Critical Review on: Identify Hazardous Road Location & Interprit by ArcGIS

Abstract

India’s share of road accidents in the world is an area of serious concern. Road traffic accidents have been recognized as one of those adverse elements which contribute to the suffocation of economic growth in the developing countries, due to the heavy loss related to them, hence causing social and economic concern. Traffic safety is an important key and plays an integral role in sustainable transportation development areas. Nowadays, the main negative impact of modern road transportation systems are loss of property injuries and deaths in road accidents. The success of traffic safety and highway improvement programs hinges on the analysis of accurate and reliable traffic accident data. Thesis applied the combination of geo-information technology and spatial statistical analysis to bring-out the influence of spatial factors in their formation. With advancement in technology, new and sophisticated models of vehicle are available and their numbers are increasing day by day. Development of a model for traffic accident density estimation explained by GIS data can enable the determination of dangerous areas objectively and easily, indicating where area-wide traffic calming can be implemented preferentially .A traffic accident has multi-facet characteristics associated with it. For proper traffic accident analysis use of GIS technique has become an inevitable tool. This Study examined the relations between traffic accidents and city characteristics, such as population, road factors, and spatial factors. A model was developed to estimate traffic accident density. Kernel density estimation (KDE) techniques were used to assess the relations efficiently. In general, the location of traffic accidents is described as an address with text, so they are difficult to display on the map. Thesis concern with how to utilize the GIS to work-out on Spatial Analyst to record the traffic accidents. Thesis workout in presenting the method which takes the potential of reducing accidents as an index to extract the black spots