Identification Of Potential Road Accident Hot Spots Using Geospatial Techniques A Case Study Of Thika Superhighway

Abstract

Road safety is a matter that does not receive the attention it so requires especially in Kenya. However, according to World Health Organization (WHO), Global Status Report on Road Safety 2018, deaths from road traffic crash have been on the rise and are reported to be about 1.35 million every year worldwide. Traditional approaches to road safety have focussed on road crash history where safety measures are put in place after gathering statistics from accident occurrences. This does little to prevent an increase in deaths and serious injuries since the data is collected after accidents have already occurred. Therefore, there is an urgent requirement to develop a holistic approach that can predict potential road accident hot spots in order to save lives and enhance road safety. This study therefore focused on identifying high accident risk areas while predicting potential road accident hot spots through use of geospatial models and techniques. The study explored the use of various models including; speed model (designated speed), curve models (horizontal and vertical) and an integrated model using GIS in order to predict road accident hot spots of the study area i.e Thika Superhighway in Nairobi Metropolitan Region. All this was achieved by acquiring and analysing high resolution satellite images, road center-line data, slope analysis and digital elevation models (DEM) of the study area. The geospatial high accident risk prediction model was compared with the existing crash data (NTSA) for the study area for the past two (2) years for validation. This comparison showed consistency of results especially in the area between Kasarani and Githurai roundabouts on Thika Super Highway and Juja area, Kiambu County. It was found out that most road crashes occur at intersections and undesirable curves which are hot spots for loss of control type of accidents. The study achieved its overall objective of preparing a geospatial high accident risk prediction model that can be used to identify potential road accident hot spots. The geospatial high accident risk prediction model however has limited capability to identify road accident hotspots in areas with straight road profile. Incorporation of road design data coupled with operating speeds data from roads authority can improve the overall performance of this model which can then be replicated by road safety authorities, road engineers and transportation planners for other roads of national importance and ultimately help save lives.