In order to stop climate change, it is inevitable to transition to more environmental-friendly ways of transportation, such as cycling. An important criterion for selecting the bicycle as a modality is the distance to the destination. An increase in travel distance coincides with a decrease in modal share of bicycles compared to alternative modalities. Literature does not explain why cycling is preferred for certain distances and not for others, except for some practical reasons. This thesis investigates the built environment in relation to cycling distance. Literature indicates the built environment as being important for destination choice, mode choice and route choice. Moreover, literature also indicates a relationship between cycling distance and these decision choices. As urbanization of the built environment increases, more destinations are within reach and this tends to reduce travel distance. At the same time, network friction reduces due to increasing network density. Finally, preferences to cycle through or avoid certain characteristics of the built environment may result in detours, which increase travel distance. People’s decisions on travel destination, transportation mode and route appear to be crucial for choosing the bicycle as mode of transport. Humans make these decisions simultaneously, but in this thesis, it is assumed that it is done in the order: destination, mode, and route. By making this assumption, it is possible to say that (a) destination choice is connected to Euclidean distance, (b) mode choice is connected to network friction, and (c) route choice is connected to behavioural detour. The 7D’s framework of Ewing & Cevero (2010) has been used to quantify the built environment. It will turn out that only the first three D’s (Design, Density and Diversity) are influencing cycling distance. The choices for destination, mode and route choice are also influenced by the following elements: network density, cycle paths, green environments, smooth surface, built environment density and the mixture of functions in the built environment. This research has been conducted with bicycle trip data within the municipalities of Amsterdam, Amstelveen, Diemen and Ouder-Amstel. Those municipalities offer a fitting case study, since the ‘Greater Amsterdam’ has a varied built environment and has a need to reduce car traffic significantly. In this thesis, the relation of the elements with cycling distance are statistically tested with a multiple linear regression analysis. In order to do so, elements have to been quantified and combined with the bicycle trips. Explanatory models are developed to explain cycling distance, Euclidean distance, and detour distance with the elements of the built environment. The models constructed in this thesis show that the elements green environments, waterbodies, smooth surface material, and cycle paths have a positive increasing impact on cycling distance, while network density and built environment have a negative decreasing impact. No clear impact on travel distance is found for the element mixture of functions. This thesis also show that the built environment is more important for determining the Euclidean distance than its importance detour distance. The results from this thesis confirm the relationship between built environment and cycling distance. To be specific, the results show that the built environment density is most important for Euclidean distance and therefore destination choice. Green environments and waterbodies are incentive elements of the built environment to overcome the distance added by the network friction. The two elements can also affect the cycling distance due to the choice of behavioural detour. This shows that bicycle use for longer distances can be stimulated by adding more green and potential waterbodies in the built environment.

This research aims at analyzing route choice behavior of E-bikers, in terms of which factors play a role and to what extent. The research is performed based on E-bikers route data in Ghent. Two interesting developments are aimed to include as well, in terms of multi-attribute cost functions as input for route generation and including land use attributes to cyclist’s route choice models.

Recently many bicycle sharing programs have been introduced in cities worldwide. The combined use of shared bicycles and public transport could offer an attractive alternative for private motorised vehicles and could contribute to making cities better accessible and liveable. However, it is unsure whether this combination, especially with urban public transport (bus/tram), is often used or that shared bicycles function more as a substitute of urban public transport. Therefore, this study explores to what extent people use the shared bicycle in combination with urban public transport within a mid-size city, The Hague, in the Netherlands and how the integration between these modalities could be improved. This is examined by analysing the operational trip data of the bicycle sharing program called HTM-fiets, conducting a survey among the users of this bicycle sharing program and performing an expert meeting. The results indicate that in this case the shared bicycle does not complement urban public transport to a large extent in a single ride and seems more used as a substitute of urban public transport. Therefore, it is probably more valuable to focus on integrating the shared bicycle with urban public transport as a whole than to assure a good integration in a single ride. To realise the integrated public transport and shared bicycle system, the shared bicycle could be marketed as a form of public transport and the shared bicycle could be made available in areas with weak urban public transport connections to increase the total network and with that the accessibility in cities.

