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1. Incident management and analysis dashboard for fire departments: ICCPS demo

   https://doi.org/10.1145/3302509.3313329  

   Pettet, Geoffrey ; Mukhopadhyay, Ayan ; Samal, Chinmaya ; Dubey, Abhishek ; Vorobeychik, Yevgeniy ( January 2019 , Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems)

   null (Ed.)

   This work presents a dashboard tool that helps emergency responders analyze and manage spatial-temporal incidents like crime and traffic accidents. It uses state-of-the-art statistical models to learn incident probabilities based on factors such as prior incidents, time and weather. The dashboard can then present historic and predicted incident distributions. It also allows responders to analyze how moving or adding depots (stations for emergency responders) affects average response times, and can make dispatching recommendations based on heuristics. Broadly, it is a one-stop tool that helps responders visualize historical data as well as plan for and respond to incidents. 

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2. Incident analysis and prediction using clustering and bayesian network

   Pettet, Geoffrey ; Nannapaneni, Saideep ; Stadnick, Benjamin ; Dubey, Abhishek ; Biswas, Gautam ( August 2017 , 2017 IEEE International Conference on Smart City Innovations)

   Advances in data collection and storage infrastructure offer an unprecedented opportunity to integrate both data and emergency resources in a city into a dynamic learning system that can anticipate and rapidly respond to heterogeneous incidents. In this paper, we describe integration methods for spatio-temporal incident forecasting using previously collected vehicular accident data provided to us by the Nashville Fire Department. The literature provides several techniques that focus on analyzing features and predicting accidents for specific situations (specific intersections in a city, or certain segments of a freeway, for example), but these models break down when applied to a large, general area consisting of many road and intersection types and other factors like weather conditions. We use Similarity Based Agglomerative Clustering (SBAC) analysis to categorize incidents to account for these variables. Thereafter, we use survival analysis to learn the likelihood of incidents per cluster. The mapping of the clusters to the spatial locations is achieved using a Bayesian network. The prediction methods we have developed lay the foundation for future work on an optimal emergency vehicle allocation and dispatch system in Nashville. 

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3. On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

   Pettet, Geoffrey ; Mukhopadhyay, Ayan ; Kochenderfer, Mykel ; Vorobeychik, Yevgeniy ; Dubey, Abhishek ( January 2020 , roceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems)

   Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance. 

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4. On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

   Pettet, Geoffrey ; Mukhopadhyay, Ayan ; Kochenderfer, Mykel ; Vorobeychik, Yevgeniy ; Dubey, Abhishek ( January 2020 , International Conference on Autonomous Agents and Multiagent Systems (AAMAS))

   Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption — algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem — a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance. 

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5. Designing Emergency Response Pipelines : Lessons and Challenges

   Mukhopadhyay, Ayan ; Pettet, Geoffrey ; Kochenderfer, Mykel ; Dubey, Abhishek ( January 2020 , AI for Social Good Workshop, AAAI Fall Symposium Series 2020)

   null (Ed.)

   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. 

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