Free-floating (e-)moped sharing systems are becoming increasingly popular and provide new ways of travel in urban areas. The vehicles offer the flexibility of one-way transportation, though the downside for service providers of free-floating shared fleet systems is that their operations may result in a spatial-temporal imbalance of the vehicle distribution within the system. The potential effectiveness of user-based relocation strategies depend on the trade-offs users make between (reduced) ride fees and (prolonged) walking times. It is therefore essential to establish the trade-offs exercised by users in the context of moped sharing systems. We elicit travel preferences and assess how vehicle choice is influenced by user characteristics, travel context and alternative-specific attributes by means of a choice experiment. Our findings indicate that respondents in the Netherlands require a reduction of €0.02 of the ride fee for each additional walking minute required for accessing a vehicle. The application of our model demonstrates that vehicle availability, pricing, trip characteristics, and socioeconomic factors significantly drive adoption rates, emphasising the importance of understanding user preferences and thereby the factors shaping the acceptance and utilisation of e-moped sharing services. These findings offer essential implications for policymakers and operators, enabling them to tailor e-moped sharing services to diverse user segments as well as understanding the impact of policies, such as helmet mandates. The outcomes presented in this paper are likely to be applicable to other vehicle sharing systems with comparable design and management configurations.

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Arguably, nearly all real-world decisions, including travel choices, are inherently associated with subjective uncertainty where decision-makers’ personal evaluations play a significant role. In public transport networks, uncertainty due to waiting time and, recently, the COVID-19 pandemic possibly induce the most frustration and anxiety. Therefore, with the overarching aim of making public transport a viable and satisfying option, this thesis is dedicated to modelling and analysing the impact of such pervasive uncertainty on public transport travellers’ route choice behaviour.@en

With a few exceptions, public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are now likely to adapt their behaviour with a focus on factors that contribute to the risk of COVID-19 transmission. Given the unprecedented spatial and temporal scale of this crisis, these changes in behaviour may even be sustained after the pandemic. To evaluate travellers’ behaviour in public transport networks during these times and assess how they will respond to future changes in the pandemic, we conduct a stated choice experiment with train travellers in the Netherlands at the end of the first infection wave. We specifically assess behaviour related to three criteria affecting the risk of COVID-19 transmission: (i) crowding, (ii) exposure duration, and (iii) prevalent infection rate. Observed choices are analysed using a latent class choice model which reveals two, nearly equally sized latent traveller segments: ‘COVID Conscious’ and ‘Infection Indifferent’. The former has a significantly higher valuation of crowding, accepting, on average 8.75 min extra waiting time (average total travel time in the choice scenarios was about 40 min) to reduce one person on-board. Moreover, this class indicates a strong desire to sit without anybody in the neighbouring seat and is quite sensitive to changes in the prevalent infection rate. By contrast, the Infection Indifferent class has a value of crowding (1.04 waiting time minutes/person) that is only slightly higher than pre-pandemic estimates and is relatively unaffected by infection rates. We find that older and female travellers are more likely to be COVD Conscious while those reporting to use the trains more frequently during the pandemic tend to be Infection Indifferent. Further analysis also reveals differences between the two segments in attitudes towards the pandemic and self-reported rule-following behaviour. We believe that the behavioural insights from this study will not only contribute to better demand forecasting for service planning but will also inform public transport policy decisions aimed at curbing the shift to private modes.

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Although waiting times are inherently uncertain in public transport networks, previous research has primarily studied route choice behaviour under risk. Since understanding such behaviour is important to correctly model network flows and gain better understanding of traveller satisfaction, we propose a method to assess travellers’ route choice behaviour under natural ambiguity. Specifically, we devise a realistic route choice situation whereby travellers’ attitudes and perceptions towards waiting time uncertainty as well as the effects of situational contexts thereon can be quantified in terms of a certainty equivalent. The choice situation is contextualised in a stated preferences experiment to analyse the premium travellers in the Dutch railways are willing to pay for certainty in waiting time. Results indicate a significantly improved model fit and predictive value when accounting for waiting time uncertainty with travellers, on average, willing to trade-off more than 7 minutes of in-vehicle time to have certainty in their waiting time. Minor non-linear effects of elapsed waiting time and anticipated delays on the value of certainty are also found and heterogeneity analysis indicates that younger travellers tend to seek more certainty. The proposed method provides snapshots of travellers’ behaviour under uncertainties in a real-world public transport system and can as such be used to improve transportation models, provide more tailored travel advice, and be used to test the efficacy of different policies.

