1 code implementation • 19 Feb 2024 • Simon Dirmeier, Ye Hong, Fernando Perez-Cruz
Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, audio, natural language processing, or biomolecule generation.
no code implementations • 20 Nov 2023 • Ye Hong, Yanan Xin, Simon Dirmeier, Fernando Perez-Cruz, Martin Raubal
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions.
1 code implementation • 1 Nov 2023 • Simon Dirmeier, Ye Hong, Yanan Xin, Fernando Perez-Cruz
Reliable quantification of epistemic and aleatoric uncertainty is of crucial importance in applications where models are trained in one environment but applied to multiple different environments, often seen in real-world applications for example, in climate science or mobility analysis.
no code implementations • 22 Oct 2023 • Tanhua Jin, Kailai Wang, Yanan Xin, Jian Shi, Ye Hong, Frank Witlox
Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms.
1 code implementation • 11 Aug 2023 • Alexander Timans, Nina Wiedemann, Nishant Kumar, Ye Hong, Martin Raubal
We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
1 code implementation • 30 May 2023 • Ye Hong, Emanuel Stüdeli, Martin Raubal
While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model.
2 code implementations • 4 Dec 2022 • Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems.
1 code implementation • 8 Oct 2022 • Ye Hong, Henry Martin, Martin Raubal
Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options.
1 code implementation • 27 Oct 2019 • Henry Martin, Ye Hong, Dominik Bucher, Christian Rupprecht, René Buffat
The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour.