Integrated Safety Incident Forecasting and Analysis

综合安全事件预测与分析

• 批准号: 1640624
• 负责人: Yevgeniy Vorobeychik
• 金额: $ 20万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640624&HistoricalAwards=false
• 关键词: Integrated; Safety; Incident; Forecasting; Analysis

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders in smart and connected communities. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. This research project provides a unique opportunity to study the problem by integrating both the data and emergency resources from distinct urban agencies in the City of Nashville along with other widely available data such as pedestrian traffic, road characteristics, traffic congestion, and weather. This will allow development of models for anticipating heterogeneous incidents, such as distinct categories of crime, as well as vehicular accidents. With these models we can develop decision support tools to optimize both resource allocation and response times. These tools will help the emergency responders determine which units to dispatch (police, fire, or both) in order to minimize expected response time, and what equipment is most appropriate, taking into account the time, location, and nature of incidents, as well as those predicted to occur in the future. Ultimately, the methods developed in this research can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge.The technical aspects of this project will require us to develop methods for solving the algorithmic challenge related to continuous-time forecasting of spatio-temporal time series of heterogeneous incidents. In tackling the forecasting task, we will develop methods to cluster incidents taking into account multiple features, and use the resulting groupings to develop distinct continuous-time models that forecast incident occurrence distributions based on survival analysis. The optimization framework, in turn, requires a scalable solution for integrated spatio-temporal allocation of heterogeneous emergency responders, making use of developed integrated forecasting methods. The proposed optimization methods will transform the incident response problem into a transportation problem with heterogeneous resources, which can be formalized as a network-flow linear program, augmented to account for heterogeneity in the resources and incidents that these resources can address. The developed solutions will be made available to the community for maximal dissemination. This research has the potential to impact actual operational planning at the Metro Nashville Police Department and Nashville Fire Department, by optimally coordinating responses. Broader impacts also include involvement in educational activities, including STEM-related projects for High School students at the School for Science and Math at Vanderbilt, undergraduate and graduate teaching, and active engagement of undergraduates and graduates in research.

本研究的目的是了解和改善智能和互联社区中第一响应者使用的资源协调和调度机制。在现有技术以及实践中，事件预测和响应通常按类别和部门孤立，降低了预测的有效性并妨碍了资源的有效协调。这个研究项目提供了一个独特的机会，通过整合数据和应急资源，从不同的城市机构在纳什维尔沿着与其他广泛可用的数据，如行人交通，道路特征，交通拥堵和天气的问题。这将有助于开发预测不同事件的模型，例如不同类别的犯罪以及车辆事故。有了这些模型，我们可以开发决策支持工具，以优化资源分配和响应时间。 这些工具将帮助应急响应人员确定派遣哪些单位（警察、消防或两者），以最大限度地缩短预期响应时间，并考虑到事件的时间、地点和性质以及预测未来发生的事件，确定最合适的设备。最终，在这项研究中开发的方法可以应用到其他领域的多资源的时空调度是一个挑战，这个项目的技术方面将需要我们开发的方法来解决与连续时间预测的时空时间序列的异构事件的算法的挑战。在处理预测任务时，我们将开发考虑多个特征的事件聚类方法，并使用由此产生的分组来开发基于生存分析预测事件发生分布的不同连续时间模型。优化框架，反过来，需要一个可扩展的解决方案，异构应急响应的综合时空分配，利用开发的综合预测方法。所提出的优化方法将事件响应问题转化为具有异构资源的运输问题，其可以形式化为网络流线性规划，增强以考虑这些资源可以解决的资源和事件的异构性。 制定的解决方案将提供给社区，以便最大限度地传播。 这项研究有可能影响实际的业务规划在地铁纳什维尔警察局和纳什维尔消防局，通过最佳协调的反应。更广泛的影响还包括参与教育活动，包括范德比尔特科学与数学学院高中学生的STEM相关项目，本科生和研究生教学，以及本科生和研究生积极参与研究。

项目成果

Near-optimal interdiction of factored MDPs

因子 MDP 的近乎最佳拦截

• 发表时间: 2017
• 期刊: Conference on Uncertainty in Artificial Intelligence
• 作者: Panda, Swetasudha;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Controlling Elections through Social Influence

通过社会影响力控制选举

• 发表时间: 2018
• 期刊: International Conference on Autonomous Agents and Multiagent Systems
• 作者: Wilder, Bryan;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Deceiving Cyber Adversaries: A Game Theoretic Approach

欺骗网络对手：博弈论方法

• 发表时间: 2018
• 期刊: International Conference on Autonomous Agents and Multiagent Systems
• 作者: Schlenker, Aaron;Thakoor, Omkar;Xu, Haifeng;Tambe, Milind;Vayanos, Phebe;Fang, Fei;Tran-Thanh, Long;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

A Decision Theoretic Framework for Emergency Responder Dispatch

紧急救援人员调度的决策理论框架

• 发表时间: 2018
• 期刊: International Conference on Autonomous Agents and Multiagent Systems
• 作者: Mukhopadhyay, Ayan;Wang, Zilin;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

An online decision-theoretic pipeline for responder dispatch

• DOI: 10.1145/3302509.3311055
• 发表时间: 2019-02
• 期刊: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems
• 作者: Ayan Mukhopadhyay;Geoffrey Pettet;Chinmaya Samal;A. Dubey;Yevgeniy Vorobeychik