arXiv ·
Researchers developed a data-driven toolkit for short-term traffic forecasting using high-resolution traffic data from urban road sensors. The method models forecasting as a matrix completion problem, mapping inputs to a higher-dimensional space using kernels and adaptive boosting. Validated using real-world data from Abu Dhabi, UAE, the method outperforms state-of-the-art algorithms.
arXiv ·
This paper introduces an interpretable pipeline that integrates mobility and social media data to analyze human behavior during crises. The framework was evaluated through two case studies, including a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021. The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structures, and mines association rules. Results demonstrate clear cross-domain behavioral structures in crises, yielding both scientifically credible and policy-actionable intelligence. Why it matters: This work provides a novel methodological approach for developing actionable crisis management strategies by fusing multimodal data, directly applicable to public health and emergency response in the UAE and the broader region.
arXiv ·
The paper introduces a novel method for short-term, high-resolution traffic prediction, modeling it as a matrix completion problem solved via block-coordinate descent. An ensemble learning approach is used to capture periodic patterns and reduce training error. The method is validated using both simulated and real-world traffic data from Abu Dhabi, demonstrating superior performance compared to other algorithms.