Identification of black spots in urban roads and spatiotemporal patterns mining

Abstract

In order to identify black spots and their spatiotemporal evolution patterns in urban road networks accurately, rapidly and microcosmically, and to effectively reduce frequency of traffic accidents, a crash data mining method based on space-time cubes was proposed. Firstly, selection method of important parameters in construction of space-time cubes model was studied, and spatiotemporal black spots in urban road network were identified by "space-time cubes + cumulative frequency curve method". Then, their spatiotemporal dynamic evolution patterns were obtained based on emerging hot spot analysis method. Finally, the space-time cubes method was compared with quality control method, empirical Bayesian method and kernel density estimation from aspects of their advantages, disadvantages and application scopes. The results show that space-time cubes method and kernel density estimation are applicable to identification of black spots at the meso and micro scale while emerging hot spot analysis method can be utilized to obtain their spatiotemporal evolution patterns.