Disruptions occur frequently in railway networks, requiring adjustments to the timetable, rolling stock planning and crew planning while causing delays and cancellations. Although the evolu­tion of system performance during a disruption can be visualized in the resilience curve, not much is known about performance during disruptions or the extent to which the curve applies in practice. The limited quantitative knowledge about the resilience of railway networks makes it hard to design appropriate recovery measures. In this thesis, a data-driven evaluation approach is presented to make an ex post assessment of the resilience of railway networks. Several resilience metrics are extracted from literature and two new resilience metrics are introduced. Using historical traffic realization data, resilience curves are reconstructed for a large and heterogeneous set of single disruptions and are quantified in terms of the resilience metrics. Among others, the values of the resilience metrics are compared across disruptions of different causes using Welch’s ANOVA and the Games-Howell test. The approach is applied to a case study of the Dutch railway network, with a focus on the five most common disruption causes. The results of the case study show that there is significant heterogeneity in the shape of the resilience curve, even within disruptions of the same cause. Train defects are found to be the least impactful disruptions on multiple resilience metrics, while collisions are found to be the most impactful disruptions on multiple resilience metrics. The successful application of the approach shows that it can be used by practitioners to assess which types and which parts of disruptions deserve attention to improve disruption management practices, and thus, improve resilience.

Traffic congestion and undeveloped public transport define the modus operandi in Kampala, the capital city of Uganda. Most people see motorcycles as a solution to escape the growing traffic congestion and poor public transport. This study thus aimed to determine the traffic efficiency and safety effectiveness of introducing motorcycle dedicated lanes in non–lane based and mixed traffic. These effects were studied by microsimulation of unsignalized intersections without priority markings in urban areas of developing countries. The study centered on an intersection commonly known as “Spear Motors” in Kampala. In addition, off-peak traffic was simulated since traffic police controls the intersection during peak time and microsimulation model cannot simulate traffic while a policeman (or two) is on duty. Video images of the intersection were officially obtained from Uganda Police and processed to traffic data such as traffic volume, trajectories, composition, and conflicts. In addition, geometric data (lane width, slope, intersection dimensions, coordinates, and aerial pictures) about the intersection was physically collected during a daytime site visit. A representation of the current traffic conditions at the intersection, referred to as the base model, was developed in VISSIM 21. The base model was calibrated using Particle Swarm Optimization (PSO). Thereafter, the model was microscopically and macroscopically validated. Three scenarios were considered in this study, which are: scenario 1 with straight crossings of motorcycles in the intersection, scenario 2 with deflected crossings and scenario 3 with a roundabout separated for the main road traffic and motorcycles. Simulations with driver behavior parameters set to optimum values were performed to replicate the actual behavior. Consequently, traffic conflicts were generated from vehicle trajectories per scenario. Post Encroachment Time (PET) and Time to Collision (TTC) were calculated for the conflicts. The use of conflicts is proactive compared to reactive approaches that use crashes. The use of deflected crossings for motorcycles and then a roundabout separated for main traffic and motorcycles increased the flow of the intersection by 31% and 20%, respectively. Additionally, the implementation of deflected motorcycle crossings and roundabout reduced critical density by 38.6% and 63.7%, respectively. Regarding safety, more benefits are expected by using either deflected crossings or roundabout than straight crossings. The number of severe critical conflicts reduced by 87.9% with the application of a roundabout separated for main road traffic and motorcycles. This is higher than the decrease of 48.5% after the application of dedicated motorcycle lanes with deflected crossings. To determine the conflict rates per scenario, the number of severe critical conflicts was divided by total traffic simulated. Deflected crossings for motorcycles reduced severe critical conflict rate by 40% whilst it decreased by 75% with intersection improvement to a roundabout separated for main traffic and motorcycles. With the high motorcycle demand of over 50% of traffic, implementation of dedicated motorcycle lanes at unsignalized intersections as well as urban roads in general will result into better flow of traffic. In addition, the motorcycle lanes will contribute to the general road safety in Kampala.

E-bikes have gained global popularity due to their environmentally friendly and sustainable attributes, as well as their ability to provide fast speeds and power assistance. However, the increasing popularity of e-bikes has introduced conflicts and crashes with other road users, especially the interaction with conventional bikes, as e-bikes and bikes share the same cycling infrastructure. To study the perceived risk of interaction with e-bikes, this research delves into the various factors, in terms of traffic environment, bicycle type, and individual factors. This research has used the video-based survey method to measure the perceived risk of cyclists and presented traffic environment and bicycle types in hypothetical traffic scenarios with pre-recorded videos. A questionnaire is designed to investigate participants` perceived risk of hypothetical bicycle interactions and their personal information on demographics, cycling experience, competence of riding skills, cycling behaviors, and expectancy on e-bikes. Moreover, the perceived risk is measured by two items separately: the rate of perceived risk and the likelihood of being involved in a crash. After recruitment and data collection, the effects of various factors have been estimated by the random-effects ordered logit model. Results implied that compared to interaction between conventional bikes, riding on an e-bike when encountering a conventional bike decreased the perceived risk. Moreover, riding at peak hours and having a conflict with left-turning cyclists was found to increase the perceived risk, while riding at a large intersection was found to decrease the perceived risk. In terms of individual factors, the experience of bicycle crashes and the preference to use an e-bike were found positively related to the perceived risk. While the competence of cycling skills and age were found negatively associated with perceived risk. Additionally, it was found that traffic environment factors were more prominent in predicting the rate of perceived risk, while individual factors had a stronger influence on predicting the likelihood of being involved in a crash. These findings provide insights into the influence of e-bikes in shared cycling spaces, underscore the importance of traffic management and safety measures in crowded bicycle traffic, and emphasize the significance of intervention and educational initiatives for cyclists to decrease the perceived risk and enhance cycling safety.