Long-distance / International travel has seen little attention in the past, largely due to the irregular and sporadic nature of such trips. And yet, a single long-distance trip can amount to a distance equivalent to a year’s worth of commute trips, resulting in a similar, if not worse, environmental footprint. Understanding travellers’ behaviour is therefore just as relevant for such trips, as it is for everyday commute trips. As international travel is slowly picking up from the COVID-19 pandemic, it has been marred by an abundance of national and regional pandemic-related safety measures. While their primary goal is to protect the local population from infection, these safety may also make travellers feel safer while travelling. This perceived safety can differ from the true efficacy of the measures. In this research, we investigate people’s perception of eight COVID-19-related safety measures related to long-distance trips and how subjective perception of safety impacts their mode choice among car, train and aircraft. We employ a Hierarchical Information Integration (HII) approach to capture subjective perceptions and then model the obtained data by means of a Latent Class Choice Model, resulting in four distinct segments. To extrapolate the segments onto the rating experiment of HII, we apply a weighted least squares (WLS) regression, to obtain segment-specific safety perception. Two segments show a relatively high value-of-time (72€/h and 50€/h), tend to be more mode-agnostic and prefer determining the level of risk by themselves (relying primarily on infection and vaccination rate). The remaining two segments have a lower value-of-time (38€/h and 15€/h) and have strong mode affinity, for the train and car respectively. Future research could look into a way that segments the sample based on both the mode choice and rating experiment, providing additional insights into the heterogeneity of individuals in their perceptions.@en

Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference survey in August 2021, yielding 936 valid responses. The respondents were faced with static pre-trip information on the expected waiting time, followed by the actually experienced waiting time for their selected alternative. We analyse this data by means of an instance-based learning (IBL) approach to evaluate how individuals respond to service performance variation and how this impacts their future decisions. Different novel specifications of memory fading, captured by the IBL approach, are tested to uncover which describes the user behaviour best. Additionally, existing and new specification of inertia (habit) are tested. Our model outcomes reveal that the perception of unexpected waiting time is within the expected range of 2–3 times the value-of-time. Travellers seem to place a higher reward on an early departure compared to a penalty for a late departure of equal magnitude. A cancelled service, after having made a booking, results in significant disutility for the passenger and a strong motivation to shift to a different provider. Considering memory decay, our results show that the most recent experience is by far the most relevant for the next decision, with memories fading quickly in importance. The role of inertia seems to gain importance with each additional consecutive choice for the same option, but then resetting back to zero following a shift in behaviour.

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This research aims to analyse the perception of covid-19 infection risk in long-distance travel in Europe and how it impacts mode choice and travel behaviour. We make use of an HII variant type experiment and model it by means of a latent class choice model, where we uncover four distinct user groups. For infection risk perception, we apply a novel approach in the field, utilising a weighted least squares regression, to obtain segment-specific regression functions, based on their respective probabilistic segment allocations. Some segments exhibit risk-aversion behaviour that is time-based (longer journeys perceived as more risky), whereas others see it as time-independent. With respect to modal preferences, the four segments either show a strong preference or aversion to one of the two land-based modes: car-loving, car-averse (using train or air), trainloving and train-averse (using car and air).@en

On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations.

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Neighbourhood mobility hubs may play an important role in mitigating the impact of passenger cars on climate change and urban public space. As a relatively new concept, academic research on the user potential of neighbourhood mobility hubs is so far limited. This research aims to identify which user groups are likely to adopt services offered by a neighbourhood mobility hub. A survey was distributed in the Netherlands (N=298), an Exploratory Factor Analysis (EFA) executed and a Latent Class Cluster Analysis (LCCA) estimated. Four distinctive groups of intended users are uncovered. Two of the clusters have intentions to use neighbourhood mobility hubs. Two other clusters do not (yet) intend to use neighbourhood mobility hubs. The clusters indicate that people who currently already travel more by sustainable modes (train or (e-)bicycle) are more likely to be adopters of neighbourhood mobility hubs than the traditional car users. In practice, this may limit the positive effect of hubs or even increase car use. However it could also facilitate those travelling sustainably to do so for longer as additional shared modes become available to them via hubs. Limitations and directions for further research are discussed.

