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.
more »
« less
An online decision-theoretic pipeline for responder dispatch
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.
more »
« less
- NSF-PAR ID:
- 10098756
- Date Published:
- Journal Name:
- Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems
- Page Range / eLocation ID:
- 185 to 196
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3302509.3311055
