-1

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