Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution. - Sander van Cranenburgh; Marco Kouwenhoven (IWANN (1), 2019)

Choice modelling in the age of machine learning. - Sander van Cranenburgh; Shenhao Wang; Akshay Vij; Francisco Pereira 002; Joan Walker (CoRR, 2021)

Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence. - Caspar G. Chorus; Sander van Cranenburgh; Aemiro Melkamu Daniel; Erlend Dancke Sandorf; Anae Sobhani; Teodóra Szép (Math. Soc. Sci., 2021)

Enriching geospatial data with computer vision to identify urban environment determinants of social interactions. - Francisco Garrido-Valenzuela; Sander van Cranenburgh; Oded Cats (AGILE Conf., 2022)

A regionalization method filtering out small-scale spatial fluctuations. - Lucas Spierenburg; Sander van Cranenburgh; Oded Cats (AGILE Conf., 2022)

Perceived challenges and opportunities of machine learning applications in governmental organisations: an interview-based exploration in the Netherlands. - Jeroen Delfos; Anneke Zuiderwijk; Sander van Cranenburgh; Caspar G. Chorus (ICEGOV, 2022)

Explainability of Deep Learning models for Urban Space perception. - Ruben Sangers; Jan van Gemert; Sander van Cranenburgh (CoRR, 2022)

Identifying Vehicle Interaction at Urban Intersections: A Comparison of Proximity Resistance, Time-to-Collision, and Post-Encroachment-Time. - Yiru Jiao; Simeon C. Calvert; Hans van Lint; Sander van Cranenburgh (ITSC, 2023)

Computer vision-enriched discrete choice models, with an application to residential location choice. - Sander van Cranenburgh; Francisco Garrido-Valenzuela (CoRR, 2023)

Characterizing residential segregation in cities using intensity, separation, and scale indicators. - Lucas Spierenburg; Sander van Cranenburgh; Oded Cats (Comput. Environ. Urban Syst., 2023)

Where are the people? Counting people in millions of street-level images to explore associations between people's urban density and urban characteristics. - Francisco Garrido-Valenzuela; Oded Cats; Sander van Cranenburgh (Comput. Environ. Urban Syst., 2023)

An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks. - Jose Ignacio Hernandez; Niek Mouter; Sander van Cranenburgh (CoRR, 2024)

A unified theory and statistical learning approach for traffic conflict detection. - Yiru Jiao; Simeon C. Calvert; Sander van Cranenburgh; Hans van Lint (CoRR, 2024)

A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam. - Sander van Cranenburgh; Francisco Garrido-Valenzuela (CoRR, 2024)

Understanding the decision-making process of choice modellers. - Gabriel Nova; Sander van Cranenburgh; Stephane Hess (CoRR, 2024)

Integral system safety for machine learning in the public sector: An empirical account. - Jeroen Delfos; Anneke Zuiderwijk-van Eijk; Sander van Cranenburgh; Caspar G. Chorus; Roel I. J. Dobbe (Gov. Inf. Q., 2024)

Structure-preserving contrastive learning for spatial time series. - Yiru Jiao; Sander van Cranenburgh; Simeon C. Calvert; Hans van Lint (CoRR, 2025)

Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures. - Yiru Jiao; Simeon C. Calvert; Sander van Cranenburgh; Hans van Lint (CoRR, 2025)

Improving choice model specification using reinforcement learning. - Gabriel Nova; Sander van Cranenburgh; Stephane Hess (CoRR, 2025)

Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities. - Georges Sfeir; Gabriel Nova; Stephane Hess; Sander van Cranenburgh (CoRR, 2025)

Studying riots through the lens of social media. - Lucas Spierenburg; Sander van Cranenburgh; Oded Cats (J. Big Data, 2025)