Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman

Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy healthcare. There is little systematic understanding of the geography RTCs spatial correlations in Middle-East region, particularly Oman where leading disability-adjusted life years lost. The overarching goal this paper to evaluate temporal dimensions, identifying high risk areas or hot-zones more frequent, using geocoded data from Muscat governorate. This study based drawn Royal Police (ROP) sample iMAAP database National Traffic Crash (NRTC) database, managed by ROP made available for research use Research Council Sultanate Oman. covered period 1st January 2010 2nd November 2014. Only occurred Governorate were included study. 12,438 registered incidents, however, due disconnections found road network, disconnected parts removed final analysis considered only 9,357 incidents. We an adjacency network integrating GIS RTC robust estimation techniques including: Kernel Density Estimation (KDE) both 1-D 2-D space Network-based Nearest Neighbour Distance (Net-NND), K-Function, Random Forest Algorithm (RF) spatiotemporal Hot-zone analysis. highlight evidence clustering recurrence long roads demarcated intersections roundabouts Muscat. findings confirm that elevate than other effects attributed geometry features. results application NRTC validated generated database. provide statistical our hypothesis (roundabouts, crosses bridges) represent higher causing geometric also demonstrate quantitative spatio-temporal patterns associated with crash over different locations networks More importantly, clearly pinpoint importance influence related features