Abstract
There are several safety questions associated with operating passenger trains and freight trains on shared-use rail corridors (SRCs). Among them are adjacent track accidents (ATA) in which derailed railroad equipment intrudes upon (“fouls”) adjacent tracks and is struck by another train on adjacent tracks. ATAs can occur in any multiple track territory, but they become more complex and potentially more hazardous on SRCs. ATAs can be broken down into three principal events: the initial derailment, an intrusion, and a train present on an adjacent track. Previous research established a foundation for addressing intrusion risk by qualitatively identifying the risk and potential mitigation measures and conducting preliminary quantitative intrusion probability analysis; however, a gap remains between current research on intrusion risk and a comprehensive risk assessment model for ATAs. This paper presents an index-based, semi-quantitative risk analysis framework to evaluate probability and consequence of ATAs. A new risk index system was developed to evaluate ATA risk by assigning levels of probability to the three principal events and the overall ATA consequence level to different track segments, thereby enabling comparison of relative ATA risk among these track segments. The levels of ATA probability and consequence are determined by various infrastructure, rolling stock, and operational factors identified in this research where each factor contributes risk scores that can be summed and converted to levels of probability and consequence. The magnitude of risk due to each factor is determined by their effect, i.e., whether it increases or decreases the probability and/or consequence. A case study based on a 320-km, modified real-world SRC is presented to demonstrate and validate the model. Higher operating speed, lack of containment or barriers, and higher initial derailment rate all significantly affect ATA risk. The model enables comparisons of the relative ATA risks among different track segments at a resolution not previously achieved. It can also be used to locate high-risk
locations (risk hotspots) on a railroad corridor where ATA risk is high. This model also provides information pertinent to future improvements in quantification of ATA risk and research on mitigation measures.