Urbanization, a desire for a reduction of CO2 and a need for a more efficient use of space are likely to greatly increase demand for rail travel in the coming decades. To improve railway capacity, extra infrastructure can be built, or existing track can be optimized. A silo analogy provides an interesting idea for an integral approach to network development that combines the demand of heavy-rail and bus, tram, and metro: either obtain more demand for the heavy-rail system and thus validate expensive infrastructural investments or reduce demand. Both options require a broadening of the analysis so that it includes bus, tram, and metro networks. To explore the practical implications of this analogy, the research question is set up as follows:
“Does integrated public transport network development in the form of a simultaneous consideration for infrastructural investment in both heavy-rail and hierarchically lower public transport modes offer a better solution for fulfilling transportation needs, compared to a segregated approach?”
To answer the research question, literature review has been used to gain insights in public transport integration from a technical and a governance perspective. Literature has also been used to form integration strategies that could be applied in network development. The formed strategies consist of using network hierarchy as a guideline for either applying bus, tram, and metro to take over lower hierarchy services from the heavy-rail and vice versa. A case study on the corridor Amsterdam – Lelystad in The Netherlands was used to apply the integration strategies on and to analyze the effects of the development following the different strategies in a real-life situation.
The results show that the variant following the strategy of assigning lower hierarchy services to heavy-rail appears best in terms of generalized travel time, synthetic demand, and investment costs. It scores well on fairness of the offer, but lowest on ambition fulfillment and operational costs. It turned out that each variant needed investment in more heavy-rail infrastructure to allow for completing of the heavy-rail ambitions. The variant with more tasks assigned to bus, tram, and metro scored the worst, except for ambition fulfillment. Furthermore, the multicriteria analysis, although useful for providing insights in the qualities of the different variants, does not give a definitive answer on what the best variant would be. Using external input to assign weights to the multicriteria analyses would have been a good improvement in getting to that answer. Instead, a stakeholder reflection is done to give insight in possible stances regarding the criteria from the stakeholder’s perspective.
The research suggests that it is worth considering ambitions and problems outside of just the bus, tram, and metro or just the heavy-rail system. The process of brainstorming based on expertise, using qualitative multicriteria analyses for making decisions with supporting arguments in the design phase, planning a plausible timetable and analyzing and comparing several variants forms a good skeleton for investigating the possibilities of public transport network development in places where heavy-rail capacity becomes problematic.

If people repeat choices in daily travel behaviour under stable conditions, over time this can form habitual travel behaviour and lead to inertia. If travellers face a new metro line, to what extent does their habitual travel behaviour affect their willingness to use this new transport alternative. This study investigate the inertia effects in a multimodal public transport network using a fully revealed preference data set. Habitual route choice behaviour is quantified prior to the opening of the new metro line. We then use the quantitative metric for habitual behaviour as an indicator in the situation after the metro line opens to reflect the inertia effects of individuals. The outcome of the route discrete choice model reveal that the inertia effects for the new metro line are significant. The interaction effect of habitual travellers with the new transport alternative show that there is an additional effect for the new alternative if travellers are classified as habitual. Furthermore, not accounting for inertia effects leads to bias in travel demand estimations of the new transport alternative. The model with the inertia effect improves model fit and is able to more accurately.

In this thesis, the functioning of passenger delay-based refund schemes in urban public transportation settings is evaluated, such as metros or light-rails. These are used when a passenger encounters a delay on their trip, in order to grant a form of refund to them. To the best of knowledge, no research has been carried out yet on several aspects of these refund schemes, such as which design considerations can be taken into account upon establishing one, or how their performance can be evaluated. To clarify these issues, the state of the practice is reviewed by surveying the internet and the information that is being provided by the individual public transport operators. By categorising this information, it can be established what the design aspects of these refund schemes are. These are the delay type such as departure or arrival delay, the delay threshold, i.e., the minimum delay from which a trip taken is elegible for a refund, the refund type, which is the form of refund that the passenger receives, and the possibility for alternative transport to be refunded. In order to determine whether there could be a form of linearity between delays and refunds, and to facilitate the evaluation of different refund schemes, a regression analysis was carried out on approximately a year of passenger trip data and vehicle data from the Washington Metro. This enabled acquiring more reliable, i.e., excluding individual passenger’s possibly erroneous behaviour, observations of the departure delay and arrival delay of every trip taken during this study period. Using this data, a possible link between the total amounts of delays encountered and refunds granted per day was analysed. It could be concluded that there is indeed a strong linear effect between these two. Furthermore, a number of key performance indicators (KPI’s) was established which allowed to assess the performance of a set of synthetic refund schemes, in which the parameters were varied that resulted from the aforementioned survey. Furthermore, a refund scheme was recreated that uses the general punctuality on a line and subsequently refunds all travelers that can have been expected to be effected by a possible drop in this punctuality. Using these KPI’s, conclusions could be drawn on the performance of different refund schemes, in the sense of how many refunds per trip are granted, how the ratio of the average delay of all refunded trips vs. the average delay of all trips is, how many revenue losses the refund schemes yield for the operator, and how much of the time losses (using a certain Value of Time) are compensated for.