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进智能互联社区中急救人员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进行隔离，从而降低了预测的有效性并妨碍了资源的有效协调。该研究项目通过整合纳什维尔市不同城市机构的数据和应急资源以及其他广泛可用的数据（例如行人交通、道路特征、交通拥堵和天气），为研究该问题提供了独特的机会。这将允许开发预测异质事件的模型，例如不同类别的犯罪以及车辆事故。通过这些模型，我们可以开发决策支持工具来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、消防队或两者），以最大限度地缩短预期响应时间，以及最合适的设备，同时考虑事件的时间、地点和性质。正如预测未来会发生的那样。最终，本研究中开发的方法可以应用于多资源时空调度面临挑战的其他领域。该项目的技术方面将要求我们开发解决与连续时间预测相关的算法挑战的方法。异质事件的时空时间序列。在处理预测任务时，我们将开发考虑多种特征的事件聚类方法，并使用所得分组来开发不同的连续时间模型，基于生存分析来预测事件发生分布。反过来，优化框架需要一个可扩展的解决方案，利用已开发的综合预测方法，对异构应急响应人员进行综合时空分配。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，可以将其形式化为网络流线性程序，并增强以考虑资源的异构性以及这些资源可以解决的事件。 开发的解决方案将提供给社区以进行最大程度的传播。 这项研究有可能通过最佳协调响应来影响纳什维尔地铁警察局和纳什维尔消防局的实际运营规划。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

Scalable Initial State Interdiction for Factored MDPs

因子 MDP 的可扩展初始状态拦截

• 发表时间: 2018-01
• 期刊: International Joint Conference on Artificial Intelligence
• 作者: Panda, Swetasudha;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Incident analysis and prediction using clustering and bayesian network

使用聚类和贝叶斯网络进行事件分析和预测

• 发表时间: 2017-08
• 期刊: 2017 IEEE International Conference on Smart City Innovations
• 作者: Pettet, Geoffrey;Nannapaneni, Saideep;Stadnick, Benjamin;Dubey, Abhishek;Biswas, Gautam
• 通讯作者: Biswas, Gautam

Deceiving Cyber Adversaries: A Game Theoretic Approach

欺骗网络对手：博弈论方法

• 发表时间: 2018-01
• 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;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: 2018-01
• 期刊: International Conference on Autonomous Agents and Multiagent Systems
• 作者: Wilder, Bryan;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

An online decision-theoretic pipeline for responder dispatch

用于响应者调度的在线决策理论管道

• DOI: 10.1145/3302509.3311055
• 发表时间: 2019-02-21
• 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
• 通讯作者: Yevgeniy Vorobeychik