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van Cranenburgh

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Articles by van Cranenburgh

Model fit vs prediction performance: contrasting fixed-coefficient models with mixture models

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By van Cranenburgh
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Economics > General Economics

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By van Cranenburgh
| arXiv
[Submitted on 6 Jun 2025 (v1), last revised 8 Oct 2026 (this version, v4)] View PDF HTML (experimental) Comments: Subjects: General Economics (econ.GN); Machine Learning (cs.LG) Cite as: arXiv:2506.06410 [econ.GN] (or arXiv:2506.06410v4 [econ.GN] for this version) https://doi.org/10.48550/arXiv.2506.06410
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Combine and conquer: model averaging for out-of-distribution forecasting

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By van Cranenburgh
| arXiv
[Submitted on 4 Jun 2025 ( v1 ), last revised 6 Oct 2026 (this version, v4)] Title:Combine and conquer: model averaging for out-of-distribution forecasting View a PDF of the paper titled Combine and conquer: model averaging for out-of-distribution forecasting, by Stephane Hess and 1 other authors View PDF HTML (experimental) Abstract:Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from...
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[2505.13556] Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures

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By van Cranenburgh, Van Lint
| arXiv
Work on one of the world's most important websites and make an impact on open science.
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[2502.06380] Structure-preserving contrastive learning for spatial time series

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By van Cranenburgh, Van Lint
| arXiv
[Submitted on 10 Feb 2025 ( v1 ), last revised 9 May 2025 (this version, v3)] Title:Structure-preserving contrastive learning for spatial time series View a PDF of the paper titled Structure-preserving contrastive learning for spatial time series, by Yiru Jiao and 3 other authors View PDF HTML (experimental) Abstract:The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can...
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[2410.17880] A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam

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By van Cranenburgh
| arXiv
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[2407.10959] A Unified Probabilistic Approach to Traffic Conflict Detection

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By van Cranenburgh, Van Lint
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2308.08276] Computer vision-enriched discrete choice models, with an application to residential location choice

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By van Cranenburgh, Francisco Garrido-Valenzuela
| arXiv
[Submitted on 16 Aug 2023] Title:Computer vision-enriched discrete choice models, with an application to residential location choice Download a PDF of the paper titled Computer vision-enriched discrete choice models, with an application to residential location choice, by Sander van Cranenburgh and Francisco Garrido-Valenzuela Download PDF Abstract: Visual imagery is indispensable to many multi-attribute decision situations.
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