This repository contains useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories.

• 0x00 Papers
  • Reviews
  • Deep Learning Based Traffic Prediction Methods
    • 2015
    • 2016
    • 2017
    • 2018
    • 2019
    • 2020
      • 2021
      • 2022
  • Statistic Based Traffic Prediction Methods
    • 2018
    • 2019
  • Time Series Forecasting
  • Temporal Network Embedding
    • 2021
• 0x01 Tutorial
  • Textbook
  • Blogs
• 0x02 DataSource
  • Datasets
    • Traffic Dataset in Non-China Region
    • Traffic Dataset in China
    • Commute flow
    • Point of Interest/Land use
  • Trajectory
  • ETA
  • Website
• 0x03 Toolkits
• 0x04 Conferences & Journals
• 0x05 Research Group
  • Company
  • College
• 0x06 Related Repositories
• Contribution

1. [TITS 2015] Traffic Flow Prediction With Big Data: A Deep Learning Approach [paper]
2. [KDD 2020] Deep Learning for Spatio-Temporal Data Mining: A Survey [paper]
3. [Information Fusion 2020] Urban flow prediction from spatiotemporal data using machine learning: A survey [paper]
4. [Arxiv 2020] Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions [paper]
5. [Arxiv 2021] Graph Neural Network for Traffic Forecasting: A Survey [paper]
6. [Applied Intelligence 2022] Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues [paper]

1. [NIPS 2015] Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting [paper]

1. [Sigspatial 2016] DNN-Based Prediction Model for Spatio-Temporal Data [paper] [code]

1. [AAAI 2017] Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction [paper]
2. [ISPRS 2017] Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes [paper]
3. [Arxiv 2017] DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting [paper]

1. [TITS 2019] T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction [paper] [code]
2. [TKDE 2018] Flow prediction in spatio-temporal networks based on multitask deep learning [paper]
3. [TITS 2018] Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method [paper]
4. [IJCAI 2018] Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting [paper] [code] [review]
5. [IJCAI 2018] LC-RNN: A Deep Learning Model for Traffic Speed Prediction [paper]
6. [IJCAI 2018] GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction [paper]
7. [ICLR 2018] Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting [paper] [code-official-tf] [code-pytorch] [review] [data]
8. [KDD 2018] Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data [paper]
9. [CS224W 2018] Efficient Traffic Forecasting With Graph Embedding [paper] [code]
10. [CVPR 2018] Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks [paper] [code]
11. [AAAI 2018] Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction [paper][code] [data]
12. ……

1. [TITS 2019] TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets [paper]
2. [TITS 2019] Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction [paper] [code]
3. [TITS 2019] Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting [paper]
4. [IJCAI 2019] Graph WaveNet for Deep Spatial-Temporal Graph Modeling [paper] [code]
5. [IJCAI 2019] GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction [paper]
6. [AAAI 2019] Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction [paper] [code]
7. [AAAI 2019] DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis [paper] [code]
8. [AAAI 2019] Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting [paper] [code-pytorch]
9. [AAAI 2019] Multi-Range Attentive Bicomponent Graph Convolutional NetworkforTraicForecasting [paper]
10. [WWWC 2019] Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction [paper]
11. [IWPHM 2019] Spatio-Temporal Clustering of Traffic Data with Deep Embedded Clustering [paper]
12. [ICCV 2019] STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction [paper] [code]
13. [KDD 2019] Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning [paper][code]
14. [ICIKM 2019] Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction [paper]
15. [TKDE 2019] Flow prediction in spatio-temporal networks based on multitask deep learning [paper]
16. [IJGIS 2019] Traffic speed prediction for intelligent transportation system based on a deep feature fusion model [paper]
17. [Access 2019] Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting [paper]
18. [Arxiv 2019] Forecaster: A graph transformer for forecasting spatial and time dependent data [paper]
19. [Arxiv 2019] Temporal fusion transformers for interpretable multi-horizon time series forecasting. [paper]
20. ……

1. [Arxiv 2020] Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [paper] [code-not-official]
2. [Arxiv 2020] Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting [paper] [code]
3. [Arxiv 2020] Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting [paper]
4. [TGIS 2020] Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting [paper]
5. [ICTON 2020] Traffic Prediction in Optical Networks Using Graph Convolutional Generative Adversarial Networks [paper]
6. [AAAI 2020] Spatio-Temporal Graph Structure Learning for Traffic Forecasting [paper] [SOTA]
7. [AAAI 2020] Learning Geo-Contextual Embeddings for Commuting Flow Prediction [paper]
8. [AAAI 2020] Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting [paper] [code]
9. [Access 2020] STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting [paper]
10. [Sensor 2020] City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network [paper]
11. [Mobile Computing 2020] BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning [paper]
12. [TKDE 2020] Spatio-Temporal Meta Learning for Urban Traffic Prediction [paper]
13. [WC 2020] What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities [paper]
14. [TITS 2020] Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting [paper] [code]
15. [NIPS 2020] Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting [code] [code]
16. [AAAI 2020] GMAN: A Graph Multi-Attention Network for Traffic Prediction [paper] [code]
17. [KDD 2020] ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps [paper]
18. [KDD 2020] Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction [paper]
19. [TITS 2020] Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction [paper]
20. [TITS 2020] A Spatial-Temporal Attention Approach for Traffic Prediction [paper]
21. [TITS 2020] Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks [paper]
22. [WWW 2020] Traffic Flow Prediction via Spatial Temporal Graph Neural Network [paper]
23. [IJGIS 2020] Graph attention temporal convolutional network for traffic speed forecasting on road networks [paper]
24. [Arxiv 2020] ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed [paper]
25. [IF 2020] Spatial Temporal Incidence Dynamic Graph Neural Networks for Traffic Flow Forecasting [paper]
26. [ICDM 2020] TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting [paper]
27. ……

1. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [paper] [code]
2. [TITS 2021] Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies [paper] [code]
3. [Arxiv 2021] Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction [paper] [code]
4. [KDD 2021] Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution [paper] [code]
5. [KDD 2021] Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting [paper]
6. [AAAI 2021] TS2Vec: Towards Universal Representation of Time Series [paper]
7. [Arxiv 2021] Spatio-temporal joint graph convolutional networks for traffic forecasting [paper]
8. [PAKDD 2021] SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network [paper]
9. [IJCNN 2021] Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network [paper]
10. ……

1. [TITS 2022] 2F-TP:Learning Flexible Spatiotemporal Dependency for Flexible Traffic Prediction. [paper]
2. [Arxiv 2022] Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting [paper]
3. [Arxiv 2022] A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting [paper]

1. [TITS 2018] Probabilistic Data Fusion for Short-Term Traffic Prediction With Semiparametric Density Ratio Model [paper]

1. [TRPET 2019] A generalized Bayesian traffic model [paper]

1. Context-aware Forecasting for Multivariate Stationary Time-series
2. [Arxiv 2020] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [paper] [code]

1. [KDD 2021] Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space

1. Urban Computing
2. Multimodal Analytics for Next-Generation Big Data Technologies and Applications
3. ……

1. Traffic prediction with advanced Graph Neural Networks
2. ……

1. Cityscapes

2. New York City

3. NYC Bike

4. NYC Taxi

5. Train Station Dataset

6. Apolloscape

   该轨迹数据集由基于摄像头的图像、激光雷达扫描的点云和人工标注的轨迹组成。该数据集是在中国北京各种光照条件和交通密度下收集的。更具体地说，它包含了高度复杂的交通流，混合了车辆、乘客和行人。

7. data.world.traffic

8. PEMS-SF Dataset From UCI

   每个属性都描述了测量站在一天中的某个时间戳记录的捕获器位置的占用率测量值（在0和1之间）。每个测站的ID在 stations_list文本文件中给出。更多关于每个测量站的位置(GPS, 公路, 方向)的信息请参考PEMS网站。每条记录有963个（站点）x144个（时间戳）=138.672个属性。

9. Seattle Inductive Loop Detector Dataset

10. Road location and traffic data

11. INRIX – Driving Intelligence

    Charged Data

12. Los Angeles (METR-LA)

13. ……

1. 百度开放数据

2. 滴滴盖亚计划

   西安市脱敏路况预测数据，包括路网拓扑、道路属性、历史和实时车流等信息

3. HZJTD

   杭州综合交通研究中心收集的数据，包括杭州主城区202条道路的交通状况、速度、拥堵指数。

1. Longitudinal Employer-Household Dynamics
2. ……

1. PLUTO

   provide landuse data organized in .csv format, including more than 70 fields managed by city insititution.

2. ……

1. 深圳网约车行程轨迹数据-SIGSPATISL 2021 GISCUP 预估到达时间数据集

1. Welcome to PeMS
2. ……

1. Open Source Routing Machine
2. PyG Temporal
3. LibCity

1. ACM SIGSPATIAL SpatialDI
2. IEEE Transactions on Intelligent Transportation Systems
3. Association for the Advancement of Artificial Intelligence

1. http://urban-computing.com/yuzheng
2. DeepMind
3. ……

1. 北京交通大学 万怀宇老师团队

   Research Interest: Data mining and information extraction. e.g. Spatio-temporal data mining, social network mining, text information extraction and application of knowledge graph.

1. paper with code

2. https://github.com/topics/traffic-prediction

3. Awesome-Trajectory-Prediction

4. traffic_prediction

5. transdim

   This project aim at provide accurate and efficient solution for spatio-temporal data prediction.

6. 城市数据派

7. Multivariate Time Series Forecasting

8. deep-learning-time-series

9. GNN paper

10. Discovering millions of datasets on the web

11. LibCity An open-source research platform for intergrating several algorithms, data, and evalution metrics for traffic prediction.

12. GNN4Traffic

13. 交通轨迹数据、工具、论文汇总

14. ……

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