4 citations · 9 across the 5 of their papers we have counts for
6 papers
Timeseries Foundation Models for Mobility: A Benchmark Comparison with Traditional and Deep Learning Models
Anita Graser
Crowd and flow predictions have been extensively studied in mobility data science. Traditional forecasting methods have relied on statistical models such as ARIMA, later supplement…
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond
Zilong Liu, Krzysztof Janowicz, Kitty Currier +5
While the paths humans take play out in social as well as physical space, measures to describe and compare their trajectories are carried out in abstract, typically Euclidean, spac…
Towards eXplainable AI for Mobility Data Science
Anahid Jalali, Anita Graser, Clemens Heistracher
This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS track…
Towards Mobility Data Science (Vision Paper)
Mohamed Mokbel, Mahmoud Sakr, Li Xiong +45
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracki…
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
Viktorija Pruckovskaja, Axel Weissenfeld, Clemens Heistracher +5
Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learn…
From Simple Features to Moving Features and Beyond?
Anita Graser, Esteban Zimányi, Krishna Chaitanya Bommakanti
Mobility data science lacks common data structures and analytical functions. This position paper assesses the current status and open issues towards a universal API for mobility da…