Black Spot Analysis And Decision Support Research Of Urban Road Based On GIS

With the improvement of the level of urban mobility, urban road traffic safety problems are becoming more and more prominent, in the urban road to form a certain number of road traffic accidents. Therefore, it is an effective method to identify the multiple links quickly and accurately, and it is found that the road accident black spot is an important link to reduce the number of road accidents and ensure the safety of life and property of the residents.First of all, the multiple point identification of the accident can be combined with the firefly clustering algorithm and GIS. On the one hand, the advantage of GIS in the management and analysis of spatial objects, on the other hand, the characteristics of the road traffic accidents are abstracted by using the firefly clustering algorithm. Due to the spatial characteristics of the accident, the clustering of road traffic accidents, different from the general clustering, standard glowworm swarm optimization algorithm, the Euclidean distance metric in the space distance calculation problems, with based on GIS road network distance calculation to improve the insufficiency, improve the clustering accuracy. The feasibility of the model algorithm is verified by an example of data analysis.Secondly, descriptive analysis of the target area of urban road traffic accidents in different time, different roads, different weather, different road linear index distribution characteristics and using the principal component analysis method to analyze the key factors of regional urban road traffic accidents, and the distribution features and key factor as the theoretical basis for the proposed suggestions of how to prevent the traffic accident prone (section) and corrective measures.Finally, based on the previous theoretical research results, using GIS development platform, the paper builds a road black spot analysis and decision support system. Through the system, the accident spatial distribution, distribution characteristics and other conditions of visual display, and the use of computer automated identification of accident prone sections of the method, greatly improving the ability of accident information analysis.