Sander Van Cranenburgh

Sander Van Cranenburgh
Delft University of Technology | TU · Faculty of Technology, Policy and Management

PhD

About

43
Publications
7,577
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
665
Citations

Publications

Publications (43)
Article
Full-text available
Characteristics of urban space (co-)determine human behaviour, including their social interaction patterns. However, despite numerous studies that have examined how the urban space impacts social interactions, their relationships are still poorly understood. Recent developments in computer vision and machine learning fields offer promising new ways...
Article
Full-text available
Regionalization is the process of aggregating contiguous spatial units to form areas that are homogeneous with respect to one or a set of variables. It is useful when studying spatial phenomena or when designing region-based policies, as it allows to unravel the latent spatial structure of a dataset. However, this task is challenging when small-sca...
Article
We examine identifiability and distinguishability in Decision Field Theory (DFT) models and highlight pitfalls and how to avoid them. In the past literature, the models’ parameters have been put forward as being able to capture the psychological processes in a decision maker’s mind during deliberation. DFT models have been widely used to analyse hu...
Article
Full-text available
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 sust...
Article
Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems an...
Article
Full-text available
This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covari...
Article
Full-text available
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 lar...
Article
Artificial Neural Networks (ANNs) are rapidly gaining popularity in transportation research in general and travel demand analysis in particular. While ANNs typically outperform conventional methods in terms of predictive performance, they suffer from limited explainability. That is, it is very difficult to assess whether or not particular predictio...
Preprint
Full-text available
Public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are likely to adapt their behaviour to avoid the risk of transmission and these changes 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 fut...
Preprint
Full-text available
Public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are likely to adapt their behaviour to avoid the risk of transmission and these changes 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 fut...
Article
Full-text available
Fully Automated Vehicles (AVs) have been widely expected to revolutionise the future travel experience. Recent studies have shown that their impact may also reach beyond the travel episode, and lead their users to alter other activities performed during the day – their daily lifestyles. This study is among the first to empirically investigate the c...
Preprint
Full-text available
Since its inception, the choice modelling field has been dominated by theory-driven models. The recent emergence and growing popularity of machine learning models offer an alternative data-driven approach. Machine learning models, techniques and practices could help overcome problems and limitations of the current theory-driven modelling paradigm,...
Article
Full-text available
Theories of decision-making are routinely based on the notion that decision-makers choose alternatives which align with their underlying preferences—and hence that their preferences can be inferred from their choices. In some situations, however, a decision-maker may wish to hide his or her preferences from an onlooker. This paper argues that such...
Article
Although Automated vehicles (AVs) are expected to have a major and positive effect on road safety, recent accidents caused by AVs tend to generate a powerful negative impact on the public opinion regarding safety aspects of AVs. Triggered by such incidents, many experts and policy makers now believe that paradoxically, safety perceptions may well p...
Article
Full-text available
Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis, being appreciated in particular for their strong predictive power. However, many choice modellers are critical – and rightfully so – about using ANNs, for the reason that they are hard to diagnose. That is, for analysts it is hard to see whether a trained (estimat...
Article
In this paper we propose the concept of ‘substitutability’, which we define as the extent to which the preferred travel alternative can be substituted by other initially less preferred alternatives. This is particularly of interest when the preferred alternative is no longer available, e.g. due to labour strikes, weather conditions, power failures,...
Article
Full-text available
Environmental effects of transport projects have a weak position in Cost-Benefit Analysis (CBA) which might be rooted in the valuation approach adopted in the dominant style of CBA. This conventional valuation approach has been criticized for not valuing positive and negative impacts of transport projects in relation to each other and for not valui...
Chapter
Full-text available
The Value-of-Travel-Time (VTT) expresses travel time gains into monetary benefits. In the field of transport, this measure plays a decisive role in the Cost-Benefit Analyses of transport policies and infrastructure projects as well as in travel demand modelling. Traditionally, theory-driven discrete choice models are used to infer the VTT distribut...
Article
At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret minimisat...
Article
Many experts believe the transport system is about to change dramatically. This change is due to so-called fully-automated vehicles (AVs). However, at present, there are numerous important knowledge gaps that need to be solved for the successful integration of AVs in our transport systems, in particular regarding the impacts of AVs on travel demand...
Article
This study develops a novel Artificial Neural Network (ANN) based approach to investigate decision rule heterogeneity amongst travellers. This complements earlier work on decision rule heterogeneity based on Latent Class discrete choice models. We train our ANN to recognise the choice patterns of four distinct decision rules: Random Utility Maximis...
Article
Full-text available
Automated Vehicles (AVs) are expected to allow their users to engage in a broad range of non-driving activities while travelling, such as working, sleeping, playing games. The impact of this possibility on the satisfaction with travel and on travel demand has been extensively discussed in the literature. However, it has been hardly recognised that...
Article
Full-text available
