Sander van Cranenburgh

According to our database, Sander van Cranenburgh authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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

A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam.

Sander van Cranenburgh, Francisco Garrido-Valenzuela

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

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

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., June, 2023

Characterizing residential segregation in cities using intensity, separation, and scale indicators.

Lucas Spierenburg, Sander van Cranenburgh, Oded Cats

Comput. Environ. Urban Syst., 2023

Computer vision-enriched discrete choice models, with an application to residential location choice.

Sander van Cranenburgh, Francisco Garrido-Valenzuela

CoRR, 2023

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

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

2022

Explainability of Deep Learning models for Urban Space perception.

Ruben Sangers, Jan van Gemert, Sander van Cranenburgh

CoRR, 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

Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance, 2022

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

Choice modelling in the age of machine learning.

Sander van Cranenburgh, Shenhao Wang, Akshay Vij, Francisco Pereira, Joan Walker

CoRR, 2021

2019

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

Sander van Cranenburgh, Marco Kouwenhoven

Proceedings of the Advances in Computational Intelligence, 2019