Identification of road traffic crashes hotspots on an intercity expressway in India using geospatial techniques

Abstract

Ascertaining the underlying pattern of road traffic crashes (RTCs) and identifying hotspots is essential for improving safety on the road network. Researchers have employed various statistical modelling and spatial methods to predict crash frequency and identify their hotspots on the road network. In India, the road network length has been increasing, especially the expressway network length. The increase in the network length has also increased RTCs. Hence, it is essential to assess the crash pattern and identify hotspots on the intercity expressways in India. This study aims to identify the fatal crash hotspots on the selected intercity expressway using geospatial methods. First, in this study, hotspot sections were identified using ordinary kriging (OK) and, kernel density estimation (KDE), network kernel density estimation (NKDE) methods. Next, the employed techniques were compared to know their predictive effectiveness in identifying the hotspots. The study used the fatal crash data from August 2012 to October 2018 for the selected 165 km intercity expressway. Outcomes of the geospatial methods revealed some of the common hotspots are identified by both methods. The comparative analysis indicated that the NKDE method is more effective in identifying the hotspots in smaller segments than the other two methods. Consequently, this research's outcomes would facilitate intercity expressway-owning agencies to select a practical and readily applicable hotspot identification methodology in LMICs.