Impacts of the Enhanced Train Control (ETC) System on Train Operators in Canadian Railways

Abstract

Canada's rail transportation system is a vital part of the economy, ensuring the efficient movement of goods and passengers. However, maintaining safety and efficiency remains a challenge, as the system relies on human operators, making it susceptible to human errors. The Transportation Safety Board of Canada (TSB) reported an average of 35 missed signal incidents per year between 2004 and 2021, underscoring the associated risks. To enhance railway safety, the TSB and the 2018 Railway Safety Act Review Panel recommended implementing a train control system similar to Positive Train Control (PTC) in the U.S., the European Train Control System (ETCS), and the Chinese Train Control System (CTCS). Transport Canada is now developing a vision for Enhanced Train Control (ETC), though it is not yet federally regulated. A Notice of Intent in the Canada Gazette, Part I, outlines a risk-prioritization approach for ETC’s implementation. The introduction of train control systems, however, raises concerns about the potential for suboptimal workload levels for train operators, which could degrade both operator and system performance. As a result, mental workload assessments must be integrated into system design. While workload models have been widely applied in aviation and nuclear power fields, their use in railway operations — primarily through analytical methods —remains limited. Moreover, existing studies have mainly focused on static mental workload predictions, neglecting the dynamic cognitive demands that train operators face throughout a journey. This research explores vulnerabilities in train control systems by examining their impact on train operators' mental workload within Canadian Railways. To achieve this, it begins with a review of systematic accident analysis methods, such as the Human Factors Analysis and Classification System (HFACS) and Functional Resonance Analysis Method (FRAM), establishing a systemic approach to evaluating complex train control systems and gaining a deeper understanding of accident dynamics in railways. Subsequently, data on Passing a Stop Signal (PASS) occurrences in Canadian railways, along with weather and geospatial information, were collected, integrated, and analyzed using XGBoost and SHAP machine learning methods. A binary classification model was developed to predict PASS occurrences based on relevant features, while SHAP analysis identified and ranked key non-human contributing factors, assessing their collective impact on missed signals. The findings indicate that track geometry, environmental conditions, and operational factors significantly increase the risk of PASS occurrences in freight trains on Canadian mainlines. Specifically, sharp track curvatures before signals, downhill gradients, low temperatures, high humidity, and low atmospheric pressure were found to be major contributing factors. These elements interact in complex, non-linear ways, affecting signal visibility, stopping distances, and brake performance, ultimately increasing the likelihood of passing stop signals. Comparing the XGBoost model with Logistic Regression highlighted XGBoost’s superior ability to capture these intricate non-linear interactions, reinforcing its effectiveness in modelling complex dependencies. These insights guided the development of safety measures to mitigate PASS incidents and contributed to refining workload measurement through human performance shaping factors. The core of this thesis introduces a hybrid workload measurement method that integrates VACP analysis, fuzzy set theory, and SPAR-H to enhance the traditional VACP method and assess workload across various train control automation levels and real-world settings. Applied across Canadian rail routes under various train control systems and operational conditions, the model revealed that automation does not always reduce mental workload. Compared to highly automated systems, low and Intermediate levels of automation were found to increase workload, particularly when compounded by challenging track and weather conditions. Workload also varies along routes based on environmental, operational, and infrastructural factors; the lower the automation level, the more external conditions impact operators. Based on the findings, safety recommendations were provided, including human factors requirements, system integration, alarm management, and training programs. This research enhances our understanding of the complex relationship between train control systems, contextual factors, and train operators' mental workload. It introduces a novel analytical mental workload assessment methodology, establishes a baseline for evaluating ETC’s impact on operators, promotes a common framework among stakeholders, and expands the field's knowledge. These findings benefit safety professionals, policymakers, and regulators by supporting the development of evidence-based policies and proactive safety measures for train control implementation