Metro networks face operational challenges due to increasing ridership and system growth, particularly in managing delay propagation. Epidemiology models have recently been an interesting method in transportation research for studying delays. This study, therefore, aims to see if the Susceptible-infectious-susceptible (SIS) model is suitable to help model delay propagation in a metro network through its ability to reproduce the vulnerability of metro stations for specific instances. Using data from the Washington Metro Network, delay propagation instances were grouped, and the model was trained and tested using a differential evolution algorithm. The results indicate that the vulnerability values as calculated from the data do not follow the expected trend. Also, the model can predict the vulnerability values for the first group more accurately than the second group. However, limitations such as underestimation and overestimation of station vulnerabilities, and sensitivity to training data and parameters were observed. These challenges stemmed from the dynamics between specific parameters, the mismatch in the order of magnitude of model components, and the lack of additional factors.

This research presents a dynamic scheduling model designed to reduce extreme departure time deviations for freight train requests and distribute deviations more evenly across the planning horizon, as an improvement over the current first-come, first-served method. By integrating deterministic demand forecasting and congestion-based penalties into a dynamic scheduling algorithm, the model evaluates overall customer satisfaction after allocating all requests. A sensitivity analysis is conducted to identify the optimal balance between minimizing forecasted congestion and reducing current departure time deviations. The approach aims to achieve a scheduling strategy that minimizes extreme departure time deviations without disproportionately impacting overall time deviations. Results demonstrate the model's ability to reduce some extreme deviations in low-capacity network scenarios and distribute the deviations more evenly throughout the planning horizon, with minimal impact on overall time deviations. In high-capacity networks, improvements are negligible. The findings highlight the potential for targeted benefits under constrained conditions while underscoring the need for further refinement to ensure consistent and meaningful performance across diverse scenarios.

Disruptions in urban public transport networks significantly impact urban mobility, causing inconveniences for passengers, economic losses, and vulnerabilities in transit systems. Major disruptions may lead some passengers to permanently switch to alternative modes like cars, even after the disruptions resolve, potentially worsening congestion and environmental issues. While the immediate impact of disruptions on behavior has been extensively studied, there is limited research on the long-term effects, often restricted to qualitative methods. This study aims to address this gap by proposing a framework to investigate the prolonged effects of public transport disruptions on passenger travel behavior using smart card data. A mixture latent Markov model is used to track passenger behavior from the pre-disruption to the post-disruption period. This framework identifies travel patterns and tracks how passengers transition between these patterns over time, thus inferring the impact of disruptions on behavior. Each passenger is assigned a mobility style that reflects their general travel attitude, such as those who do not change their behavior. The results from our case study reveal that the impact of the disruption was not as substantial as anticipated, with a high proportion of passengers maintaining their behavior.

Despite being a long-cherished EU ambition and a crucial key to achieving climate goals, there is still no European High-Speed Rail (HSR) network, with the few completed projects often facing disappointing demand resulting in unprofitability. In order to gain insights to profitable network design, this study develops a new formulation to the “Transport Network Design & Frequency Setting Problem” (TNDFSP), as current literature lacks one that can optimally solve the problem for instances of this size while also accounting for demand elasticity. Our model works with elastic and dynamic HSR demand calculated from optimal fare settings and observed travel mode characteristics from competing alternatives. The optimal solution is largely insensitive to fare changes, and in some measures outperform current state-of-the-art. Our model considers the 111 most populous European cities, along with all their origin-destination (OD) pairs, and finds the most profitable network design within a reasonable solution time frame. The results show HSR can be very profitable in Europe, but only when concentrated around a selected group of the largest cities in the western part of the continent. Despite being profitable and contributing significantly to set Green Deal goals, the viability of our network heavily depends on the willingness of several countries to invest significantly in infrastructure construction.