Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based round-trip bikesharing (SBRT) scheme located at train stations throughout the country. This scheme allows users to rent a bike to connect the train station and their destination. The round-trip characteristic SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. While on the latter a wide range of research exists, little research has been conducted on round-trip bikesharing, especially when being integrated into an existing public transport scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system OV-fiets using multiple linear regression (MLR). The results are compared with findings on one-way bikesharing schemes. The results are then used as an input to forecast short-term demand. To identify a best performing forecasting method, the statistical methods MLR and Prophet are compared with the neural-network based method Long Short-Term Memory (LSTM).
It is found that for hourly rentals in an SBRT-system, the highest explanatory power achieved with the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Further, the magnitude of the correlation between the determinants and the hourly demand differs across the stations in the system. For forecasting, the performance of the methods differs across the stations and forecasted periods due to the stations' distinct characteristics. But, especially in times of uncertainty, LSTM is likely to outperform the others due to it's capability of adapting to short-term changes in the demand.

Understanding pedestrian traffic is essential to design for safe and reliable pedestrian infrastructures, facilities and operations. For the assessment and design of pedestrian transfer areas at stations capacity standards for uni- and bidirectional flows are applied. It has been observed that, at railway stations, also other configurations of flows occur, such as intersecting flows at different angles (e.g. intersections between transfer areas and stairs/escalators or shops). In contrast to uni- and bidirectional flows, for which many researches have quantitatively studied the pedestrian flow characteristics, only little research has been done to intersecting flows. The purpose of this research is to gain insight into how the flow capacity of a pedestrian intersection at a railway station is influenced by several factors and to explore methods that can estimate the flow capacity of a pedestrian intersection. A theoretical framework has been constructed that shows the hypothesized causal relations between the research topic - intersection flow capacity - and several variables that describe the context of this study. Furthermore, three capacity estimation methods were proposed to estimate the flow capacity of a pedestrian intersection. According to the field study, the second and third method both seem suitable for capacity estimations, even though the intersection flow capacity (according to the traffic flow principles) could not be estimated in this study. Several recommendations have been made for future research, including points of improvement regarding the the studied methods, directions for research focus and the usability of the findings in this study.

There is a need to know flow capacities of common bottlenecks of cycle storages because an increasing number of large cycle storages are being designed. Flow capacity values are not available from literature nor is a suitable method to determine those values. This research contributes to filling these research gaps, by developing a method to measure capacity, and applying it to case studies. The method also offers a way to calculate the standard deviation of the flow, as well as a confidence interval for the capacity. This is possible because the flow is normally distributed. Knowing the variation of the flow, predictions can be made on how often a queue will emerge. The essence of the method is to measure in an event-based manner, discard the time at which there was no queue before the bottleneck, and then divide the remaining measurements into intervals of equal duration and treat those intervals as individual capacity measurements. The method has shown to yield a capacity value that is independent of the averaging interval. The standard deviation however, does not always vary as independent data from a normal distribution would. It is hypothesised that the autocorrelation could be used to correct for that. 95% confidence intervals for capacity were found as follows; check-in: 21.2 ± 1.2 (σ=3.5), stairs: 24.78 ±0.60 (σ=1.02).