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

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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.