Traffic Forecasting

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 58

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From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Jun 8, 2026Shuhao Li, Weidong Yang, Yue Cui +4Traffic Flow Estimation Spatiotemporal Forecasting
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200082, China · Tongyi Lab, Alibaba Group, Cloud Valley, NO.1008 Dengcai Street, Xihu District, Hangzhou 310020, China · The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China +1
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
Jun 2, 2026Jichao Li, Xuanming ShiIntelligent Transportation Systems Spatiotemporal Forecasting
Portsmouth Abbey School · CodingFuture (Shanghai) Education Technology Co., Ltd.
Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction
May 30, 2026Oluwaleke Yusuf, Adil Rasheed, Frank LindsethIntelligent Transportation Systems Multi-Task Learning
Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway · Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway
Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
May 28, 2026Amirhossein Ghaffari, Saeid Sheikhi, Ekaterina GilmanAdaptive Model Routing Mixture-of-Experts Models
Future Computing Group, University of Oulu
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
May 28, 2026Du Yin, Hao Xue, Arian Prabowo +2Intelligent Transportation Systems Forecasting Benchmarks
University of New South Wales, Sydney, NSW, Australia
A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning
May 27, 2026Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong AboahIntelligent Transportation Systems Traffic Flow Estimation
North Dakota State University, 1410 14th Avenue Offerdahl North Building, CIE 201, Fargo, 58102, North Dakota, United States
PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
May 25, 2026Ruiwen Gu, Yahao Liu, Zhenyu Liu +2Graph Neural Networks Intelligent Transportation Systems
Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting
May 25, 2026Ruiwen Gu, Qitai Tan, Yahao Liu +1Attention Mechanisms Spatiotemporal Forecasting
Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
May 22, 2026Edward Henderson, George De Ath, Nick PepperAir Traffic Management Intelligent Transportation Systems
The Alan Turing Institute · University of Exeter
Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift
May 22, 2026Yiming MaDomain Adaptation Traffic Forecasting
Department of Statistics and Finance, School of Management, University of Science and Technology of China
Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival
May 18, 2026Neofytos DimitriouKG Construction Traffic Forecasting
Maritime Digitalization Centre Cyprus Marine and Maritime Institute Larnaca 6300, Cyprus
A Global-Local Graph Attention Network for Traffic Forecasting
May 16, 2026Tianchi ZhangIntelligent Transportation Systems Spatiotemporal Forecasting
Unlocking air traffic flow prediction through microscopic aircraft-state modeling
May 11, 2026Bin Wang, Anqi Liu, Jiangtao Zhao +8Air Traffic Management Intelligent Transportation Systems
Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, Shandong, China. · Sanya Oceanographic Institution, Ocean University of China, Sanya, 572022, Hainan, China. · Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics, Chengdu, 610000, Sichuan, China. +2
TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
May 9, 2026Bowen Liu, Haijian Lai, Chan-Tong Lam +4Time Series Forecasting Traffic Forecasting
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
Efficient Prompt Learning for Traffic Forecasting
May 8, 2026Qianru Zhang, Xinyi Gao, Alexander Zhou +3OOD Generalization Prompt Tuning
School of Computing and Data Science The University of Hong Kong · School of Electrical Engineering and Computer Science The University of Queensland · Department of Computing The Hong Kong Polytechnic University
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
May 7, 2026Łukasz Gorczyca, Kacper Drozd, Michał Bujak +1Intelligent Transportation Systems Traffic Flow Estimation
Jagiellonian University Doctoral School of Exact and Natural Sciences · Jagiellonian University
Road Risk Monitor: A Deployable U.S. Road Incident Forecasting System with Live Weather and Road-Level Tiles
May 5, 2026Anton IvchenkoIntelligent Transportation Systems Traffic Accident Anticipation
Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
May 5, 2026Xiao Zhang, Yafei Li, Ruixiang Wang +3Spatiotemporal Forecasting Neural ODEs
Zhengzhou University · Northwestern Polytechnical University
CNN-based Multi-In-Multi-Out Model for Efficient Spatiotemporal Prediction
May 2, 2026Hyeonseok JinEfficient Neural Network Inference Precipitation Forecasting
Department of Artificial Intelligence Convergence, Graduate School, Chonnam National University
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
Apr 22, 2026Georgios Anyfantis, Pere Barlet-RosTemporal GNNs Traffic Forecasting
Department of Computer Architecture Universitat Polit`ecnica de Catalunya Barcelona, Spain
GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
Apr 18, 2026Dongyi He, Yuanquan Gao, Bin Jiang +1Long-Term Time Series Forecasting Intelligent Transportation Systems
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China
FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction
Apr 17, 2026Seerat Kaur, Sukhjit Singh Sehra, Dariush EbrahimiPrivacy-Preserving Language Models Traffic Flow Estimation
Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, Canada
Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting
Apr 17, 2026Weijiang Xiong, Robert Fonod, Nikolas GeroliminisTraffic Forecasting Probabilistic Time Series Forecasting
Urban Transport Systems Laboratory (LUTS), EPFL, Lausanne, 1015, Switzerland
Deep Learning Network-Temporal Models For Traffic Prediction
Mar 12, 2026Yufeng Xin, Ethan FanMultivariate Time Series Forecasting LLM-Based Time Series Forecasting
RENCI, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions
Feb 15, 2026Yue Wang, Djellel Difallah, Areg Karapetyan +1Intelligent Transportation Systems Spatiotemporal Forecasting
New York University Abu Dhabi
HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
Nov 26, 2025Yongyao Wang, Xie Yu, Jingyuan Wang +2Intelligent Transportation Systems Spatiotemporal Forecasting
1State Key Laboratory of Complex & Critical Software Environment (SKLCCSE) · School of Computer Science and Engineering, Beihang University, Beijing, China · 3MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University, China
Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference
Apr 10, 2025Zhifeng Hao, Kai Hu, Juncai Zhang +2Temporal Convolutional Networks Dynamic Graph Learning
School of Cyberspace Security, Hainan University, Haikou, China
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
Aug 2, 2024Zibo Liu, Zhe Jiang, Zelin Xu +5Long-Term Time Series Forecasting Spatiotemporal Forecasting
Department of Computer & Information Science & Engineering, University of Florida · Department of Computer Science, University of Kentucky

Jun 8, 2026, cs.AIJ/K move · Enter open · S save

From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

Shuhao Li, Weidong Yang, Yue Cui, Zizhuo Xu+3

College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200082, China · Tongyi Lab, Alibaba Group, Cloud Valley, NO.1008 Dengcai Street, Xihu District, Hangzhou 310020, China · The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China+1

Traffic Flow Estimation Spatiotemporal Forecasting Traffic Forecasting Multiscale Time-Series Modeling

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Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and preprocessing pipelines. To address this temporal granularity mismatch, we formulate a novel problem: predicting fine-grained future traffic using coarse-grained sampled data. We propose the Spatial-Temporal Refinement Predictor (STRP), a granularity-aware framework for spatio-temporal data systems. STRP integrates two components: Tree Convolution for efficient and interpretable spatial dependency modeling, and Inverse Dilated Convolution for progressive temporal extrapolation. STRP supports two practical prediction settings: window-based and duration-based, to handle different forms of granularity mismatch. Experiments on six benchmark datasets show that STRP significantly outperforms state-of-the-art baselines in both accuracy and efficiency. Our work offers a practical and interpretable approach to managing granularity mismatches in spatio-temporal traffic data systems.

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