Cost-benefit analyses for transportation projects usually value impacts on safety and travel time through experiments in which consumers of mobility ('drivers') choose between routes which differ in safety and travel time. This approach has been criticized for failing to consider that private choices may not fully reflect citizens' preferences over...
Article
Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis. But, at present, it is unknown what sample size requirements are appropriate when using ANNs in this particular context. This paper fills this knowledge gap: we empirically establish a rule-of-thumb for ANN-based discrete choice analysis based on analyses of synth...
Article
Due to the surge in the amount of data that are being collected, analysts are increasingly faced with very large data sets. Estimation of sophisticated discrete choice models (such as Mixed Logit models) based on these typically large data sets can be computationally burdensome, or even infeasible. Hitherto, analysts tried to overcome these computa...
Article
We present a methodology to derive efficient designs for Stated Choice (SC) experiments based on Random Regret Minimisation (RRM) behavioural assumptions. This complements earlier work on the design of efficient SC experiments based on Random Utility Maximisation (RUM) models. Capitalizing on this methodology, and using both analytical derivations...
Article
This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive) utility maxi...
Article
Full-text available
This special issue presents seven selected papers from the 5th Symposium of the European Association of Research in Transportation (hEART) which was held in September 2016 in Delft, The Netherlands, and was organised by Delft University of Technology. The contributions cover a wide range of topics in transportation, reflecting the broad scope of th...
Conference Paper
Full-text available
It is widely believed that fully-automated vehicles (AVs) will be part of transportation systems in the foreseeable future. Technological developments are rapidly speeding up their adoption as an increasing number of car manufacturers and research centers join efforts to develop AVs. However, at present, there are numerous important knowledge gaps...
Article
Full-text available
A recent paper published in this journal compares two regret based choice models, and concludes that one of them is theoretically inferior and has a worse empirical performance in the context of a particular data set [Rasouli and Timmermans, Transportation 6:1–22, 2016]. Unfortunately, those conclusions are ill-founded as they are based on a misint...
Article
Full-text available
This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive) utility maxi...
Article
Full-text available
Transport policy decisions often involve a trade-off between travel time and safety. Transport economists generally evaluate the societal value of transport policy options involving travel time versus safety trade-offs in a Cost-Benefit Analysis (CBA) through multiplying the expected change in traffic casualties with the value of a statistical life...
Article
This paper presents a method to infer a Willingness to Pay (WtP) distribution based on a sample of observations of individuals who pay a particular price for a particular quality increase. Crucially, no observations are available of individuals who reject a higher price/higher quality proposition, and choose a lower price/lower quality alternative...
Conference Paper
In this study we extend recent work into the role of Weber's law in discrete choice theory towards non-regret based models of (travel) choice behavior; and we provide an empirical exploration of the relevance of Weber's law in the context of utility-and regret-based models of travel behavior. Being one of the most well-known regularities in the Soc...
Article
Full-text available
This study presents empirical insights into Dutch citizens' preferences for spatial equality in the context of decision-making regarding the composition of a national transport investment plan. To the best of our knowledge, our study is the first study worldwide which empirically investigates citizens' preferences for the spatial distribution of be...
Article
Full-text available
Recent empirical evidence suggests that travellers are becoming increasingly multimodal. Coinciding with this trend, a growing interest can be observed in the transport literature to study the concept of multimodality. Most studies, in this regard, have focused on assessing the determinants of multimodal travel behaviour. While it is interesting to...
Article
Full-text available
This paper investigates vacationers’ short-term responses to a sharp increase in transport costs. It aims to (1) acquire an understanding of the relative popularity of the different types of responses among vacationers and (2) explore whether there are distinct market segments of vacationers that respond differently to a sharp increase in transport...
Article
As discrete choice models may be misspecified, it is crucial for choice modellers to have knowledge on the robustness of their modelling outcomes towards misspecification. This study investigates the robustness of Random Regret Minimization (RRM) modelling outcomes towards one sort of model misspecification: the omission of relevant attributes. We...
Article
This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination w...
Article
Aviation carbon taxes have occasionally been debated as a measure to curb aviation carbon dioxide (CO2) emissions. This paper presents a simulation study on the effects of high aviation carbon taxes on tourism and its related CO2 emissions. The paper investigates the scenario in which, as a result of high aviation carbon taxes, air fares increase b...
Article
This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximizati...
Article
This paper investigates vacation behaviour under high travel cost conditions. We estimate discrete portfolio vacation choice models on data obtained in a novel free format Stated Preference of Revealed Preference (SP-off-RP) choice experiment. The substantive contribution of this paper is that we develop new insights into vacation behaviour under h...
Article
During the past 40 years, mobility patterns have enduringly changed several times as a result of the occurrence of a number “substantial changes”. Examples of such substantial changes are the rapid emergence of affordable air travel, the oil crises, and profound ICT developments. To most researchers and policy-makers in transportation, it seems mor...

Network

Cited By

Projects

Project (1)
Project
STAD studies the Spatial and Transport impacts of Automated Driving, The STAD project is funded by the Dutch National Science Foundation NWO as part of the Smart Urban Regions of the Future Program. It is conducted by a 30 party consortium lead by TU Delft.