NSF/USDOT: Modeling Matched Traffic and Accident Datasets to Significantly Improve Safety

NSF/USDOT：对匹配的交通和事故数据集进行建模以显着提高安全性

• 批准号: 0338643
• 负责人: Amelia Regan
• 金额: $ 15万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2006-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0338643&HistoricalAwards=false
• 关键词: NSF; USDOT; Modeling; Matched; Traffic

Advances in information technologies for transportation systems have led to the accumulation of large quantities of raw data on transportation system status. To fully leverage such data, new tools must be developed to combine and effectively analyze large databases. The proposed research involves the application of nonlinear multivariate analysis methods to analyze combined truck traffic, detailed traffic flow, accident, and environmental data in order to identify the influences of mixes of truck traffic on the likelihood of accidents by type under different traffic conditions and on different types of network links. Understanding the complex factors surrounding truck accidents, can provide opportunities for intervention to enhance safety. The main analysis method is nonlinear canonical correlation analysis with multiple sets of mixed categorical, ordinal, and numerical variables. This eigenvalue method, implemented through alternating least squares algorithms, is characterized by the optimal scaling of the nonlinear variables and graphical interpretation of results. The project has four distinct phases: (1) establishing a comprehensive database of traffic flow and crash information that is appropriate for identifying truck safety issues on urban freeways (2) identifying, through a specific type of multivariate nonlinear model, freeway locations and time periods where the mix of truck traffic within particular traffic flow conditions has the most adverse safety effects, (3) identifying ways to improve safety in problematic time-space situations. (4) identifying ways to apply our research to data available in other states.In summary, the work will develop tools to help identify unsafe traffic conditions so that accidents can be avoided. We focus on truck involved accidents because these tend to be more severe than those involving only passengers and because trucks are increasingly equipped with communication devices and their drivers can be easily warned that they are entering unsafe conditions. The broader impacts of the proposed research include improvements in traffic safety, the technical training of graduate student researchers and outreach to local high schools with significant under-represented populations.

运输系统信息技术的进步导致积累了大量关于运输系统状况的原始数据。 为了充分利用这些数据，必须开发新的工具来联合收割机并有效地分析大型数据库。 拟议的研究涉及应用非线性多变量分析方法来分析组合的卡车交通，详细的交通流量，事故和环境数据，以确定在不同的交通条件下，不同类型的网络链接的类型的事故的可能性混合的卡车交通的影响。 了解卡车事故的复杂因素，可以提供干预机会，以提高安全性。 主要的分析方法是非线性典型相关分析与多组混合分类，顺序和数值变量。这种特征值法，通过交替最小二乘算法实现，其特征在于非线性变量的最佳缩放和结果的图形解释。 该项目有四个不同的阶段：（1）建立适合于识别城市高速公路上的卡车安全问题的交通流和碰撞信息的综合数据库（2）通过特定类型的多元非线性模型识别在特定交通流条件下卡车交通的混合具有最不利的安全影响的高速公路位置和时间段，（3）确定在有问题的时空情况下提高安全性的方法。(4)确定如何将我们的研究应用于其他州的可用数据。总之，这项工作将开发工具，以帮助识别不安全的交通状况，从而避免事故。 我们专注于卡车事故，因为这些事故往往比那些只涉及乘客的事故更严重，因为卡车越来越多地配备了通信设备，司机可以很容易地警告他们正在进入不安全的条件。拟议研究的更广泛影响包括改善交通安全、研究生研究人员的技术培训以及与代表性严重不足的当地高中的联系。