Spatial identification of hazardous segments on rural highways considering the interaction weather-pavement surface conditions using PKDE and NKDE

The objectives of this study are to present a methodology for identifying high-crash density zones using geographical information systems and apply a kernel density estimation (KDE) for determining most hazardous road segments. Moreover, we connect weather information with the crash analysis to demonstrate the interactions between adverse weather conditions and the surface pavement condition (friction and texture) and apply their relationship to the crash frequencies. The motivation of this study was to identify mainly local regions where most crashes occur and to conduct interventions there, thus improving the safety of the studied segment of the highway. To develop this methodology and conduct the analysis, data of crashes of 7 years (2009–2015) occurring on a heavy-duty Brazilian highway were used. The findings demonstrate that when data of total crashes (e.g., rear-end collision, side-impact collision, sideswipe collision, head on collision, and rollover) are compared with those of skidding crashes, which are generally caused by wet pavements, the critical sections are essentially the same. This indicates that the wet pavement is a significant factor influencing crash occurrences. Therefore, solutions for asphaltic resurfacing that increase friction and aims to improve drainage have the potential to decrease the number of crashes. This study presents a spatial perspective to research on delineating hazardous road segments and the complex issue of how they can be measured.