Integrated Safety Incident Forecasting and Analysis

本研究的目的是了解和改进智能和互联社区中急救人员使用的资源协调和调度机制。在现有技术和实践中，事件预测和响应通常是按类别和部门划分的，这降低了预测的有效性，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，如行人交通、道路特征、交通拥堵和天气，来研究这个问题。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。有了这些模型，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决定派遣哪个单位（警察、消防或两者），以最大限度地减少预期的响应时间，以及考虑到事件的时间、地点和性质以及未来预计发生的事件，哪些设备最合适。最终，本研究开发的方法可以应用于其他具有多资源时空调度挑战性的领域。该项目的技术方面将要求我们开发方法来解决与异构事件的时空时间序列的连续时间预测相关的算法挑战。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，并使用所得分组开发不同的连续时间模型，以基于生存分析预测事件发生分布。反过来，优化框架需要利用已开发的综合预测方法，为异构应急响应人员的综合时空分配提供可扩展的解决方案。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，该问题可以形式化为网络流线性规划，并扩展到考虑这些资源可以处理的资源和事件的异质性。拟定的解决办法将提供给社区，以便最大限度地传播。这项研究有可能影响纳什维尔大都会警察局和纳什维尔消防局的实际行动计划，通过最佳协调反应。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。

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.