Rural, Isolated, Tribal, or Indigenous (RITI) communities across the United States are disadvantaged from a transportation safety perspective. Particular concern is focusing on rural road safety. Official data from Federal Highway Administration (FHWA) shows that, in 2012, 54 percent of all fatalities occurred on rural roads while only 19 percent of the US population lived in rural communities. The fatality rate was 2.4 times higher in rural areas than in urban areas (1.81 and 0.74, respectively). Since RITI communities often do not have the capability and resources to sufficiently solve roadway safety problems, several challenges are encountered for addressing transportation safety issues in RITI communities, including: (1) Crashes are often randomly distributed on local and rural roads in RITI areas; (2) Strategies to address safety issues are diverse for different RITI communities and draw from several safety areas. As a result, there is a critical need to realize equitably-augmented safety solutions that address the needs of these underserved and underinvested RITI communities. A survey made by National Association of Counties (NACo) in 2009 revealed that only 42 percent of counties surveyed maintained a database that tracks the number and types of crashes on their rural roads and slightly under half of the respondents have conducted a road safety audit. Till recently, existing databases are still incomplete for most of the RITI communities. It is necessary to develop a complete safety database system for RITI communities. The aim of this research is to conduct a two-year project to build up an effective safety database platform to facilitate data partitioning and visualization for each RITI community in the first year (Phase I) and develop a data-driven safety assessment framework based on the safety database platform to enable effective roadway safety management for RITI communities in Washington State in the second year (Phase II).

In our Year 1 CSET project, a baseline data platform was developed by integrating the collected safety related data for the RITI communities in Washington State. However, the developed baseline data only integrates general rural roadway safety data without classifying the data into groups for each RITI community. Thus, a data partitioning and visualization-based safety database platform is critically needed for realizing effective roadway safety management for RITI communities. The safety assessment framework is the cornerstone of the roadway safety management system. Due to different RITI communities have different safety data sources, a general safety assessment method may not be adapted to all the RITI communities. To provide context sensitive solutions, the roadway safety cultural factors such as local driving habit and training level will be considered. As a result, this project is going to build up a complete safety database with classified data for each RITI community and develop a data-driven safety assessment framework based on the safety database platform to enable an effective roadway safety management system for RITI communities which can mitigate rural crash severities and risks.