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

交通系统信息技术的进步积累了大量有关交通系统状态的原始数据。 为了充分利用这些数据，必须开发新工具来组合和有效分析大型数据库。 所提出的研究涉及应用非线性多变量分析方法来分析组合卡车交通、详细交通流、事故和环境数据，以便确定卡车交通混合对不同交通条件下和不同类型网络链路上不同类型的事故可能性的影响。 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。 主要分析方法是多组混合分类变量、序数变量和数值变量的非线性典型相关分析。这种特征值方法通过交替最小二乘算法实现，其特点是非线性变量的最佳缩放和结果的图形解释。 该项目分为四个不同的阶段：(1) 建立一个适合识别城市高速公路上卡车安全问题的交通流和碰撞信息综合数据库 (2) 通过特定类型的多元非线性模型识别高速公路位置和时间段，其中特定交通流条件下卡车交通的混合会产生最不利的安全影响，(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。总而言之，这项工作将开发工具来帮助识别不安全的交通状况，从而避免事故。 我们重点关注涉及卡车的事故，因为这些事故往往比仅涉及乘客的事故更为严重，而且卡车越来越多地配备通信设备，并且可以很容易地向驾驶员发出警告，表明他们正在进入不安全的状况。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向人口严重不足的当地高中进行推广。

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.