CDS&E: Point Process Models for Traffic Risk Analysis and Crash Prevention

州和联邦交通部门收集的交通和撞车数据库包含大量可用于提高高速公路安全的信息。例如，事故数据库可用于确定事故频繁发生的位置以及导致这些事故的潜在因素。对撞车数据库的这种分析可随后导致确定可采取的对策，以减少高危地点发生撞车的频率。虽然正在进行许多努力来利用这些数据库中包含的信息来提高交通安全，但现代交通数据集包含的信息比目前使用传统数据分析技术所能提取的信息更多。常见的碰撞数据统计技术最明显的缺点是，这种技术只关注道路的一小部分(如交叉口)，而不是同时分析整个道路网。在这个项目中，研究人员正在开发统计方法，该方法将分析整个公路网，以捕捉可能导致撞车增加的道路特征之间的重要关系。最终，该项目的目标是分析交通网络数据，以确定为所有出行者创建更安全的道路网络的方法。除了研究活动，与该项目相关的指导活动还包括学生在数据科学和交通安全工程方面的高级主题的指导。教育活动将包括向高中生介绍STEM职业生涯，以及发展一个新的跨学科研究小组。从历史上看，交通事故的统计模型分析了道路段的碰撞总数，其中聚集的数据丧失了对路段内任何信息的使用。然而，现代撞车数据库包含关于撞车准确位置的数据(称为点模式数据)，如果分析得当，这些数据可以给出比汇总数据更丰富的统计推断。该项目旨在充分利用现代交通数据库中的信息，通过考虑道路交通的连续性质，而不是依赖于任意聚合的路段计数数据。具体地说，该项目将开发易于实施和在计算上可行的方法来对撞击点模式数据进行建模，以确定可能发生撞车的位置(称为热点识别)以及道路特征如何影响碰撞的可能性(称为风险因素识别)。具体地说，该项目有以下与统计和土木工程科学相关的目标：(1)在公路网上开发分段线性点过程模型，(2)开发一种分层的点过程方法来同时模拟多种碰撞类型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Traffic and crash databases collected by state and federal departments of transportation contain a wealth of information that can be used to increase highway safety. For example, crash databases can be used to identify locations where crashes frequently occur as well as the underlying factors that contributed to those crashes. Such analysis of crash databases can subsequently lead to identifying countermeasures that can be enacted to decrease the frequency of crashes at high-risk locations. While many efforts are ongoing to use the information contained in these databases to increase traffic safety, modern traffic datasets contain more information than can be currently extracted using traditional data analysis techniques. The most glaring shortcoming of common statistical techniques for crash data is that such techniques focus only on small segments of the road (e.g. intersections) rather than analyzing the entire roadway network simultaneously. In this project, the researchers are developing statistical methodology that will analyze an entire roadway network to capture important relationships between roadway features that may lead to an increase in crashes. Ultimately, the goal of this project is to analyze traffic network data so as to identify ways to create a safer roadway network for all travelers. Beyond research activities, mentoring activities associated with this project include student mentoring on advanced topics in data science and traffic safety engineering. Educational activities will include STEM career presentations to high school students as well as the development of a novel interdisciplinary research group.Historically, statistical models for traffic crashes have analyzed aggregated crash counts along with roadway segments, where aggregated data forfeit the use of any within-segment information. Modern crash databases, however, contain data on the exact locations of crashes (referred to as point pattern data) which, if analyzed appropriately, can give richer statistical inferences than aggregated data. This project seeks to fully utilize the information in modern traffic databases by considering the continuous nature of roadway traffic rather than relying on arbitrarily aggregated count data over roadway segments. Specifically, this project will develop easily implementable and computationally feasible approaches to modeling crash point pattern data to determine where crashes are likely to occur (referred to as hot spot identification) as well as how features of the roadway influence the potential for a crash (referred to as risk factor identification). Specifically, this project has the following goals related to statistical and civil engineering science: (1) develop piece-wise linear point process models on roadway networks and (2) develop a hierarchical point process approach to model multiple crash types simultaneously.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.