Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（交通工程教育部重点实验室，同济大学）；Urban Mobility Institute, Tongji University（交通 mobility 院，同济大学）
Accurately predicting short-term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary conditions, their accuracy often degrades significantly when faced with distribution shifts caused by external events or evolving urban dynamics. Frequent model retraining to adapt to such changes incurs prohibitive computational costs, especially for large-scale or foundation models. To address this challenge, we propose FORESEE (Forecasting Online with Residual Smoothing and Ensemble Experts), a lightweight online adaptation framework that is accurate, robust, and computationally efficient. FORESEE operates without any parameter updates to the base model. Instead, it corrects today's forecast in each region using yesterday's prediction error, stabilized through exponential smoothing guided by a mixture-of-experts mechanism that adapts to recent error dynamics. Moreover, an adaptive spatiotemporal smoothing component propagates error signals across neighboring regions and time slots, capturing coherent shifts in demand patterns. Extensive experiments on seven real-world datasets with three backbone models demonstrate that FORESEE consistently improves prediction accuracy, maintains robustness even when distribution shifts are minimal (avoiding performance degradation), and achieves the lowest computational overhead among existing online methods. By enabling real-time adaptation of traffic forecasting models with negligible computational cost, FORESEE paves the way for deploying reliable, up-to-date prediction systems in dynamic urban environments. Code and data are available at https://github.com/xiannanhuang/FORESEE
A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction
Shandong University（山东大学）；Southern University of Science and Technology（南方科技大学）；Northwest University（西北大学）；Didi Chuxing（滴滴出行）；Renmin University of China（中国人民大学）；Jilin University（吉林大学）
CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（同济大学交通工程教育重点实验室）；Urban Mobility Institute, Tongji University（同济大学城市交通研究院）
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Lanzhou University（兰州大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Wollongong（伍伦贡大学）；Jiangxi University of Science and Technology（江西理工大学）
AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
Innovation, Science and Economic Development Canada (ISED) and Carleton University（创新、科学与经济发展的加拿大（ISED）和卡尔顿大学）；Communications Research Centre (CRC)（通讯研究中心（CRC））；Carleton University（卡尔顿大学）
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）；University of Central Florida（中佛罗里达大学）；Hong Kong University of Science and Technology（香港科技大学）
