MR

Martin Raubal

12 records found

The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary servic ...
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. This study introduces a causal interve ...
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results difficult to interpret by users. This study aims to leverage an Explainable AI approach, counterfact ...
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations an ...

Vehicle-to-grid and car sharing

Willingness for flexibility in reservation times in Switzerland

Combining vehicle-to-grid (V2G) with car sharing can substantially contribute to decarbonization of both energy and transportation sectors. Car-sharing users’ booking slot flexibility is crucial for integration yet remains underexplored. We developed an integrated choice and late ...
Complex simulations and machine-learning models increase in application in research, industry, and governance. However, applying these systems with reasonable accuracy and efficiency requires large-scale efforts of data collection, data transformation, data analysis, and data vis ...
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. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs chargin ...
Quantifying intra-person variability in travel choices is essential for the comprehension of activity–travel behaviour. Due to a lack of empirical studies, there is limited understanding of how an individual's travel pattern evolves over months and years. We use two high-resoluti ...
Deep learning (DL) models have shown strong predictive power in solving traffic problems in the past few years. Due to their lack of interpretability and transparency, applications of such models are sometimes controversial. To ensure trust in the model, it is crucial for model e ...
The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if ...

Vision paper

Causal inference for interpretable and robust machine learning in mobility analysis

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intel ...