Accurate radio source localization on resource-constrained hardware presents a primary challenge in wireless networks. We introduce EfficientLocNet, a novel architecture achieving superior accuracy with exceptional computational efficiency, driven by two distinct design choices. Its efficiency stems from lightweight Depthwise Separable Convolutions, while its accuracy is enhanced by two components working in tandem: Atrous Spatial Pyramid Attention to capture long-range spatial features, and a Self-Attention module to refine the latent representation. Evaluated against state-of-the-art (SOTA) methods, EfficientLocNet outperforms the top-performing model, DSLoc, on all key metrics: it reduces the mean localization error by at least 5%, possesses a 40x smaller model size, and requires over 100x fewer computations. This compelling combination of performance and efficiency validates EfficientLocNet as a powerful solution for deployment in edge computing environments.

Accurate traffic forecasting is crucial for Intelligent Transportation Systems (ITS) but is significantly challenged by non-periodic external events that disrupt regular traffic patterns. While Graph Neural Networks (GNNs) excel at modeling periodic traffic, they often falter in predicting event-driven dynamics. Existing event-aware methods either rely on manually engineered features with limited generalization or depend on curated textual event datasets that are costly to maintain and incomplete. The advent of Large Language Models (LLMs) offers new avenues for understanding and integrating event information. However, directly applying LLMs for all spatio-temporal reasoning can be inefficient, and effectively leveraging their event understanding capabilities within structured forecasting workflows remains a challenge. This paper introduces FUSE-Traffic, a framework which synergizes the dynamic event querying and understanding prowess of LLMs with the spatio-temporal modeling capabilities of GNNs. FUSE-Traffic features an on-demand event information extraction module using LLM prompting and a cross-attention based multimodal fusion mechanism to integrate rich event semantics with traffic flow features. This design enables the model to dynamically perceive and adapt to event-triggered traffic pattern changes. Comprehensive experiments on the METR-LA and PEMS datasets demonstrate that FUSE-Traffic significantly outperforms state-of-the-art models, especially under high-impact event conditions, showcasing robust predictive accuracy and resilience where traffic patterns are most disrupted. Code available at https://github.com/GeoAICenter/FUSE-Traffic_Sigspatial2025

Geo-obfuscation is a Location Privacy Protection Mechanism used in location-based services that allows users to report obfuscated locations instead of exact ones. A formal privacy criterion, geoindistinguishability (Geo-Ind), requires real locations to be hard to distinguish from nearby locations (by attackers) based on their obfuscated representations. However, Geo-Ind often fails to consider context, such as road networks and vehicle traffic conditions, making it less effective in protecting the location privacy of vehicles, of which the mobility are heavily influenced by these factors.

Understanding and predicting Origin-Destination (OD) flows is crucial for urban planning and transportation management. Traditional OD prediction models, while effective within single cities, often face limitations when applied across different cities due to varied traffic conditions, urban layouts, and socio-economic factors.