Smart Incident Response (‘StatResp’)

The goal of this research area is to improve emergency response systems using proactive resource management that minimizes time and maximizes the effectiveness of the response. With road accidents accounting for 1.25 million deaths globally and 240 million emergency medical services (EMS) calls in the U.S. each year, there is a critical need for a proactive and effective response to these emergencies. Furthermore, a timely response to these incidents is crucial and life-saving for severe incidents. The process of managing emergencies requires full integration of planning and response data and models and their implementation in a dynamic and uncertain environment to support real-time decisions of dispatching emergency response resources. However, the current state-of-the-art research has mainly focused on advances that target individual aspects of emergency response (e.g., prediction, optimization) when different components of an Emergency Response Management (ERM) system are highly interconnected. Additionally, the current practice of ERM workflow in the U.S. is reactive, resulting in a large variance in response times


Our work in ERM has spanned the last six years. This project was started by a collaboration between the Smart and Resilient Computing for Physical Environments Lab (SCOPE) and the Computational Economics Research Lab (CERL) at Vanderbilt University. We are thankful to the Center of Automotive Research at Stanford (CARS), the National Science Foundation (NSF), and Tennessee Department of Transportation (TDOT) for sponsoring the project. We have had the fortune of collaborating with the Tennessee Department of Transportation (TDOT), the Nashville Fire Department (MNPD) and Chattanooga City during this project. Currently, this open-source repository is a collection of forecasting, planning, and operationalization tools. We also collaborate actively with Hemant Purohit from George Mason University to model the dynamics of crowd sourced incident data and use it in the resource allocation models. A key component of this work is a set of open source forecasting, clustering and visualization tools to aid first responders better understand the dynamics of spatial-temporal incident occurrence.

Our research has been showcased at multiple global smart city summits, won an innovation from the government technology magazine, covered in the Financial Times, and won the best paper award at ICLR’s AI for Social Good Workshop. Our broader approach can be understood from checking the Research page, or through the overview paper. An early prototype of our tool is available at the following link.

Visit for details about this work.


We are building ‘StatResp’ – an open-source integrated tool-chain to aid first responders understand where and when incidents occur, and how to allocate responders in anticipation of incidents. The historical analysis module of the toolchain is available as a public data dashboard at and

In this work, we use continuous-time generative models to forecast spatiotemporal incidents and the decision-theoretic problem of dispatching responders based on semi-Markovian dynamics. We have also developed efficient and scalable approaches to solve the high-dimensional optimization problem of proactive stationing and dispatch under uncertainty by using Multi-agent Monte Carlo Tree Search (MMCTS). This is important because the problem of stationing and dispatch (response strategies) is complex and requires solutions that can cope with extremely large state spaces resulting from semi-Markovian decision processes (SMDP). One possible approach is to directly solve the SMDP model by estimating the underlying transition function that governs the evolution of the process. Unfortunately, this approach is too slow for dynamically rebalancing the distribution of responders (proactive stationing), which for an average-sized metropolitan area, has a cardinality of 10^25. Therefore, we use the Monte-Carlo Tree Search (MCTS) family of algorithms, which evaluate actions by sampling from a large number of possible scenarios.

Note that a standard MCTS-based approach is not suitable for dynamic allocation due to the sheer size of the state-space in consideration coupled with the low latency that ERM systems can afford. Therefore, we sought a decentralized multi-agent MCTS (MMCTS) approach, originally explored by Claes et. al for multi-robot task allocation during warehouse commissioning. In MMCTS, individual agents build separate trees focused on their own actions, rather than having one monolithic, centralized tree, dramatically reducing their search space. To this end, we use a queue-based rebalancing heuristic, described in our AAMAS 2020 paper [1], to approximate agent behavior. Our innovation lies in adding the concept of action filtering to the standard MCTS approach. This is required because the dispatching domain has several global constraints to adhere to, such as ensuring that an incident is serviced if possible.

