Center for Safety Equity (CSET)

The Center for Safety Equity has conducted many projects to improve the safety condition of rural, isolated, tribal, and indigenous communities. These is a sever equity issue in these communities, as one is significantly more likely to be injured or killed in a traffic collision than in urban areas. Further, Native Americans are also more likely to be killed or injured in traffic collisions than caucasians. These issues compound, as many tribal reservations and lands are locaed in rural areas. These projects address this critical disparity by targeting the unique circumstances that precipitate these collisions. The UW STAR Lab has partenred with several tribal nations in th state of Washington to target the unique safety issues faced by the tribal communites. We have partered with the Confederated Tribes of Colville to develop a safety data collision database to collect, store and utilise the collisoin records for the reservation. This has allowed safety engieers to better understand the causes behind collisions and to develop countermeasures to address these collisions. Similarly, we have partered several times iwtht he Yakama Nation to further develop this safety technology. Building off of the safety database developed for the Conferdereated Tribes of Colville, we have developed a safety data visualization and analysis tool (pictured), which allows the tribe to both visualize where collisions are happening on their road network and to analyse those crashe dto extract the most dangerous sections of roadway that need the most attention. We have also foudn through our work wiht the tribes that dthe volume, quality, and accessability of data is lacking. To address this, we have partered with Yakama Nation to pilot a sensor installment, the MUST Sensor [[maybe reference this to somewhere else int he website that discussed the MUST in detail]], to collect critical safety data including vehicle counts, vehicle speeds, pedestrian counts, bicycle counts, roadway surface condtisons, weather conditions. These new datasources will be useful to identify the safety concerns faced by the community and to begin to implement safety countermeasures to addrss these issues and reduce roadway fatalities.

Figure 1. Safety Net for crash visualization on three aggregation levels: point-based, segment-based, and area-based.

Related Publication
Wang, Y., Sun, W., Ricord, S., Souza, C. M. D., Yin, S., & Tsai, M. J. (2021). Developing a Data-Driven Safety Assessment Framework for RITI Communities in Washington State [2021] (No. INE/CSET 21.09). University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET).

STAR Lab