Zobrazeno 1 - 10
of 463
pro vyhledávání: '"Raubal, Martin"'
Prediction problems in geographic information science and transportation are often motivated by the possibility to enhance operational efficiency and thereby reduce emissions. Examples range from predicting car sharing demand for relocation planning
Externí odkaz:
http://arxiv.org/abs/2410.11709
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced d
Externí odkaz:
http://arxiv.org/abs/2407.17703
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network plan
Externí odkaz:
http://arxiv.org/abs/2405.01770
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 Explai
Externí odkaz:
http://arxiv.org/abs/2405.00456
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 stu
Externí odkaz:
http://arxiv.org/abs/2311.11749
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
Externí odkaz:
http://arxiv.org/abs/2311.07349
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity pattern
Externí odkaz:
http://arxiv.org/abs/2310.17643
Despite the strong predictive performance of deep learning models for traffic prediction, their widespread deployment in real-world intelligent transportation systems has been restrained by a lack of interpretability. Uncertainty quantification (UQ)
Externí odkaz:
http://arxiv.org/abs/2308.06129
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits
Externí odkaz:
http://arxiv.org/abs/2305.19428
Publikováno v:
Journal of Transport Geography 114 (2024)
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 meth
Externí odkaz:
http://arxiv.org/abs/2303.14421