The results from the AAMAS 2020 paper indicating that proper choice of parameters reduces response time variance without incurring large rebalancing costs (i.e. the distance agents move during rebalancing). Similar improvements accrue for the dispatching stage as shown in our ICCPS 2019 paper. Using these methods we have also developed analysis procedures to help answer questions such as where should the next fire station be located, how many new emergency dispatch trucks are required and reducing the average response time as shown in our existing research. Recently, we have improved the scalability of the approach using heirarchical abstractions as shown in our ICCPS 2021 paper.

A key aspect of our solution approach is the development of demand forecasting models that describe the need for resources in a given area at a given future time. For this, we build forecasting models using historical incident data, temporal data (weather and traffic), and static roadway data. The data is processed and fed into an aggregator, which identifies clusters of roadway segments (or any user-specified unit of spatial discretization) that are similar to each other using a visual analytic tool. For each such cluster, a set of online forecasting models are learned and compared automatically using criteria such as test-set likelihood and AIC (Akaike Information Criteria) and are updated as new incidents are reported. Our tool currently supports Poisson regression, negative binomial regression, parametric survival modeling, and zero-inflated Poisson regression.

Visit for details about this work.


Ayan and Sayyed gave a tutorial on this work at the SmartComp 2021 Conference. The videos of the talk are available on youtube.

Publications in this area

  1. Y. Senarath, A. Mukhopadhyay, S. Vazirizade, hemant Purohit, S. Nannapaneni, and A. Dubey, Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services, in 21st IEEE International Conference on Data Mining (ICDM 2021), 2021.
  2. S. Singla, A. Mukhopadhyay, M. Wilbur, T. Diao, V. Gajjewar, A. Eldawy, M. Kochenderfer, R. Shachter, and A. Dubey, WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants, in 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021.
  3. S. M. Vazirizade, A. Mukhopadhyay, G. Pettet, S. E. Said, H. Baroud, and A. Dubey, Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems, 2021.
  4. G. Pettet, A. Mukhopadhyay, M. Kochenderfer, and A. Dubey, Hierarchical Planning for Resource Allocation in Emergency Response Systems, in Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, Nashville, TN, USA, 2021.
  5. G. Pettet, A. Mukhopadhyay, M. Kochenderfer, Y. Vorobeychik, and A. Dubey, On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities, in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2020, Auckland, New Zealand, 2020.
  6. Y. Senarath, S. Nannapaneni, H. Purohit, and A. Dubey, Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion, in The 2020 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology, 2020.
  7. S. Basak, A. Dubey, and B. P. Leao, Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks, in IEEE Big Data, Los Angeles, Ca, 2019.
  8. A. Mukhopadhyay, G. Pettet, C. Samal, A. Dubey, and Y. Vorobeychik, An online decision-theoretic pipeline for responder dispatch, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 185–196.
  9. G. Pettet, A. Mukhopadhyay, C. Samal, A. Dubey, and Y. Vorobeychik, Incident management and analysis dashboard for fire departments: ICCPS demo, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 336–337.
  10. G. Pettet, S. Sahoo, and A. Dubey, Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge, in IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, 2019, pp. 511–516.
  11. J. P. Talusan, F. Tiausas, K. Yasumoto, M. Wilbur, G. Pettet, A. Dubey, and S. Bhattacharjee, Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, June 12-15, 2019, 2019, pp. 13–18.
  12. M. Wilbur, A. Dubey, B. Leão, and S. Bhattacharjee, A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 274–282.
  13. H. Purohit, S. Nannapaneni, A. Dubey, P. Karuna, and G. Biswas, Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph, in 2018 IEEE International Science of Smart City Operations and Platforms Engineering in Partnership with Global City Teams Challenge (SCOPE-GCTC), 2018, pp. 30–35.
  14. A. Mukhopadhyay, Y. Vorobeychik, A. Dubey, and G. Biswas, Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8-12, 2017, 2017, pp. 168–177.
  15. A. Ghafouri, A. Laszka, A. Dubey, and X. D. Koutsoukos, Optimal detection of faulty traffic sensors used in route planning, in Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017, 2017, pp. 1–6.
  16. G. Pettet, S. Nannapaneni, B. Stadnick, A. Dubey, and G. Biswas, Incident analysis and prediction using clustering and Bayesian network, in 2017 IEEE SmartWorld, 2017, pp. 1–8.