MoE Enhanced Federated Learning for Spatiotemporal Prediction

Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor deployment and uneven urban development. Cross-city knowledge transfer has thus attracted increasing attention, enabling data-rich cities to assist data-scarce ones. However, centralized approaches raise privacy concerns, while existing federated methods struggle with pronounced spatiotemporal heterogeneity across cities. To address these challenges, we propose MoE-FedTP, a personalized federated cross-city spatiotemporal prediction framework based on lightweight Mixture-of-Experts (MoE) networks. MoE-FedTP first employs spatiotemporal neural networks to extract features from both source and target cities, then introduces a set of expert networks derived from different source cities through partial parameter sharing. A gating mechanism dynamically fuses the experts to capture diverse traffic dynamics, achieving fine-grained modeling of urban heterogeneity while preserving privacy. Experiments on four real-world traffic datasets show that MoE-FedTP consistently outperforms state-of-the-art cross-city and federated learning baselines, demonstrating its effectiveness in enhancing prediction accuracy for data-scarce cities.

A Dataset for Spatiotemporal-Sensitive POI Question Answering

Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, navigating an unfamiliar city involves planning itineraries that sequence locations and timing cultural experiences. However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models' spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. Our dataset challenges models to parse complex spatiotemporal dependencies, and evaluations of state-of-the-art multilingual LLMs (e.g., Qwen2.5-7B, Llama3.1-8B) reveal stark limitations: even the top-performing model (Qwen2.5-7B fine-tuned with RAG+LoRA) achieves a top 10 Hit Ratio (HR@10) of only 0.41 on the easiest task, far below human performance at 0.56. This underscores persistent weaknesses in LLMs' ability to perform consistent spatiotemporal reasoning, while highlighting POI-QA as a robust benchmark to advance algorithms sensitive to spatiotemporal dynamics. The dataset is publicly available at https://www.kaggle.com/ds/7394666.