Effective emergency (medical, fire or criminal) response is crucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-driven optimization models for efficient allocation of emergency response vehicles (ERVs) to base locations. However, these data-driven optimization models either ignore the dispatch strategy of ERVs (typically the nearest available ERV is dispatched to serve an incident) or employ myopic approaches (e.g., greedy approach based on marginal gain). This results in allocations that are not synchronised with the real evolution dynamics on the ground or can be improved significantly. To bridge this gap, we make the following contributions: (1) We first provide a novel exact optimization model for allocation of ERVs that incorporates the non-linear real-world dispatch strategy as linear constraints and ensures that optimization exactly imitates the real-world dynamics of EMS; (2) In order to improve scalability, we then provide two novel heuristic approaches to solve problems with large number of emergency incidents; and (3) Finally, using two real-world EMS data sets, we empirically demonstrate that our heuristic approaches provide significant improvement over the best known benchmark approach.
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