Identification of High-Risk Driving Behavior and Sections for Rail Systems

Abstract

Risk assessment is an important process for railway safety. Current practices for assessing the risks of driving behaviors aim to inspect the driving record generated by automatic train protection systems. This paper proposes an automatic process to access detailed data contained in driving data, and identifies six high-risk driving behaviors. The modules can assess the competency of drivers and evaluate the frequency of high-risk behaviors in each section. Moreover, an integrated risk index for driving behaviors is proposed to compare each driver and section. An empirical study for drivers and sections is performed to demonstrate the feasibility of applying the proposed modules in practice. Results reveal that 20% of high-risk drivers contribute to 74% of the total risk, while 15% of high-risk sections contribute to 80% of the total risk. The proposed modules identify the drivers and sections with high risk. By enabling the operators of railway systems to take countermeasures, this methodology could enable them to improve the safety of railway systems more efficiently.