Ayan Mukhopadhyay, Yevgeniy Vorobeychik, Abhishek Dubey, Gautam Biswas
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, S\~ao Paulo, Brazil, May 8-12, 2017 2017
Emergency response efficiency depends on optimal responder placement and dispatch. Traditional approaches assume uniform incident distributions and fixed response times. This work is innovative because it combines predictive modeling of incident severity with optimization algorithms to adaptively allocate responders based on spatial-temporal patterns.
This paper addresses emergency responder allocation in urban areas using incident prediction models and optimization algorithms. The work develops methods to predict incident arrival times and severities using survival analysis, then formulates responder allocation as an optimization problem balancing coverage and response times. A hierarchical clustering approach identifies incident patterns.
The paper demonstrates improved emergency response times through data-driven responder allocation in Nashville. Results show how incident prediction models enable intelligent dispatch that accounts for incident severity, reducing overall response times compared to traditional approaches.
@inproceedings{Mukhopadhyay2017,
author = {Mukhopadhyay, Ayan and Vorobeychik, Yevgeniy and Dubey, Abhishek and Biswas, Gautam},
booktitle = {Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2017, S{\~{a}}o Paulo, Brazil, May 8-12, 2017},
title = {Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model},
year = {2017},
acceptance = {27},
pages = {168--177},
abstract = {Efficient emergency response is a major concern in densely populated urban areas. Numerous techniques have been proposed to allocate emergency responders to optimize response times, coverage, and incident prevention. Effective response depends, in turn, on effective prediction of incidents occurring in space and time, a problem which has also received considerable prior attention. We formulate a non-linear mathematical program maximizing expected incident coverage, and propose a novel algorithmic framework for solving this problem. In order to aid the optimization problem, we propose a novel incident prediction mechanism. Prior art in incident prediction does not generally consider incident priorities which are crucial in optimal dispatch, and spatial modeling either considers each discretized area independently, or learns a homogeneous model. We bridge these gaps by learning a joint distribution of both incident arrival time and severity, with spatial heterogeneity captured using a hierarchical clustering approach. Moreover, our decomposition of the joint arrival and severity distributions allows us to independently learn the continuous-time arrival model, and subsequently use a multinomial logistic regression to capture severity, conditional on incident time. We use real traffic accident and response data from the urban area around Nashville, USA, to evaluate the proposed approach, showing that it significantly outperforms prior art as well as the real dispatch method currently in use.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/atal/MukhopadhyayVDB17},
category = {selectiveconference},
contribution = {colab},
file = {:Mukhopadhyay2017-Prioritized_Allocation_of_Emergency_Responders_based_on_a_Continuous-Time_Incident_Prediction_Model.pdf:PDF},
keywords = {incident prediction, emergency response, optimization, responder allocation, survival analysis},
project = {smart-emergency-response,smart-cities},
tag = {ai4cps,incident},
timestamp = {Wed, 27 Sep 2017 07:24:00 +0200},
url = {http://dl.acm.org/citation.cfm?id=3091154}
}