Identifying the emerging vulnerability of railway transport systems across countries by automated analysis of railway accident reports

Although railway accident reports and recommendations are proposed after railway accidents, practitioners and researchers suffer from the need to deal with a large amount of textual data given that most railway safety-related information is recorded and stored in the form of text. Hence, there is a growing need for accurate estimations of the vulnerability of railway transport and for effective mitigation strategies. This thesis extends knowledge on the vulnerability of the railway system by exploring the underlying hazards and building rigorous and automated models to enlarge the database. The conceptual frameworks HazardMap and RecoMap were developed to overcome this gap, using Natural Language Processing (NLP) topic models for the automated analysis of textual data to extract critical insights. Empirical data was retrieved from official railway accident reports published by four countries: Australia - the Australian Transport Safety Bureau (ATSB), the UK - Rail Accident Investigation Branch (RAIB), the US - National Transportation Safety Board (NTSB) and Canada - the Transportation Safety Board of Canada (TSB). Scoping workshops and a survey were conducted to evaluate the usefulness and consistency of railway practice. Case studies of the application to the risk at level crossings and the platform–train interface risks are provided to illustrate how the models proposed work with real-world data. The interpretation of findings indicates the potentially emerging hazard of deterioration in railway safety. Potential barriers to learning across jurisdictions and time might deteriorate the organisational safety culture and endanger railway. To address such obstacles, the HazardMap and RecoMap proposed are capable of automating hazard analysis with adequate accuracy to help stakeholders better understand hazards and help practitioners learn across jurisdictions and time

Analysis and Prediction of Disruptions in Metro Networks

Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin