[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jwwthu--GNN4Traffic":3,"tool-jwwthu--GNN4Traffic":65},[4,23,32,40,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":22},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",85092,2,"2026-04-10T11:13:16",[13,14,15,16,17,18,19,20,21],"图像","数据工具","视频","插件","Agent","其他","语言模型","开发框架","音频","ready",{"id":24,"name":25,"github_repo":26,"description_zh":27,"stars":28,"difficulty_score":29,"last_commit_at":30,"category_tags":31,"status":22},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[19,14,18],{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":29,"last_commit_at":38,"category_tags":39,"status":22},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[18,13,14,20],{"id":41,"name":42,"github_repo":43,"description_zh":44,"stars":45,"difficulty_score":46,"last_commit_at":47,"category_tags":48,"status":22},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,3,"2026-04-04T04:44:48",[17,13,20,19,18],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":46,"last_commit_at":55,"category_tags":56,"status":22},519,"PaddleOCR","PaddlePaddle\u002FPaddleOCR","PaddleOCR 是一款基于百度飞桨框架开发的高性能开源光学字符识别工具包。它的核心能力是将图片、PDF 等文档中的文字提取出来，转换成计算机可读取的结构化数据，让机器真正“看懂”图文内容。\n\n面对海量纸质或电子文档，PaddleOCR 解决了人工录入效率低、数字化成本高的问题。尤其在人工智能领域，它扮演着连接图像与大型语言模型（LLM）的桥梁角色，能将视觉信息直接转化为文本输入，助力智能问答、文档分析等应用场景落地。\n\nPaddleOCR 适合开发者、算法研究人员以及有文档自动化需求的普通用户。其技术优势十分明显：不仅支持全球 100 多种语言的识别，还能在 Windows、Linux、macOS 等多个系统上运行，并灵活适配 CPU、GPU、NPU 等各类硬件。作为一个轻量级且社区活跃的开源项目，PaddleOCR 既能满足快速集成的需求，也能支撑前沿的视觉语言研究，是处理文字识别任务的理想选择。",75370,"2026-04-11T11:15:34",[19,13,20,18],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":29,"last_commit_at":63,"category_tags":64,"status":22},3215,"awesome-machine-learning","josephmisiti\u002Fawesome-machine-learning","awesome-machine-learning 是一份精心整理的机器学习资源清单，汇集了全球优秀的机器学习框架、库和软件工具。面对机器学习领域技术迭代快、资源分散且难以甄选的痛点，这份清单按编程语言（如 Python、C++、Go 等）和应用场景（如计算机视觉、自然语言处理、深度学习等）进行了系统化分类，帮助使用者快速定位高质量项目。\n\n它特别适合开发者、数据科学家及研究人员使用。无论是初学者寻找入门库，还是资深工程师对比不同语言的技术选型，都能从中获得极具价值的参考。此外，清单还延伸提供了免费书籍、在线课程、行业会议、技术博客及线下聚会等丰富资源，构建了从学习到实践的全链路支持体系。\n\n其独特亮点在于严格的维护标准：明确标记已停止维护或长期未更新的项目，确保推荐内容的时效性与可靠性。作为机器学习领域的“导航图”，awesome-machine-learning 以开源协作的方式持续更新，旨在降低技术探索门槛，让每一位从业者都能高效地站在巨人的肩膀上创新。",72149,"2026-04-03T21:50:24",[20,18],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":77,"owner_company":77,"owner_location":77,"owner_email":77,"owner_twitter":77,"owner_website":77,"owner_url":79,"languages":77,"stars":80,"forks":81,"last_commit_at":82,"license":77,"difficulty_score":83,"env_os":84,"env_gpu":85,"env_ram":85,"env_deps":86,"category_tags":89,"github_topics":77,"view_count":10,"oss_zip_url":77,"oss_zip_packed_at":77,"status":22,"created_at":90,"updated_at":91,"faqs":92,"releases":93},6636,"jwwthu\u002FGNN4Traffic","GNN4Traffic","This is the repository for the collection of Graph Neural Network for Traffic Forecasting.","GNN4Traffic 是一个专注于交通预测领域的图神经网络（GNN）开源资源库。它系统性地收集、整理了大量利用图神经网络技术进行交通流量、速度及拥堵预测的学术论文、代码实现及相关数据集。\n\n在智慧交通场景中，道路网络具有复杂的非欧几里得空间结构，传统方法难以有效捕捉路网节点间的空间依赖关系。GNN4Traffic 通过汇聚前沿的 GNN 模型，帮助研究者和开发者解决如何精准建模时空数据、提升交通状态预测准确率的难题。\n\n该资源库特别适合人工智能研究人员、交通工程学者以及从事智慧城市开发的算法工程师使用。无论是希望快速了解领域发展脉络的初学者，还是寻求最新模型架构进行二次开发的资深从业者，都能从中获益。其核心亮点在于不仅提供了详尽的论文清单和统计图表，展示了该领域的年度增长趋势与顶级发表渠道，还关联了多个高质量的时空数据挖掘相关仓库与专用数据集。此外，维护团队在该领域发表了多篇权威综述，为使用者提供了坚实的理论指引，是进入图神经网络交通预测研究的高效入口。","# GNN4Traffic\r\nThis is the repository for the collection of Graph Neural Network for Traffic Forecasting.\r\n\r\nIf you find this repository helpful, you may consider cite our relevant work:\r\n* Jiang W, Luo J. \u003Cb>Graph Neural Network for Traffic Forecasting: A Survey[J]\u003C\u002Fb>. Expert Systems with Applications, 2022. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417422011654)\r\n* Jiang W, Luo J. \u003Cb>Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools[J]\u003C\u002Fb>. Applied System Innovation. 2022; 5(1):23. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2571-5577\u002F5\u002F1\u002F23)\r\n* Jiang W. \u003Cb>Bike sharing usage prediction with deep learning: a survey[J]\u003C\u002Fb>. Neural Computing and Applications, 2022, 34(18): 15369-15385. [Link](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-022-07380-5)\r\n* Jiang W, Luo J, He M, Gu W. \u003Cb>Graph Neural Network for Traffic Forecasting: The Research Progress[J]\u003C\u002Fb>. ISPRS International Journal of Geo-Information, 2023. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2220-9964\u002F12\u002F3\u002F100)\r\n\r\nFor a wider collection of deep learning for traffic forecasting, you may check: [DL4Traffic](https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FDL4Traffic)\r\n\r\n**Advertisement**: We would like to cordially invite you to submit a paper to our special issue on \"Graph Neural Network for Traffic Forecasting\" for **Information Fusion (SCI-indexed, Impact Factor: 17.564)**.\r\n* Special issue website: [https:\u002F\u002Fwww.sciencedirect.com\u002Fjournal\u002Finformation-fusion\u002Fabout\u002Fcall-for-papers#graph-neural-network-for-traffic-forecasting](https:\u002F\u002Fwww.sciencedirect.com\u002Fjournal\u002Finformation-fusion\u002Fabout\u002Fcall-for-papers#graph-neural-network-for-traffic-forecasting)\r\n* Deadline for manuscript submissions: **1 December 2023**.\r\n\r\n**Advertisement**: We would like to cordially invite you to submit a paper to our Topical Collection on \"Deep Neural Networks for Traffic Forecasting\" for **Neural Computing and Applications (SCI-indexed, Impact Factor: 6.0)**.\r\n* Topical Collection website: [https:\u002F\u002Fwww.springer.com\u002Fjournal\u002F521\u002Fupdates\u002F26215426](https:\u002F\u002Fwww.springer.com\u002Fjournal\u002F521\u002Fupdates\u002F26215426)\r\n* Deadline for manuscript submissions: **1 April 2024**.\r\n\r\n**Advertisement**: If you are interested in maintaining this repository, feel free to drop me an email.\r\n\r\nSome simple paper statistics results are as follows.\r\n\r\nPaper year count:\r\n\r\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_26d8e467fa62.png)\r\n\r\nTop conferences with paper counts:\r\n\r\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_a58526a2304e.png)\r\n\r\nTop journals with paper counts:\r\n\r\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_259ed3056698.png)\r\n\r\n# Relevant Repositories\r\n* Deep Learning Time Series Forecasting [Link](https:\u002F\u002Fgithub.com\u002FAlro10\u002Fdeep-learning-time-series)\r\n\r\n* A collection of research on spatio-temporal data mining [Link](https:\u002F\u002Fgithub.com\u002Fxiepeng21\u002Fresearch_spatio-temporal-data-mining)\r\n\r\n* Some TrafficFlowForecasting Solutions [Link](https:\u002F\u002Fgithub.com\u002Fxiaoxiong74\u002FTrafficFlowForecasting)\r\n\r\n* Urban-computing-papers [Link](https:\u002F\u002Fgithub.com\u002FKnowledge-Precipitation-Tribe\u002FSpatio-Temporal-papers)\r\n\r\n* Awesome-Mobility-Machine-Learning-Contents [Link](https:\u002F\u002Fgithub.com\u002Fzzsza\u002FAwesome-Mobility-Machine-Learning-Contents\u002Fblob\u002Fmaster\u002FREADME.md)\r\n\r\n* Traffic Prediction [Link](https:\u002F\u002Fgithub.com\u002Faprbw\u002Ftraffic_prediction)\r\n\r\n* Paper & Code & Dataset Collection of Spatial-Temporal Data Mining. [Link](https:\u002F\u002Fgithub.com\u002FNickHan-cs\u002FSpatio-Temporal-Data-Mining-Survey)\r\n\r\n# Relevant Data Repositories\r\n* Strategic Transport Planning Dataset [Link](https:\u002F\u002Fgithub.com\u002Fnikita68\u002FTransportPlanningDataset)\r\n> Description: A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning. Based on simulation results of the Four Step Model in PTV Visum.\r\n> Relevant Thesis: [Development of a Deep Learning Surrogate for the Four-Step Transportation Model](https:\u002F\u002Fmediatum.ub.tum.de\u002Fdoc\u002F1638691\u002Fdwz10x0l0w38xdklv9zkrprqs.pdf)\r\n* Zhang Y, Gong Q, Chen Y, et al. \u003Cb>A Human Mobility Dataset Collected via LBSLab[J]\u003C\u002Fb>. Data in Brief, 2023: 108898. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2352340923000161) [Data](https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.15000384.v3)\r\n* Jiang R, Cai Z, Wang Z, et al. \u003Cb>Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka[C]\u003C\u002Fb>\u002F\u002F2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 6676-6677. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10020886\u002F) [Data](https:\u002F\u002Fgithub.com\u002Fdeepkashiwa20\u002FDeepCrowd)\r\n\r\n# 2024\r\n## Journal\r\n* Ju W, Zhao Y, et al. \u003Cb>COOL: A conjoint perspective on spatio-temporal graph neural network for traffic forecasting[J]\u003C\u002Fb>. Information Fusion, 2024. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1566253524001192)\r\n* Fang S, Ji W, Xiang S, et al. \u003Cb>PreSTNet: Pre-trained Spatio-Temporal Network for traffic forecasting[J]\u003C\u002Fb>. Information Fusion, 2024, 106: 102241. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1566253524000198) [Code](https:\u002F\u002Fgithub.com\u002FWoodSugar\u002FPreSTNet)\r\n\r\n## Preprint\r\n* Li H, Zhao Y, et al. \u003Cb>A Survey on Graph Neural Networks in Intelligent Transportation Systems[J]\u003C\u002Fb>. arXiv preprint arXiv:2401.00713, 2024. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.00713)\r\n\r\n# 2023\r\n## Journal\r\n* Qi X, Yao J, Wang P, et al. \u003Cb>Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach[J]\u003C\u002Fb>. IET Intelligent Transport Systems, 2023. [Link](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1049\u002Fitr2.12401)\r\n* Tian R, Wang C, Hu J, et al. \u003Cb>MFSTGN: a multi-scale spatial-temporal fusion graph network for traffic prediction[J]\u003C\u002Fb>. Applied Intelligence, 2023: 1-20. [Link](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-023-04703-4)\r\n* Zhao W, Zhang S, Zhou B, et al. \u003Cb>Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting[J]\u003C\u002Fb>. Engineering Applications of Artificial Intelligence, 2023, 124: 106615. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197623007996)\r\n* Zhou J, Qin X, Ding Y, et al. \u003Cb>Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting[J]\u003C\u002Fb>. Mathematics, 2023, 11(13): 2867. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F11\u002F13\u002F2867)\r\n* Wang C, Wang L, Wei S, et al. \u003Cb>STN-GCN: Spatial and Temporal Normalization Graph Convolutional Neural Networks for Traffic Flow Forecasting[J]\u003C\u002Fb>. Electronics, 2023, 12(14): 3158. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2079-9292\u002F12\u002F14\u002F3158)\r\n* Cheng X, He Y, Zhang P, et al. \u003Cb>Traffic flow prediction based on information aggregation and comprehensive temporal-spatial synchronous graph neural network[J]\u003C\u002Fb>. IEEE Access, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10068539\u002F)\r\n* Zhao Z, Shen G, Zhou J, et al. \u003Cb>Spatial-temporal hypergraph convolutional network for traffic forecasting[J]\u003C\u002Fb>. PeerJ Computer Science, 2023, 9: e1450. 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Accepted at the International Conference on Machine Learning (ICML) 2021. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.04100) [Code](https:\u002F\u002Fgithub.com\u002FZ-GCNETs\u002FZ-GCNETs.git)\r\n\r\n## Book Chapter\r\n* Xu D, Dai H, Xuan Q. \u003Cb>Graph Convolutional Recurrent Neural Networks: A Deep Learning Framework for Traffic Prediction[M]\u003C\u002Fb>\u002F\u002FGraph Data Mining. Springer, Singapore, 2021: 189-204. [Link](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-16-2609-8_9)\r\n\r\n## Preprint\r\n* Fu J, Zhou W, Chen Z. \u003Cb>Bayesian Graph Convolutional Network for Traffic Prediction[J]\u003C\u002Fb>. arXiv preprint arXiv:2104.00488, 2021. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.00488)\r\n\r\n* Fang Y, Qin Y, Luo H, et al. \u003Cb>CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting[J]\u003C\u002Fb>. arXiv preprint arXiv:2112.02736, 2021. 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[Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8997248\u002F)\r\n\r\n* Guo S, Lin Y, Feng N, et al. \u003Cb>Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]\u003C\u002Fb>\u002F\u002FProceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 922-929. [Link](https:\u002F\u002Fwww.aaai.org\u002Fojs\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F3881) [Code-gluon](https:\u002F\u002Fgithub.com\u002Fwanhuaiyu\u002FASTGCN) [Code-pytorch](https:\u002F\u002Fgithub.com\u002Fwanhuaiyu\u002FASTGCN-r-pytorch) [Code1](https:\u002F\u002Fgithub.com\u002FguoshnBJTU\u002FASTGCN-r-pytorch)\r\n\r\n* Guo R, Jiang Z, Huang J, et al. \u003Cb>BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing[C]\u003C\u002Fb>\u002F\u002F2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\u002FSCALCOM\u002FUIC\u002FATC\u002FCBDCom\u002FIOP\u002FSCI). IEEE, 2019: 686-693. 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[Link](https:\u002F\u002Fwww.aaai.org\u002Fojs\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F3821)\r\n\r\n* Zhang Y, Wang S, Chen B, et al. \u003Cb>GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction[C]\u003C\u002Fb>\u002F\u002F2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019: 1-8. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8852211\u002F)\r\n\r\n* Cirstea R G, Guo C, Yang B. \u003Cb>Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting[C]\u003C\u002Fb>. MiLeTS’19, Anchorage, Alaska, USA, 2019. [Link](https:\u002F\u002Fmilets19.github.io\u002Fpapers\u002Fmilets19_paper_8.pdf)\r\n\r\n* Jepsen T S, Jensen C S, Nielsen T D. \u003Cb>Graph convolutional networks for road networks[C]\u003C\u002Fb>\u002F\u002FProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019: 460-463. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3347146.3359094) [Code](https:\u002F\u002Fgithub.com\u002FTobiasSkovgaardJepsen\u002Frelational-fusion-networks)\r\n\r\n* Wu Z, Pan S, Long G, et al. \u003Cb>Graph wavenet for deep spatial-temporal graph modeling[C]\u003C\u002Fb>. \u002F\u002FProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019. [Link](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2019\u002F0264) [Code](https:\u002F\u002Fgithub.com\u002Fnnzhan\u002FGraph-WaveNet)\r\n\r\n* Fang S, Zhang Q, Meng G, et al. \u003Cb>Gstnet: Global spatial-temporal network for traffic flow prediction[C]\u003C\u002Fb>\u002F\u002FProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019: 10-16. [Link](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2019\u002F0317)\r\n\r\n* Kang Z, Xu H, Hu J, et al. \u003Cb>Learning Dynamic Graph Embedding for Traffic Flow Forecasting: A Graph Self-Attentive Method[C]\u003C\u002Fb>\u002F\u002F2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 2570-2576. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917213\u002F)\r\n\r\n* Lu Z, Lv W, Xie Z, et al. \u003Cb>Leveraging Graph Neural Network with LSTM For Traffic Speed Prediction[C]\u003C\u002Fb>\u002F\u002F2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\u002FSCALCOM\u002FUIC\u002FATC\u002FCBDCom\u002FIOP\u002FSCI). IEEE, 2019: 74-81. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9060351)\r\n\r\n* Zhang T, Jin J, Yang H, et al. \u003Cb>Link speed prediction for signalized urban traffic network using a hybrid deep learning approach[C]\u003C\u002Fb>\u002F\u002F2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 2195-2200. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917509\u002F)\r\n\r\n* Wright M A, Ehlers S F G, Horowitz R. \u003Cb>Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs[C]\u003C\u002Fb>\u002F\u002F2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 3898-3905. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917174\u002F) [Code](https:\u002F\u002Fgithub.com\u002Fmawright\u002Ftrafficgraphnn)\r\n\r\n* James J Q. \u003Cb>Online Traffic Speed Estimation for Urban Road Networks with Few Data: A Transfer Learning Approach[C]\u003C\u002Fb>\u002F\u002F2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 4024-4029. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917502\u002F)\r\n\r\n* Wang Y, Yin H, Chen H, et al. \u003Cb>Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling[C]\u003C\u002Fb>\u002F\u002FProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1227-1235. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3292500.3330877)\r\n\r\n* Hasanzadeh A, Liu X, Duffield N, et al. \u003Cb>Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction[C]\u003C\u002Fb>\u002F\u002F2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019: 3779-3788. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9005965\u002F)\r\n\r\n* Yoshida A, Yatsushiro Y, Hata N, et al. \u003Cb>Practical End-to-End Repositioning Algorithm for Managing Bike-Sharing System[C]\u003C\u002Fb>\u002F\u002F2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019: 1251-1258. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9005986\u002F)\r\n\r\n* Opolka F L, Solomon A, Cangea C, et al. \u003Cb>Spatio-temporal deep graph infomax[C]\u003C\u002Fb>. Representation Learning on Graphs and Manifolds, ICLR 2019 Workshop. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.06316)\r\n\r\n* Bai L, Yao L, Kanhere S S, et al. \u003Cb>Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction[C]\u003C\u002Fb>\u002F\u002FProceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). 2019: 2293-2296. