ATD: New Approaches for Analyzing Spatiotemporal Data for Anomalies

ATD：分析时空数据异常的新方法

• 批准号: 1830489
• 负责人: Shen Shyang Ho
• 金额: $ 12.5万
• 依托单位: Rowan University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830489&HistoricalAwards=false
• 关键词: ATD; New; Approaches; Analyzing; Spatiotemporal

High population density urban areas have increasingly been the targets of terrorism in recent years due to the possibility of inflicting a large number of casualties in a crowded environment and causing high impact disruption especially in the outdoor environment. The project develops new approaches to complement and validate results produced by existing approaches based on video surveillance and human population density estimation via cellphone usage to monitor anomalies and threat level in urban area. It will benefit agencies and local governments that require the planning and allocation of resources to secure locations with higher levels of threat in a timely manner. The use of the data on shared transportation (e.g., bikesharing, taxi) to monitor anomalies and threats provides additional coverage of an urban area and could capture "blindspots" not covered by the other data sources. Towards this end, the success of a terrorist attack in a city can be significantly reduced. The goals of the three-year project are to (i) develop a new data-driven hybrid differential equations (DE) modeling approach for object (e.g., human, bike, etc.) density and flow estimation to model human dynamics, and (ii) develop a real-time anomaly detection algorithm utilizing the DE model and real-time observed data to identify anomalous crowd density and traffic in an urban environment. The main spatiotemporal data used in this project is the publicly available bikesharing data from three US cities. Due to the interdependency of locations when modeling human dynamics, spatiotemporal data are transformed into time-evolving graphs as the data representation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，由于可能在拥挤的环境中造成大量人员伤亡，并造成高影响力破坏，尤其是在室外环境中，尤其是在室外环境中，近年来，城市人口越来越多地成为恐怖主义的目标。该项目开发了基于视频监视和通过手机使用情况估算人口密度估算的现有方法所产生的新方法，并验证了现有方法，以监视城市地区的异常和威胁水平。它将受益于需要计划和分配资源的机构和地方政府，以及时确保具有更高含量威胁的位置。使用共享运输数据（例如，自行车，出租车）来监视异常和威胁的数据提供了对城市地区的额外覆盖范围，并且可以捕获其他数据源未涵盖的“盲点”。为此，可以大大减少在城市恐怖袭击的成功。 这项三年项目的目标是（i）开发一个新的数据驱动的杂交差分方程（DE）建模方法（例如，人，自行车等）的密度和流量估计来建模人类动力学，以及（ii）使用DE模型和实时经过实时的数据来确定一个实时的异常检测算法，以确定一个型模型和实时经过的数据，以确定一个环境和交通量，并在一个环境中综合量。该项目中使用的主要时空数据是来自美国三个城市的公开骑自行车数据。由于位置在建模人体动力学时的相互依赖性，因此时空数据被转变为随着数据表示形式的变化图。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响标准通过评估来进行评估。

项目成果

A Discrete Model For Bike Share Inventory

自行车共享库存的离散模型

• 发表时间: 2020
• 期刊: International Journal of Difference Equations
• 作者: Chadwick, A;Wang, M
• 通讯作者: Wang, M

Optimal control for an ordinary differential equation online social network model

• DOI: 10.7153/dea-2022-14-13
• 发表时间: 2022
• 期刊: Differential Equations & Applications
• 作者: L. Kong;Min Wang

On the resilience of a fractional compartment model

• DOI: 10.1080/00036811.2020.1712370
• 发表时间: 2020-01
• 期刊: Applicable Analysis
• 影响因子: 1.1
• 作者: Min Wang

A fractional differential equation model for bike share systems

• DOI: 10.23952/jnfa.2019.23
• 发表时间: 2019
• 期刊: Journal of Nonlinear Functional Analysis
• 影响因子: 1.6
• 作者: J. Graef;S. Ho;L. Kong;Min Wang

Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data

• DOI: 10.1145/3356473.3365190
• 发表时间: 2019-11
• 期刊: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News
• 作者: Alex Lam;Matthew Schofield;S. Ho