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Governments worldwide are aiming to increase sustainable mode use to increase sustainability, livability, and accessibility. Integration of bicycle and transit can increase catchment areas of transit compared with walking and thus provide better competition to non-sustainable modes. To achieve this, effective measures have to be designed that require a better understanding of the factors influencing access mode and station choice. At the national/regional level this has been thoroughly studied, but there is a knowledge gap at the urban level. This study aims to investigate which factors influence the joint decision for tram access mode and tram station choice. The joint investigation can identify trade-offs between the access and transit journeys. Furthermore, the effect of each factor on the bicycle catchment area is investigated. Using data from tram travelers in The Hague, Netherlands, a joint simultaneous discrete choice model is estimated. Generally, walking is preferred to cycling. The findings of this study suggest that access distance is one of the main factors for explaining the choice, where walking distance is weighted 2.1 times cycling distance. Frequent cyclists are more likely also to cycle to the tram station, whereas frequent tram users are less inclined to cycle. Bicycle parking facilities increase the cycling catchment area by 234 m. The transit journey time has the largest impact on the catchment area of cyclists. Improvements to the system, such as fewer stops, higher frequency (like light rail transit), or both, therefore would result in a much longer accepted cycling distance.@en

Identifying the set of alternatives from which travellers choose their routes is a crucial step in estimation and application of route choice models. These models are necessary for the prediction of network flows that are vital for the planning of public transport networks. However, choice set identification is typically difficult because while selected routes are observed, those considered are not. Approaches proposed in literature are not completely satisfactory, either lacking transferability across networks (observation-driven methods) or requiring strong assumptions regarding traveller behaviour (uncalibrated choice set generation methodologies (CSGM)). Therefore, this study proposes a constrained enumeration CSGM that applies the non-compensatory decision model, elimination-by-Aspects, for choice set formation. Subjective assumptions of traveller preferences are avoided by calibrating the decision model using observed route choice behaviour from smart card data, which is becoming increasingly available in public transport systems around the world. The calibration procedure also returns two key insights regarding choice set formation behaviour: (i) the ranking of different attributes by their importance, and (ii) the acceptable detours for each attribute. To demonstrate the methodology and investigate choice set formation behaviour, the tram and bus networks of The Hague, Netherlands are used as a case study.

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The aim of this paper is to extend past research on travel behavior analysis by investigating travelers’ emotions and perceptions of the system’s performance. Perceived travel happiness as an extension of travel satisfaction is researched in the framework of the decision-making process during traveling. Socio-demographic, cognitive, and affective data were collected from a questionnaire survey that took place in Athens (Greece), the Netherlands, and Barcelona and Salamanca (Spain). A Bayesian network was developed to investigate the interrelations between travel happiness and parameters that affect travel behavior. Findings revealed that travel mode choice directly affects the level of happiness that a person experiences during everyday trips. Moreover, travel happiness is directly associated with the traveler’s perception of the occurrence of disruptions during everyday trips and the level of tolerance he/she has toward such disruptions. Results also indicated that further research should focus on understanding how the topology and performance of each country’s system affect travel-related choices. Finally, a discussion of the most significant results is provided.

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As the number of people working in office buildings increases, there is an urgent need to improve building services, such as lighting and temperature control, within these buildings to increase energy efficiency and well-being of occupants. A pedestrian behaviour model that simulates office occupants’ movements and locations can provide the high spatial and temporal resolution data required for the testing, evaluation, and optimization of these control systems. However, since most studies in pedestrian research focus on modelling specific actions at the operational level or target situations where movement schedules do not have to modelled, a pedestrian behaviour model that can simulate complex situations over long time periods is missing. Therefore, this paper proposes a tactical level model to generate occupant movement patterns in office buildings. The Markov-chain activity-based model proposed here is data parsimonious, flexible in accepting different levels of information, and can produce high resolution output. The mathematical properties of the methodology are analyzed to understand their impact on the final results. Finally, the tactical level pedestrian behaviour model is face validated using a case study of an imaginary office with a simple layout.@en

Several cities around the world are facing mobility related problems such as traffic congestion and air pollution. Although limited individually, the combination of bicycle and transit offers speed and accessibility that can compete with automobiles by complementing each other's characteristics. Recognising the potential benefits with regard to accessibility, health, and sustainability, several studies have investigated policies that encourage integration of these modes. However, the actual users and trips of the combined bicycle and transit mode have not been extensively studied empirically. This study addresses this gap by (i) reviewing empirical findings on related modes, (ii) deriving user and trip characteristics of the combined bicycle and transit mode in the Netherlands, and (iii) applying latent class cluster analysis to discover prototypical users based on their socio-demographic attributes. Most trips by this combined mode are found to be for relatively long commutes where transit is in the form of trains, and bicycle and walking are access and egress modes respectively. Furthermore, seven user groups are identified and their travel behaviour is discussed. Transport authorities may use these empirical results to further streamline integration of bicycle and transit for its largest users as well as to tailor policies to attract more travellers.

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Transit is vital to the functioning of most major cities in the world today and therefore evaluation of the robustness-capacity to withstand disruptions with minimal impact to the system-of transit networks is essential. In this paper, we study the spatial extent of link disruption impacts in urban public transport networks (PTNs) due to spill-over effects that occur as affected passengers choose alternative routes. Quantifying link criticality in terms of its spatial propagation effects is important for prioritizing robustness measures as well as devising methods to encapsulate disruption spill-over effects. To this end, a new local criticality indicator-spatial criticality-that is based on: (i) the magnitude of relative change in load on other links due to closure of a link and (ii) the topological distance between these other links and the disrupted link is introduced. Further, a stochastic user equilibrium, PTN assignment model is developed. The model explicitly considers the service layer of the PTNs and accounts for on-board crowding and denied boarding at stops to represent passenger spill-over effects. Finally, to demonstrate the proposed indicator and investigate its relationship with conventional, local topological indicators, the urban rail-bound network of Amsterdam is used as a case study.

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