A railway accident prevention method based on reinforcement learning – Active preventive strategy by multi-modal data

Abstract

Railway systems are entering an era of highly intelligent automation where stability and safety are becoming increasingly important. However, there is still a lack of intelligent and effective ways for railway accident prevention, especially active accident prevention strategies. This paper presents a railway accident prevention method based on the reinforcement learning model and multi-modal data to achieve active railway accident prevention strategies. Three metrics are designed to show the performance of active prevention methods. Based on the three metrics and the data from Federal Railroad Administration, the effectiveness of the proposed method is verified in the case study by introducing two methods as baselines. The results also show that nearly 30% of accidents can be effectively prevented through active preventive measures with the proposed method. Finally, this paper analyzes the influence of personal skills on the proposed model and advises on improving railway safety based on the analysis of the results.