The discussion involving BRT and LRT is well alive among policymakers, who often rely on personal preferences or, as it is called in literature, on “blind commitment” towards one system or the other for their decision-making. The reason for choosing a system according to personal taste is also caused by the unknown preferences that public transport users have for the two systems. It is known from literature that there exists a “rail bonus”, but it is not clear to what extent this applies to LRT compared to BRT. Moreover, it is not known what are the causes behind the rail bonus black-box. This study wants to put some light on the dilemma by testing the existence of an “LRT bonus” and trying to explain it by analysing perceptions and how these relate to each other. Moreover, the study tries to understand how policymakers perceive BRT and LRT and how the decision-making should be done.

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.

The increasing urbanisation leads to more traffic movements in cities. Since car traffic in particular has a negative impact on the livability in the city, an alternative for car travel should be investigated to discourage car use. A possible alternative for car use could be the integrated bicycle-transit mode. Although more and more research is being conducted on shared bicycle systems and the combination with public transport, there is still no clear approach to design such an integrated system. The aim of this research was to develop a design approach for bicycle-transit, and subsequently this approach was applied to a case study. First, a spatial multi-criteria analysis was used to find suitable locations for shared bicycle docking stations. Secondly, an assessment framework was drawn up to determine the indicators on the basis of which different network designs were assessed. Finally, traveller flows have been investigated to analyse several alternative network designs. From this research, it was concluded that the optimal network for the case study Binckhorst in The Hague consists of four shared bicycle stations and a tram line between the train stations Den Haag CS and Voorburg.

Cities are growing worldwide, which leads to an increase in trips in urban areas. In Europe, more than half of the trips are made by car, while car takes most space of all modalities. Of all modalities car has the highest CO2 emissions. Thehigh number of trips by car within cities lead to challenges related to the accessibility, livability, and sustainability of cities. Sustainable mode alternatives in this research are the bicycle, shared bicycle, and urban public transport whichhave potential for being an attractive alternative on short trips (<5 kilometer). Therefore, this study researches factors that influence car users’ mode choice towards those three sustainable modes, for stand-alone trips with trip purpose shopping. Through a stated preference survey amongst car drivers in a Dutch mid-sized city (N=360), preferences are gathered with respect to the mode choice. A panel mixed logit model with error component and interaction variables is used for the analysis of the stated preference data. The factors with most impact on mode choice are (shared) bicycle travel time, bicycle parking costs, shared bicycle availability, public transport travel costs, and public transport in-vehicle crowdedness. The non-mode factor with most impact is attitude towards tram, which is negatively correlated to car frequency. Car users tend to switch towards public transport, so a guaranteed seat in public transport, a more positive attitude towards public transport and higher car parking costs can achieve the switch. The switch towards bicycle can be made if the bicycle travel time is lower than currently.

The COVID-19 pandemic has had a substantial impact on public transportation. With ridership figures decreasing, it has brought a new sense of urgency to the old problem of crowding. Using a structured design approach, this paper presents the results of a project which set out to reduce crowding in Dutch train stations by absorbing it at the network level. The design which is detailed in this paper uses advance communication of bike parking availability and price incentives on shared bikes as means to stimulate travellers to access or egress the railway system through alternative, uncrowded stations. It is determined that, theoretically, up to 7% of daily travellers in the Amsterdam region might use the system, suggesting that effects on station capacity would be substantial high adoption levels are realised.

The bicycle-transit combination is researched through a revealed preference survey in The Hague, applied on the tram network. Two main things are examined: which factors affect feeder distance and which feeder mode choice. This is done by bivariate analyses, multivariate analyses (two logistic regression models) and a qualitative analysis (choice motives that the respondents provided, and a spatial analysis).

Research into the bidirectional relationships between travel behaviour, attitude and risk perception during the COVID-19 pandemic. Using travel data obtained by the NS Panel, a cross-lagged panel model is constructed to analyse the causation in the three predictors for car and train travel.