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Sander van Cranenburgh
Sander van Cranenburgh
Associate professor, Delft University of Technology
Verified email at tudelft.nl - Homepage
Title
Cited by
Cited by
Year
Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis
A Alwosheel, S van Cranenburgh, CG Chorus
Journal of choice modelling 28, 167-182, 2018
2922018
On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preferenceá…
GH de Almeida Correia, E Looff, S van Cranenburgh, M Snelder, ...
Transportation Research Part A: Policy and Practice 119, 359-382, 2019
1682019
New insights on random regret minimization models
S van Cranenburgh, CA Guevara, CG Chorus
Transportation Research Part A: Policy and Practice 74, 91-109, 2015
1272015
Random regret minimization for consumer choice modeling: Assessment of empirical evidence
C Chorus, S van Cranenburgh, T Dekker
Journal of Business Research 67 (11), 2428-2436, 2014
1012014
How will automated vehicles shape users’ daily activities? Insights from focus groups with commuters in the Netherlands
B Pudāne, M Rataj, EJE Molin, N Mouter, S van Cranenburgh, CG Chorus
Transportation Research Part D: Transport and Environment 71, 222-235, 2019
1002019
Choice modelling in the age of machine learning-discussion paper
S van Cranenburgh, S Wang, A Vij, F Pereira, J Walker
Journal of Choice Modelling 42, 100340, 2022
70*2022
An artificial neural network based approach to investigate travellers’ decision rules
S van Cranenburgh, A Alwosheel
Transportation Research Part C: Emerging Technologies 98, 152-166, 2019
692019
Vacation behaviour under high travel cost conditions–A stated preference of revealed preference approach
S Van Cranenburgh, CG Chorus, B van Wee
Tourism Management 43, 105-118, 2014
562014
Do individuals have different preferences as consumer and citizen? The trade-off between travel time and safety
N Mouter, S Van Cranenburgh, B Van Wee
Transportation research part A: policy and practice 106, 333-349, 2017
522017
An empirical assessment of Dutch citizens' preferences for spatial equality in the context of a national transport investment plan
N Mouter, S van Cranenburgh, B van Wee
Journal of Transport Geography 60, 217-230, 2017
432017
Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands
S Shelat, O Cats, S van Cranenburgh
Transportation Research Part A: Policy and Practice 159, 357-371, 2022
362022
The consumer-citizen duality: Ten reasons why citizens prefer safety and drivers desire speed
N Mouter, S Van Cranenburgh, B Van Wee
Accident Analysis & Prevention 121, 53-63, 2018
322018
Potential changes in value of travel time as a result of vehicle automation: a case study in the Netherlands
E De Looff, GHA Correia, S van Cranenburgh, M Snelder, B van Arem
97th Annual Meeting of the Transportation Research Board, 7-11, 2018
252018
Why did you predict that? Towards explainable artificial neural networks for travel demand analysis
A Alwosheel, S van Cranenburgh, CG Chorus
Transportation Research Part C: Emerging Technologies 128, 103143, 2021
232021
Revealing transition patterns between mono-and multimodal travel patterns over time: A mover-stayer model
M Kroesen, S van Cranenburgh
European Journal of Transport and Infrastructure Research 16 (4), 2016
202016
On the robustness of random regret minimization modelling outcomes towards omitted attributes
S Van Cranenburgh, CG Prato
Journal of choice modelling 18, 51-70, 2016
202016
Substantial changes and their impact on mobility: a typology and an overview of the literature
S Van Cranenburgh, C Chorus, B Van Wee
Transport Reviews 32 (5), 569-597, 2012
202012
On the robustness of efficient experimental designs towards the underlying decision rule
S van Cranenburgh, JM Rose, CG Chorus
Transportation Research Part A: Policy and Practice 109, 50-64, 2018
172018
‘Computer says no’is not enough: Using prototypical examples to diagnose artificial neural networks for discrete choice analysis
A Alwosheel, S Van Cranenburgh, CG Chorus
Journal of choice modelling 33, 100186, 2019
152019
An artificial neural network based method to uncover the value-of-travel-time distribution
S van Cranenburgh, M Kouwenhoven
Transportation 48, 2545-2583, 2021
142021
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