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

运输系统信息技术的进步导致了大量关于运输系统状况的原始数据的积累。为了充分利用这些数据，必须开发新的工具来组合和有效地分析大型数据库。提出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，以确定不同交通条件和不同类型的网络链路上卡车交通混合对不同类型事故可能性的影响。了解围绕卡车事故的复杂因素，可以为加强安全干预提供机会。主要的分析方法是包含多组混合的范畴变量、序数变量和数值变量的非线性典型相关分析。该特征值法通过交替最小二乘算法实现，其特点是非线性变量的最优标度和结果的图形化解释。该项目分为四个不同的阶段：(1)建立一个全面的交通流量和碰撞信息数据库，适合于确定城市快速路上的卡车安全问题(2)通过特定类型的多变量非线性模型，确定在特定交通流条件下卡车交通的混合对安全影响最不利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)找出将我们的研究应用于其他州现有数据的方法。总而言之，这项工作将开发工具，帮助识别不安全的交通状况，从而避免事故发生。我们关注涉及卡车的事故，因为这些事故往往比只涉及乘客的事故更严重，而且卡车越来越多地配备通信设备，司机可以很容易地得到警告，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，对研究生研究人员进行技术培训，以及推广到人口比例严重偏低的当地高中。

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.