Title: Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems
Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders. more »« less
Pettet, Geoffrey; Mukhopadhyay, Ayan; Samal, Chinmaya; Dubey, Abhishek; Vorobeychik, Yevgeniy(
, 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.
Pettet, Geoffrey; Baxter, Hunter; Vazirizade, Sayyed Mohsen; Purohit, Hemant; Ma, Meiyi; Mukhopadhyay, Ayan; Dubey, Abhishek(
, 2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER))
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.
Pettet, Geoffrey; Nannapaneni, Saideep; Stadnick, Benjamin; Dubey, Abhishek; Biswas, Gautam(
, 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.
Mukhopadhyay, Ayan; Pettet, Geoffrey; Samal, Chinmaya; Dubey, Abhishek; Vorobeychik, Yevgeniy(
, Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems)
The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time of responders with a drastic reduction in computational time.
Mukhopadhyay, Ayan; Pettet, Geoff; Samal, Chinmaya; Dubey, Abhishek; Vorobeychik, Yevgeniy(
, International Conference on Cyber-Physical Systems)
The problem of dispatching emergency responders
to service traffic accidents, fire, distress calls and crimes plagues
urban areas across the globe. While such problems have been
extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments
under which critical emergency response occurs, and therefore,
fail to be implemented in practice. Any holistic approach towards
creating a pipeline for effective emergency response must also
look at other challenges that it subsumes - predicting when
and where incidents happen and understanding the changing
environmental dynamics. We describe a system that collectively
deals with all these problems in an online manner, meaning that
the models get updated with streaming data sources. We highlight
why such an approach is crucial to the effectiveness of emergency
response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for
responder dispatch. We argue that carefully crafted heuristic
measures can balance the trade-off between computational time
and the quality of solutions achieved and highlight why such
an approach is more scalable and tractable than traditional
approaches. We also present an online mechanism for incident
prediction, as well as an approach based on recurrent neural
networks for learning and predicting environmental features that
affect responder dispatch. We compare our methodology with
prior state-of-the-art and existing dispatch strategies in the field,
which show that our approach results in a reduction in response
time with a drastic reduction in computational time.
Vazirizade, Sayyed, Mukhopadhyay, Ayan, Pettet, Geoffrey, El Said, Said, Baroud, Hiba, and Dubey, Abhishek. Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems. Retrieved from https://par.nsf.gov/biblio/10275728. IEEE Conference on Smart Computing. SmartComp 2021. .
Vazirizade, Sayyed, Mukhopadhyay, Ayan, Pettet, Geoffrey, El Said, Said, Baroud, Hiba, and Dubey, Abhishek.
"Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems". IEEE Conference on Smart Computing. SmartComp 2021. (). Country unknown/Code not available. https://par.nsf.gov/biblio/10275728.
@article{osti_10275728,
place = {Country unknown/Code not available},
title = {Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems},
url = {https://par.nsf.gov/biblio/10275728},
abstractNote = {Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.},
journal = {IEEE Conference on Smart Computing. SmartComp 2021.},
author = {Vazirizade, Sayyed and Mukhopadhyay, Ayan and Pettet, Geoffrey and El Said, Said and Baroud, Hiba and Dubey, Abhishek},
editor = {null}
}
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