Overview of the identification of traffic accident-prone locations driven by big data

Abstract

Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards. This paper first systematically compares the research in Chinese and foreign literature, and proposes three types of identification indicators, namely absolute, relative and comprehensive, according to different reference standards. According to the evaluation indicators and modelling methods, the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics, cluster analysis, machine learning and conflict technology. The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management, while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation. Driven by big data, the identification of traffic accident-prone points has been further developed at the meso-micro scale. Morphological image processing methods are widely used, combined with GIS platforms, to accurately mine the spatial attributes and correlations of accidents. Also, considering the spatial and temporal distribution of accidents, the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.

Graphical abstract

Keywords

Traffic safety / Accident-prone locations / Review / Data mining / Mesoscale