Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities. Emergency response management comprises of several stages and sub-problems like forecasting, resource allocation, and dispatch. The design of principled approaches to tackle each problem is necessary to create efficient emergency response management (ERM) pipelines. Over the last six years, we have worked with several first responder organizations to design ERM pipelines. In this paper, we highlight some of the challenges that we have identified and lessons that we have learned through our experience in this domain. Such challenges are particularly relevant for practitioners and researchers, and are important considerations even in the design of response strategies to mitigate disasters like floods and earthquakes.
Emergency response to incidents such as accidents, medical
calls, urban crimes, poaching, and fires is one of the most
pressing problems faced by communities across the globe.
Emergency response must be fast and efficient in order to
minimize loss of lives
ERM can be divided into five major components: 1) mitigation, 2) preparedness, 3) detection, 4) response, and 5) recovery. The stages of ERM systems are heavily interlinked (Mukhopadhyay et al. 2020). Mitigation involves sustained efforts to reduce long-term risks to people and property. It also involves creating forecasting models to understand the spatial and temporal characteristics of incidents. Preparedness involves creating policy and allocating resources that enable emergency response management. The third phase seeks to use automated techniques to detect incidents as they happen in order to expedite response. The dispatch phase, the most critical phase in the field, involves responding to incidents when they occur. Finally, the recovery phase seeks to provide support and sustenance to communities and individuals affected by the emergency. While most of the prior work in ERM has studied these problems independently, these stages are inter-linked. Frequently, the output of one stage serves as the input for another. For example, predictive models learned in the preparedness stage are used in planning response strategies. Therefore, it is crucial that ERM pipelines are designed such that intricate interdependencies are considered. In this paper, we highlight some of the challenges that we have faced while designing ERM pipelines and lessons that we have learned through the process. A large portion of the insights have come from our colleagues at the Nashville Fire Department and the Tennessee Department of Transportation, who have provided invaluable domain expertise to us.
While ERM forms a crucial component of the cities and
governments, designing and deploying principled approaches
to ERM is challenging. We consider the following to be
the main challenges in the design and deployment of ERM
pipelines.
1. How to forecast incident occurrence? It has been noted
that emergency incidents are generally difficult to
predict due to the inherent random nature of such incidents
and spatially varying factors (Shankar, Mannering, and
Barfield 1995; Mukhopadhyay et al. 2017). Incidents are
highly sporadic, making it particularly difficult to learn
models of incident occurrence. For example, well-known
regression models such as Poisson regression and
negative binomial regression have been shown to perform
poorly on accident data due to the prevalence of zero
counts (Mukhopadhyay et al. 2020). Incident data has also
been shown to be particularly sensitive to the scale of
spatial and temporal resolutions, thereby making it
difficult to perform meaningful inference. There are several
approaches that have shown to alleviate these concerns.
First, identifying clusters of incidents (both spatial and
non-spatial) has shown to balance model variance and
spatial heterogeneity particularly well (Sasidharan, Wu,
and Menendez 2015; Mukhopadhyay et al. 2017).
Second, dual state models like zero-inflated Poisson models
can be used to address the issue of a high number of zero
counts in data (Qin, Ivan, and Ravishanker 2004).
2. When to optimize? Arguably, the most important
component of ERM pipelines is to dispatch responders when
incidents occur. While resource allocation and dispatch
to emergency incidents evolve in highly uncertain and
dynamic environments, the expectation is that response
is very timely (Felder and Brinkmann 2002;
Mukhopadhyay, Wang, and Vorobeychik 2018). Approaches to
optimize dispatch typically focus on decision-making after
an incident occurs
The principled design of ERM systems is an important problem faced by communities. As smart and connected communities evolve, they present both opportunities and challenges to manage ERM systems. In this short paper, we highlight common problems and lessons learned through our experience in designing ERM systems over the last few years.
We would like to acknowledge the National Science Foundation and the Center of Automotive Research at Stanford for funding this research. We would also like to thank the Nashville Fire Department (NFD) and the Tennessee Department of Transportation (TDOT) for collaborating with us and providing invaluable knowledge about the intricacies of emergency response. Felder, S.; and Brinkmann, H. 2002. Spatial allocation of emergency medical services: minimising the death rate or providing equal access? Regional Science and Urban Economics 32(1): 27– 45.
Pettet, G.; Mukhopadhyay, A.; Kochenderfer, M.; Vorobeychik, Y.; and Dubey, A. 2020. On algorithmic decision procedures in emergency response systems in smart and connected communities. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
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Sasidharan, L.; Wu, K.-F.; and Menendez, M. 2015. Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland. Accident Analysis & Prevention 85: 219–228.
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