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ORCIDSander van Cranenburgh
2020 – today
- 2025
[i8]Yiru Jiao, Sander van Cranenburgh, Simeon C. Calvert, Hans van Lint:
Structure-preserving contrastive learning for spatial time series. CoRR abs/2502.06380 (2025)- 2024
- [j4]Jeroen Delfos, Anneke Zuiderwijk-van Eijk
, Sander van Cranenburgh, Caspar G. Chorus, Roel I. J. Dobbe:
Integral system safety for machine learning in the public sector: An empirical account. Gov. Inf. Q. 41(3): 101963 (2024) - [i7]Jose Ignacio Hernandez, Niek Mouter, Sander van Cranenburgh:
An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks. CoRR abs/2404.13198 (2024) - [i6]Yiru Jiao, Simeon C. Calvert, Sander van Cranenburgh, Hans van Lint:
A unified theory and statistical learning approach for traffic conflict detection. CoRR abs/2407.10959 (2024) - [i5]Sander van Cranenburgh, Francisco Garrido-Valenzuela:
A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam. CoRR abs/2410.17880 (2024) - [i4]Gabriel Nova, Sander van Cranenburgh, Stephane Hess:
Understanding the decision-making process of choice modellers. CoRR abs/2411.01704 (2024) - 2023
- [j3]Francisco Garrido-Valenzuela, Oded Cats, Sander van Cranenburgh:
Where are the people? Counting people in millions of street-level images to explore associations between people's urban density and urban characteristics. Comput. Environ. Urban Syst. 102: 101971 (2023) - [j2]Lucas Spierenburg, Sander van Cranenburgh, Oded Cats:
Characterizing residential segregation in cities using intensity, separation, and scale indicators. Comput. Environ. Urban Syst. 103: 101990 (2023) - [c5]Yiru Jiao, Simeon C. Calvert, Hans van Lint, Sander van Cranenburgh:
Identifying Vehicle Interaction at Urban Intersections: A Comparison of Proximity Resistance, Time-to-Collision, and Post-Encroachment-Time. ITSC 2023: 973-978 - [i3]Sander van Cranenburgh, Francisco Garrido-Valenzuela:
Computer vision-enriched discrete choice models, with an application to residential location choice. CoRR abs/2308.08276 (2023) - 2022
- [c4]Lucas Spierenburg, Sander van Cranenburgh, Oded Cats:
A regionalization method filtering out small-scale spatial fluctuations. AGILE Conf. 2022: 61 - [c3]Francisco Garrido-Valenzuela, Sander van Cranenburgh, Oded Cats:
Enriching geospatial data with computer vision to identify urban environment determinants of social interactions. AGILE Conf. 2022: 72 - [c2]Jeroen Delfos, Anneke Zuiderwijk, Sander van Cranenburgh, Caspar G. Chorus:
Perceived challenges and opportunities of machine learning applications in governmental organisations: an interview-based exploration in the Netherlands. ICEGOV 2022: 82-89 - [i2]Ruben Sangers, Jan van Gemert, Sander van Cranenburgh:
Explainability of Deep Learning models for Urban Space perception. CoRR abs/2208.13555 (2022) - 2021
- [j1]Caspar G. Chorus, Sander van Cranenburgh, Aemiro Melkamu Daniel, Erlend Dancke Sandorf, Anae Sobhani, Teodóra Szép:
Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence. Math. Soc. Sci. 109: 28-44 (2021) - [i1]Sander van Cranenburgh, Shenhao Wang, Akshay Vij, Francisco Pereira, Joan Walker:
Choice modelling in the age of machine learning. CoRR abs/2101.11948 (2021)
2010 – 2019
- 2019
- [c1]Sander van Cranenburgh, Marco Kouwenhoven:
Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution. IWANN (1) 2019: 88-102
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