A Review of Current Research and Future Developments in Field Modelling for Traffic Risk Analysis

Discovering the trend of risk changes and formulating risk prevention and control measures are important links in achieving proactive risk prevention and control. Constructing and analyzing field models can visualize the distribution and change of risks and formulate effective risk prevention and control measures. Based on the current situation and trend of field model research, this paper discusses its application in risk identification, aiming to improve the accuracy of risk avoidance. Firstly, different types of field models are classified, and their respective characteristics and application scenarios are introduced. Secondly, the shortcomings in the development of field models are summarised. Finally, in the field of autonomous driving and intelligent traffic management, it is proposed that the accuracy of the model can be improved by multi-scene data fusion, the dynamic response enhances the efficiency of risk avoidance, and the aspect of risk classification in complex environments to enhance the universality of the model provides new ideas for the further application of the field model in the field of intelligent traffic.