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Artificial Intelligence for Emergency Response (2306.10068v1)

Published 15 Jun 2023 in cs.CY and cs.AI

Abstract: Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.

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References (56)
  1. Henrik Jaldell. How important is the time factor? saving lives using fire and rescue services. Fire Technology, 53(2):695–708, 2017.
  2. A review of incident prediction, resource allocation, and dispatch models for emergency management. Accident Analysis & Prevention, 165:106501, 2022.
  3. Shana Hertz Hattis. Crime in the United States. Lanham Bernan Press, 2015.
  4. Ayan Mukhopadhyay. Robust Incident Prediction, Resource Allocation and Dynamic Dispatch. PhD thesis, Vanderbilt University, 2019.
  5. USA Deparment of Homeland Security. Phases of Emergency Management, 2019.
  6. Center of Disease Control and Prevention. Road traffic injuries and deaths — a global problem. https://www.cdc.gov/injury/features/global-road-safety/index.html, 2019.
  7. National Emergency Number Association. 9-1-1 statistics. https://www.nena.org/page/911Statistics, 2019.
  8. Prioritized allocation of emergency responders based on a continuous-time incident prediction model. In International Conference on Autonomous Agents and MultiAgent Systems, pages 168–177, 2017.
  9. Ambulance location for maximum survival. Naval Research Logistics, 55(1):42–58, 2008.
  10. Ambulance allocation for maximal survival with heterogeneous outcome measures. Omega, 40(6):918–926, 2012.
  11. A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247(1):294–309, 2015.
  12. Mykel J Kochenderfer. Decision Making Under Uncertainty: Theory and Application. MIT press, 2015.
  13. A decision theoretic framework for emergency responder dispatch. In International Conference on Autonomous Agents and MultiAgent Systems, pages 588–596, 2018.
  14. The location of emergency service facilities. Operations Research, 19(6):1363–1373, 1971.
  15. The maximal covering location problem. volume 32, pages 101–118, 1974.
  16. Solving an ambulance location model by tabu search. Location Science, 5(2):75–88, 1997.
  17. Identification of hazardous rural highway locations. Transportation Research Record, 543, 1974.
  18. Learning incident prediction models over large geographical areas for emergency response systems. arXiv preprint arXiv:2106.08307, 2021.
  19. Interurban accident prediction by administrative area application in greece. Institution of Transportation Engineers Journal, 64(1):35–42, 1994.
  20. Modeling the relationship of accidents to miles traveled. Transportation Research Record, 1068:42–51, 1986.
  21. Modeling vehicle accidents and highway geometric design relationships. Accident Analysis & Prevention, 25(6):689–709, 1993.
  22. Estimating truck accident rate and involvements using linear and poisson regression models. Transportation Planning and Technology, 15(1):41–58, 1990.
  23. Poisson, poisson-gamma and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis & Prevention, 37(1):35–46, 2005.
  24. Estimation of safety at two-way stop-controlled intersections on rural highways. Transportation Research Record, (1401):83–89, 1993.
  25. A comprehensive methodology for the fitting of predictive accident models. Accident Analysis & Prevention, 28(3):281–296, 1996.
  26. Accident prediction models for urban unsignalized intersections in british columbia. Transportation Research Record: Journal of the Transportation Research Board, (1665):93–99, 1999.
  27. Assessing safety on Dutch freeways with data from infrastructure-based intelligent transportation systems. Transportation Research Record, 2083(1):153–161, 2008.
  28. Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety Science, 63:50–56, 2014.
  29. Assessment of freeway traffic parameters leading to lane-change related collisions. Accident Analysis & Prevention, 38(5):936–948, 2006.
  30. Artificial neural networks and logit models for traffic safety analysis of toll plazas. Transportation Research Record, 1784(1):115–125, 2002.
  31. Li Yen Chang. Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Safety Science, 43(8):541–557, 2005.
  32. A Bayesian neural network approach to estimating the energy equivalent speed. Accident Analysis and Prevention, 38(2):248–259, 2006.
  33. The use of convolutional neural networks for traffic incident detection at a network level. In Transportation Research Board Annual Meeting, 2018.
  34. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis & Prevention, 122:239–254, 2019.
  35. Analytic methods in accident research: Methodological frontier and future directions. Analytic methods in accident research, 1:1–22, 2014.
  36. A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity. Analytic methods in accident research, 1:23–38, 2014.
  37. Nyu’s english ace 2005 system description. In ACE 2005 Evaluation Workshop, 2005.
  38. David Ahn. The stages of event extraction. In Workshop on Annotating and Reasoning About Time and Events, pages 1–8, 2006.
  39. Refining event extraction through cross-document inference. In Annual Meeting of the Association for Computational Linguistics, pages 254–262, 2008.
  40. Event extraction via dynamic multi-pooling convolutional neural networks. In International Joint Conference on Natural Language Processing, pages 167–176, 2015.
  41. Joint event extraction via recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 300–309, 2016.
  42. Devnet: A deep event network for multimedia event detection and evidence recounting. In Conference on Computer Vision and Pattern Recognition, pages 2568–2577, 2015.
  43. Bi-level semantic representation analysis for multimedia event detection. IEEE Transactions on Cybernetics, 47(5):1180–1197, 2016.
  44. Joint attributes and event analysis for multimedia event detection. IEEE Transactions on Neural Networks and Learning Systems, 29(7):2921–2930, 2017.
  45. Cross-media structured common space for multimedia event extraction. arXiv preprint arXiv:2005.02472, 2020.
  46. Crisis Response. How waze crowdsourcing navigation app can help emergency services. https://www.crisis-response.com/Articles/594337/How_Waze_crowdsourcing.aspx.
  47. European Emergency Number Association. Exploring the use of waze for emergency response: A pilot project by waze and eena. https://eena.org/knowledge-hub/documents/waze-project-final-report/, 2018.
  48. Practitioner-centric approach for early incident detection using crowdsourced data for emergency services. In International Conference on Data Mining (to appear), pages 22–31, 2021.
  49. Locating emergency services with different priorities: the priority queuing covering location problem. Journal of the Operational Research Society, 59(9):1229–1238, 2008.
  50. Mark S. Daskin. A maximum expected covering location model: Formulation, properties and heuristic solution. Transportation Science, 17(1):48–70, 1983.
  51. The maximum reliability location problem and α𝛼\alphaitalic_α-reliable p-center problem: Derivatives of the probabilistic location set covering problem. Annals of Operations Research, 18(1):155–173, 1989.
  52. Response areas for two emergency units. Operations Research, 20(3):571–594, 1972.
  53. A markov decision process model for the optimal dispatch of military medical evacuation assets. Health Care Management Science, 19(2):111–129, 2016.
  54. An online decision-theoretic pipeline for responder dispatch. In International Conference on Cyber-Physical Systems, pages 185–196, 2019.
  55. On algorithmic decision procedures in emergency response systems in smart and connected communities. In International Conference on Autonomous Agents and Multiagent Systems, pages 1046–1054, 2020.
  56. Hierarchical planning for resource allocation in emergency response systems. arXiv preprint arXiv:2012.13300, 2020.
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