The article examines designer Wini Camacho’s Topolino XS concept—a modular micro‑car that can transform among three distinct body styles, showcasing how compact, adaptable design can turn urban transportation constraints into functional, expressive advantages.
This work proposes an adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and heterogeneous group behaviors to predict fine‑grained urban travel demand, demonstrating over 10% performance gains on real‑world Beijing and Shanghai datasets compared with classic baselines.
This work introduces a novel adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and group‑specific travel patterns, achieving over 10% improvement in short‑term traffic demand forecasts across heterogeneous urban populations.
This article introduces a hierarchical graph neural network model that jointly captures multi‑scale temporal patterns and global spatial context for urban traffic flow prediction, demonstrates its superiority over existing methods on multiple public datasets, and validates each component through extensive ablation studies.
At the 2019 CCF‑GAIR summit, Amap’s Director of Future Transportation explained how the company’s massive location‑based data, real‑time traffic feeds, and AI‑driven analytics enable smart traffic management, emergency vehicle routing, and predictive highway safety, delivering measurable congestion reductions and faster journeys across Chinese cities.