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Public transport systems have been and continue to be shaped by disruptive forces, impacting individuals’ travel behaviour and how they interact with public transport. This thesis analyses the impact of disruptors on travel behaviour, the perception and use of public transport, enabling operators and policymakers to design appropriate measures and policies in order to improve the quality of service, the sustainability of transport and the liveability of our environment.@en

This paper uses stated preference data collected in the city of Rotterdam and discrete choice modelling techniques to study the relationship between public transport and shared micromobility. It assumes a hypothetical condition of integrated systems and studies the relationships of complement and competition between these modes. The findings suggest that shared micromobility modes are viable alternatives as egress modes for metro trips. Shared micromobility can be seen as a complement to metro, yet shared e-mopeds proved to also be a viable option as individual modes for long-distance trips. Different characteristics proved to be important in choices in this context: frequency of public transport use, previous use of shared micromobility, and age. Considering the results obtained, collaboration between shared micromobility and transit operators might benefit them as well as travellers. Collaborations should be designed so that they help travellers to decrease total travel time, even if it implies longer egress legs. However, the costs of these shared modes should not be as high as to prevent travellers to use them as egress alternatives. Finally, young travellers and frequent transit users could be specifically targeted, as they showed to have a better perception of shared micromobility.

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A plethora of shared fleet services have been introduced in cities worldwide. Despite their increased presence in urban areas, it is insofar unknown what are the main determinants of travellers’ choices between the usage of shared-mopeds and cars and thereby the extent to which shared moped can substitute car travel. To this end, we design and conduct an SP choice model experiment. The estimated Panel Mixed Logit model is applied to explore the potential market share for shared moped, car and bike under several scenarios which are devised based on the expert interviews. Our findings demonstrate that the return availability of shared moped is the most influential travel time attribute. Walk time from home to the shared moped is an influential factor for people without moped experience. Moreover, model estimation results show that people who have used a shared moped before value the attributes differently than people without previous moped experience. We specifically focus on choice determinants and policy measures targeting car users to facilitate desirable behavioural changes. We present results from model application to demonstrate the effect of different policy packages on the market share of each mode, showing that certain policy interventions can attract car users to switch to shared moped while avoiding a strong reduction in bike use.@en

Het verduurzamen van de mobiliteitssector is een essentieel onderdeel van de transitie naar een klimaat- en CO2-neutrale samenleving. Voor verplaatsingen die te lang zijn voor actieve vervoerwijzen (lopen, fietsen, …), is het openbaar vervoer over het algemeen het meest duurzame alternatief voor reizigers. Met een hoge capaciteit en efficiënt gebruik van middelen is het ideaal voor het vervoeren van grote aantallen mensen over langere afstanden. Een zwakte echter bij het gebruik van het openbaar vervoer is het bekende first/last mile-probleem: voor- en natransport naar en van de halte of station. Gedeelde micromobiliteit kan hier een oplossing bieden.@en

The Passenger Centre is a crossing point for commuters, day-trippers and international travellers and must therefore provide the best possible public transport services@en

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing they may actually increase total vehicle miles travelled, worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and on-demand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample, the Sharing-ready cyclists (55%), are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes, Tech-ready individuals (27%) and Flex-ready individuals (9%) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter is highly cost-sensitive, and would therefore use the service primarily if it is cheap. The fourth class, Flex-sceptic individuals (9%) shows very limited potential for using on-demand services.

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The integrated timetable concept was developed in Switzerland in the 1980s and is now also being introduced in Austria, Germany and Denmark. What is it and what could be gained?@en

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining a larger number of observations per respondent, allowing for reliably estimating choice models even with smaller samples. In SWDCE, respondents are asked to alternatingly select the ‘best’/‘worst’ alternatives, until the choice set is exhausted. The use of BWDCE raises the question of decision-rule consistency through the stages of the experiment. We challenge the notion that the same fully compensatory decision rule is utilised throughout the experiment. We hypothesize that respondents may utilise one decision rule for selecting the ‘best’ and another for selecting the ‘worst’ alternatives. To test our hypothesis, we developed a model that combines the SBWMNL model for modelling best-worst data and the μRRM model that can account for variations in decision rules. Our results show that decision-rule heterogeneity does seem to be present in BWDCE: it is more likely that ‘best’ choices are made using a fully compensatory decision rule (maximising utility), whereas ‘worst’ choices are more likely made using a non-compensatory decision rule (minimising regret). Such behaviour is largely similar to how image theory describes the decision-making process in complex situations. Our findings give choice modellers new insight into the behaviour of respondents in best-worst experiments and allows them to represent their behaviour more accurately.

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