Abstract Pavement surface conditions have a strong positive effect on accident risks. Pavement surface distresses directly affect ride comfort and indirectly cause distraction to the driver resulting in loss of control of the vehicle, which may lead to injuries or deaths. The reason for the lack of research on contribution of bad pavement condition to traffic crashes maybe lies in the fact that previously the data of pavement condition are not readily available to researchers in traffic safety, or sometimes it is comparatively hard for researchers to get the systematic data of pavement condition to conduct analyses. The proposed research will take opportunity of current well-known databases such as the long-term pavement performance (LTPP) database and pavement management system (PMS) at state agencies, to conduct deep and systematic data mining on the existing pavement performance and traffic safety data using data-driven intelligence technologies, and develop predictive models in terms of pavement performance, material properties, traffic effects, and pavement maintenance plans. The research outcome will help guide highway agencies to better design, maintain, and manage pavement infrastructures with enhanced roadway safety.
Description Pavement surface conditions have a strong positive effect on accident risks. Pavement surface distresses directly affect ride comfort and indirectly cause distraction to the driver resulting in loss of control of the vehicle, which may lead to injuries or deaths. The reason for the lack of research on contribution of bad pavement condition to traffic crashes maybe lies in the fact that previously the data of pavement condition are not readily available to researchers in traffic safety, or sometimes it is comparatively hard for researchers to get the systematic data of pavement condition to conduct analyses. The proposed research will take opportunity of current well-known databases such as the long-term pavement performance (LTPP) database and pavement management system (PMS) at state agencies, to conduct deep and systematic data mining on the existing pavement performance and traffic safety data using data-driven intelligence technologies, and develop predictive models in terms of pavement performance, material properties, traffic effects, and pavement maintenance plans. The research outcome will help guide highway agencies to better design, maintain, and manage pavement infrastructures with enhanced roadway safety.
Objective The proposed research will address USDOT strategic goals such as a) Safety (primary strategic goal), b) Economic Strength and Global Competitiveness, c) Equity, and d) Climate and sustainability.
Impacts/Benefits The results of the project are expected to help transportation practitioners improve their decision strategies for geometric design and pavement maintenance such that crash rate can be reduced. It will also facilitate the revision and implementation of Highway Safety Manual analysis, improve conditions of transportation infrastructure, and reduce roadway accidents and fatalities. The research study will develop analysis tools for highway safety analysis as well as pavement design and management. While the primary analysis will be focused on the Midwest region of the Unite States, the research methodology can be extended to other regions of the nation.