Search Results for author: Martin Raubal

Found 12 papers, 7 papers with code

Revealing behavioral impact on mobility prediction networks through causal interventions

no code implementations20 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.

Causal Inference

Where you go is who you are -- A study on machine learning based semantic privacy attacks

1 code implementation26 Oct 2023 Nina Wiedemann, Ourania Kounadi, Martin Raubal, Krzysztof Janowicz

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data.

Uncertainty Quantification for Image-based Traffic Prediction across Cities

1 code implementation11 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.

Decision Making Decision Making Under Uncertainty +3

Evaluating geospatial context information for travel mode detection

1 code implementation30 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.

Spatially-Aware Car-Sharing Demand Prediction

no code implementations25 Mar 2023 Dominik J. Mühlematter, Nina Wiedemann, Yanan Xin, Martin Raubal

In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand.

Context-aware multi-head self-attentional neural network model for next location prediction

2 code implementations4 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.

Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis

no code implementations18 Oct 2022 Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz, Martin Raubal

Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems.

Causal Inference

National-scale bi-directional EV fleet control for ancillary service provision

no code implementations14 Oct 2022 Lorenzo Nespoli, Nina Wiedemann, Esra Suel, Yanan Xin, Martin Raubal, Vasco Medici

Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases.

How do you go where? Improving next location prediction by learning travel mode information using transformers

1 code implementation8 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.

Traffic Forecasting on Traffic Moving Snippets

1 code implementation27 Oct 2021 Nina Wiedemann, Martin Raubal

With the performance on the traffic4cast test data and further experiments on a validation set it is shown that patch-wise prediction indeed improves accuracy.

Traffic Prediction

Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey

no code implementations19 Feb 2021 Nishant Kumar, Martin Raubal

In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion.

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