Research

 

Latest journal publications

Alwosheel, A., van Cranenburgh, S. and Chorus, C.G., 2019. ‘Computer says no’is not enough: Using prototypical examples to diagnose artificial neural networks for discrete choice analysis. Journal of Choice Modelling, p.100186.

Van Cranenburgh, S. & Kouwenhoven, M. (2019). Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution. International Work-Conference on Artificial Neural Networks, Springer.

Alwosheel, A., van Cranenburgh, S. & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28, 167-182.

Van Cranenburgh, S. & Alwosheel, A. (2019). An artificial neural network based approach to investigate travellers’ decision rules. Transportation Research Part C: Emerging Technologies, 98, 152-166.

Latest working papers

 

Van Cranenburgh, S.(2020). This work blends computer vision into discrete choice models. Thereby, it opens-up the possibility to model choice behaviour in the presence of visual stimuli. 

Alwosheel, A., van Cranenburgh, S. & Chorus, C. G.(2020). This work pioneers and re-conceptualises the use of heatmap generation to explain ANN predictions for travellers’ choice behaviour analysis. 

Van Cranenburgh, S. & Kouwenhoven, M., 2019. An artificial neural network based method to uncover the Value-of-Travel-Time distribution.

Van Cranenburgh, S. & Kouwenhoven, M., 2019. A logistic regression based method to uncover the Value-of-Travel-Time distribution.