1. 2019
  2. ‘Computer says no’ is not enough: Using prototypical examples to diagnose artificial neural networks for discrete choice analysis

    Alwosheel, A., van Cranenburgh, S. & Chorus, C. G., 1 Dec 2019, In : Journal of Choice Modelling. 33, 100186.

    Research output: Contribution to journalArticleScientificpeer-review

  3. New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules

    van Cranenburgh, S. & Collins, A. T., 1 Jun 2019, In : Journal of Choice Modelling. 31, p. 104-123 20 p.

    Research output: Contribution to journalArticleScientificpeer-review

  4. 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 preference survey

    Correia, G. H. D. A., de Looff, E., van Cranenburgh, S., Snelder, M. & van Arem, B., 1 Jan 2019, In : Transportation Research Part A: Policy and Practice. 119, p. 359-382 24 p.

    Research output: Contribution to journalArticleScientificpeer-review

  5. An artificial neural network based approach to investigate travellers’ decision rules

    van Cranenburgh, S. & Alwosheel, A., Jan 2019, In : Transportation Research. Part C: Emerging Technologies. 98, p. 152-166 15 p.

    Research output: Contribution to journalArticleScientificpeer-review

  6. A logistic regression based method to uncover the Value-of-Travel-Time distribution

    van Cranenburgh, S. & Kouwenhoven, M., 2019, (In preparation) Delft University of Technology.

    Research output: Working paperScientific

  7. Experimental design optimised for discriminating among different choice models

    Huang, B., van Cranenburgh, S. & Chorus, C., 2019.

    Research output: Contribution to conferenceAbstractScientific

  8. Substitutability as a spatial concept to evaluate travel alternatives

    van Wee, B., van Cranenburgh, S. & Maat, K., 2019, In : Journal of Transport Geography. 79, 12 p., 102469.

    Research output: Contribution to journalArticleScientificpeer-review

  9. Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution

    van Cranenburgh, S. & Kouwenhoven, M., 2019, Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Joya, G., Rojas, I. & Catala, A. (eds.). Springer, p. 88-102 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11506 LNCS).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  10. 2018
  11. The consumer-citizen duality: Ten reasons why citizens prefer safety and drivers desire speed

    Mouter, N., van Cranenburgh, S. & van Wee, B., Dec 2018, In : Accident Analysis and Prevention. 121, p. 53-63 11 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Does the decision rule matter for large-scale transport models?

    van Cranenburgh, S. & Chorus, C. G., Aug 2018, In : Transportation Research Part A: Policy and Practice. 114, p. 338-353 16 p.

    Research output: Contribution to journalArticleScientificpeer-review

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