GIS-based traffic safety system in 2D and 3D views: network analysis, detecting hazardous locations, risk factors and predicting safe routes

Road traffic crashes (RTCs) continue to be one of the top global killers, especially among children and young adults, and they require a data-driven approach to improve road safety. This study presents two novel algorithms: Babylon network kernel density estimation (BAB_NETKDE) and predicting safe routes for drivers in Babylon (BAB_PSRD), designed to analyze spatial and temporal traffic data for enhanced accident risk assessment and effective route optimization in Babylon, Iraq. BAB_NETKDE calculates a severity index for high-risk road segments by analyzing the extent of property damage, serious injuries, and fatalities resulting from road accidents in a specific area, utilizing real accident data through network analysis. BAB_PSRD aims to improve real-time route suggestions to drivers by considering available situational road safety factors and not necessarily based on traditional shortest-path formulations. Various outcomes signify that these models help to cover the gaps in applying the heat maps and call on kernel density estimations (KDE) in analyzing the most perilous areas of roads and estimating risks of accidents more accurately. This study presents a network-contributed approach that offers a more realistic and dynamic assessment of the risk of an accident than the traditional spatial approaches. The real-time traffic assignment scheme provides a prospect to elaborate and lifelike estimations of accident severity and a series of potential uses to initiate safety intervention schemes. Further work will focus on algorithm streamlining and exploiting other geographic territories with real-time and time-constrained traffic information.