{"total":22,"rows":[{"id":"2503.13502","title":"Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey","created":"2025-03-12","cited_by_count":24,"area":null},{"id":"2411.03859","title":"UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces","created":"2024-11-06","cited_by_count":18,"area":null},{"id":"2412.00953","title":"BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis","created":"2024-12-01","cited_by_count":16,"area":null},{"id":"2506.01364","title":"Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review","created":"2025-06-02","cited_by_count":10,"area":null},{"id":"2506.00635","title":"Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting","created":"2025-05-31","cited_by_count":9,"area":null},{"id":"2603.11950","title":"Learning Transferable Sensor Models via Language-Informed Pretraining","created":"2026-03-12","cited_by_count":4,"area":null},{"id":"2601.03181","title":"Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey","created":"2026-01-06","cited_by_count":3,"area":null},{"id":"2408.14505","title":"RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming","created":"2024-08-24","cited_by_count":2,"area":null},{"id":"2504.02009","title":"Urban Computing in the Era of Large Language Models","created":"2025-04-02","cited_by_count":2,"area":null},{"id":"2601.12083","title":"Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models","created":"2026-01-17","cited_by_count":2,"area":null},{"id":"2602.10502","title":"Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web","created":"2026-02-11","cited_by_count":2,"area":null},{"id":"2602.13282","title":"GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction","created":"2026-02-06","cited_by_count":2,"area":null},{"id":"2609.24444","title":"WPBench: A Comprehensive Benchmark for Wind Power Forecasting","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2508.08947","title":"Generalising Traffic Forecasting to Regions without Traffic Observations","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2602.20677","title":"UrbanFM: Scaling Urban Spatio-Temporal Foundation Models","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2602.21757","title":"Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction","created":"2026-02-25","cited_by_count":1,"area":null},{"id":"2410.14726","title":"Incorporating Long-term Data in Training Short-term Traffic Prediction Model","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2602.19113","title":"Learning from Complexity: Exploring Dynamic Sample Pruning of Spatio-Temporal Training","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2606.08303","title":"GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2607.07016","title":"Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.26467","title":"Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2609.13878","title":"A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting","created":"2026-09-12","cited_by_count":0,"area":null}],"next_cursor":null}