Urban planning in the Global North has historically prioritised car infrastructure, leading to car-dependent cities with congestion and pollution. In recent years, especially in north-western Europe, there has been a policy shift toward healthier and more sustainable urban environments, with strategies such as reducing parking capacity gaining attention. While this measure can discourage car use and free up urban space for alternative purposes, its city-wide impacts—particularly on public transport and cycling—remain underexplored.

This thesis investigates how large-scale parking space removal affects travel behaviour in Rotterdam, a city with relatively high car use but suitable conditions for car-lite policies. Three intervention scenarios (20%, 40%, and 60% parking reductions) are analysed using the V-MRDH macroscopic, multimodal transport model. To address the model's limitations, a supplementary estimation method was developed to more realistically approximate shifts in transport demand.

Results show that a 20% reduction could lead to a peak-hour modal shift of up to 2,566 motorists, corresponding to increases of 2.8% in public transport and 2.2% in cycling. Although average loads remain within capacity, some tram and bus lines face overcrowding during the busiest 15 minutes. The findings highlight the need for strategic public transport planning and model enhancements to accurately assess behavioural responses to parking policy changes.

Travelers do not like transfers in their Public Transport journey; it gives a disutility. Minimizing this disutility is valuable to increase travelers' satisfaction. To be able to reduce this disutility, transfers have to be identified first. Multi-operator smart card datasets allow for the identification of transfers between different operators, as well as between the same operators. However, it is unknown in the literature how such a multi-operator smart card dataset can contribute to the minimization of transfer disutility in Public Transport. An answer is given by analyzing a multi-operator smart card dataset for the Haaglanden area in the Netherlands that uses a 35-minute time interval to identify transfers. Also, a measure has been implemented for one of the transfers with the highest potential for transfer time loss minimization and the effects on the network are examined in a transport model. It is found that a multi-operator smart card dataset can identify important transfer stations and individual transfers, for which the associated disutility factors can then be identified manually. Then, measures can be implemented to reduce the disutility of a transfer. The measure, a reduction of the waiting time, implemented on a transfer in this study resulted in changing traveler's route- and mode choices. For further research, it is recommended to explore the possible effects of implementing the measure as it can lead to crowding or other inefficient transfers, which could increase the disutility. Furthermore, further research should look into the disutility values of the case study to draw better conclusions whether, and to what degree, (dis)utility factors explain transfer flow sizes for this case specifically.

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 promises of Mobility-as-a-Service (MaaS) are generous, researchers and policymakers repeatedly present MaaS as a Holy Grail. MaaS has the potential to play a role in addressing issues as pollution, congestion and crowded cities. However, without interested users, these promises cannot be fulfilled. This study explores whether the proposed expectations of MaaS are realistic by examining the interest of potential users in MaaS. The focus is on employees in the Netherlands. Commuting trips might make an interesting first market for MaaS since there are possibilities to increase the interest of employees by financing commuting MaaS trips by employers. A latent class cluster analysis is performed to identify groups of employees based on the attitude towards and the intention to use MaaS for commuting trips. The results indicate that the majority of employees are not interested in using MaaS to commute. An increase in interest is seen when employers finance MaaS. However, this increase is not substantial. The interested groups of employees are individuals with high environmental consciousness and low car affection which often travel in a multi-modal way. This research concludes that the benefits of MaaS that are proposed by researchers and policymakers are overestimated. However, this does not imply that the MaaS concept should be discarded. The MaaS platform might be of added value for some travellers since it provides new travel options in case of unexpected disruptions and an easy and clear way of booking and paying for a door-to-door trip. For now, it is important to present MaaS in a more realistic way instead of the Holy Grail.

Due to connectivity problems, the attractiveness of public transport is limited. Policymakers aim to increase the modal share of public transport to protect the accessibility, livability, safety, sustainability and efficiency in the cities of the future. Applying Autonomous Mobility on-Demand (AMoD) systems as a feeder service for public transport hubs can improve the first- and last-mile trip leg, increasing the attractivity of public transport. It is essential for the implementation of AMoD systems to predict the impacts of varying operational strategies on beforehand. From an operators perspective, especially the financial viability of AMoD operations is vital and yet unclear. An existing gravity-based travel demand estimation model built in OmniTRANS is used to predict the AMoD passenger demand. Besides, an agent-based simulation model is developed using the software Anylogic that is connected to the demand-model as an add-on module to simulate the behavior of passengers and AMoD vehicles within an urban environment. The agent-based simulation model is applied to the case study Rotterdam-Zuid, where Station Zuidplein and Station Lombardijen function as an AMoD hub. The simulation outputs show that activating dynamic ridesharing using wireless fast chargers at the stations results in the most financially viable operation. Activating automatic relocation results in the most costly operation. Compared to existing public transport services, carsharing systems and taxi systems, the AMoD system shows to save a large amount of expenses due to the absence of drivers.

The population of The Netherlands is ageing. It is important to offer suitable transportation options for the elderly to keep them participating in social activities to prevent lonelines. This research investigated the preferences and usage of bus services of elderly in urban areas in The Netherlands and the corresponding heterogeneity. 256 elderly respondents were questioned by means of a stated preference survey. By using a multinomial logit model the values elderly place on the bus attributes were quantified. A latent class choice model was estimated to explore the heterogeneity and the nature of the heterogeneity in preferences among the elderly. Besides that, public transport card data from Groningen is used to compare theory with findings from practice. The research results show that elderly in the Netherlands can be divided into several classes with their own preferences. Not all elderly prefer a low distance to the nearest bus stop and most of the elderly prefer high-frequent bus options.