Accident Hotspot Detection and Spatial Analysis

One of the public safety issues is road traffic accidents. They contribute to a lot of loss of life and cause economic burden in most regions of the world. In this paper, the author will recommend an integrated strategy involving the use of Geographic Information Systems and machine learning to identify accident hotspots. The fundamental objective is to enhance safety in the roads by finding out the high-risk areas and the space time trends of the accident. In this, we will employ statistical spatial models such as KDE and Moran own I, clustering algorithms, such as DBSCAN and Hierarchical Agglomerative Clustering. Such approaches will enable us to determine the areas that are likely to crash. Through the open datasets provided by the government and spatial visualization, we seek to understand the manner in which accidents occur and what contribute to it. Through our theoretical experiment, it is revealed that the GIS clustering model is effective in detecting hazardous areas. This will assist the traffic management authorities to make sound decisions towards proactive safety measures.