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3357384.3358097)\r\n\r\n* Geng X, Li Y, Wang L, et al. \u003Cb>Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[C]\u003C\u002Fb>\u002F\u002FProceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 3656-3663. [Link](https:\u002F\u002Fwww.aaai.org\u002Fojs\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F4247)\r\n\r\n* Bai L, Yao L, Kanhere S S, et al. \u003Cb>STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting[C]\u003C\u002Fb>\u002F\u002FProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019: 1981-1987. [Link](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2019\u002F0274)\r\n\r\n* Ge L, Li H, Liu J, et al. \u003Cb>Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors[C]\u003C\u002Fb>\u002F\u002F2019 20th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2019: 234-242. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8788749\u002F)\r\n\r\n* Ge L, Li H, Liu J, et al. \u003Cb>Traffic Speed Prediction with Missing Data Based on TGCN[C]\u003C\u002Fb>\u002F\u002F2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\u002FSCALCOM\u002FUIC\u002FATC\u002FCBDCom\u002FIOP\u002FSCI). IEEE, 2019: 522-529. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9060248)\r\n\r\n* Ren Y, Xie K. \u003Cb>Transfer Knowledge Between Sub-regions for Traffic Prediction Using Deep Learning Method[C]\u003C\u002Fb>\u002F\u002FInternational Conference on Intelligent Data Engineering and Automated Learning. Springer, Cham, 2019: 208-219. [Link](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-33607-3_23)\r\n\r\n* Pan Z, Liang Y, Wang W, et al. \u003Cb>Urban traffic prediction from spatio-temporal data using deep meta learning[C]\u003C\u002Fb>\u002F\u002FProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1720-1730. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3292500.3330884) [Code](https:\u002F\u002Fgithub.com\u002Fpanzheyi\u002FST-MetaNet)\r\n\r\n## Preprint\r\n* Yu B, Li M, Zhang J, et al. \u003Cb>3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting[J]\u003C\u002Fb>. arXiv preprint arXiv:1903.00919, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.00919)\r\n\r\n* Zhang N, Guan X, Cao J, et al. \u003Cb>A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network[J]\u003C\u002Fb>. arXiv preprint arXiv:1904.06656, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.06656)\r\n\r\n* Lee D, Jung S, Cheon Y, et al. \u003Cb>Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding[J]\u003C\u002Fb>. arXiv preprint arXiv:1905.10709, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10709) [Code](https:\u002F\u002Fgithub.com\u002FLeeDoYup\u002FTGNet-keras)\r\n\r\n* Lee K, Rhee W. \u003Cb>Graph Convolutional Modules for Traffic Forecasting[J]\u003C\u002Fb>. arXiv preprint arXiv:1905.12256, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12256)\r\n\r\n* Lu M, Zhang K, Liu H, et al. \u003Cb>Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction[J]\u003C\u002Fb>. arXiv preprint arXiv:1903.06261, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.06261)\r\n\r\n* Shleifer S, McCreery C, Chitters V. \u003Cb>Incrementally Improving Graph WaveNet Performance on Traffic Prediction[J]\u003C\u002Fb>. arXiv preprint arXiv:1912.07390, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.07390) [Code](https:\u002F\u002Fgithub.com\u002Fsshleifer\u002FGraph-WaveNet)\r\n\r\n* Geng X, Wu X, Zhang L, et al. \u003Cb>Multi-modal graph interaction for multi-graph convolution network in urban spatiotemporal forecasting[J]\u003C\u002Fb>. arXiv preprint arXiv:1905.11395, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.11395)\r\n\r\n* Zhou X, Shen Y, Huang L. \u003Cb>Revisiting Flow Information for Traffic Prediction[J]\u003C\u002Fb>. arXiv preprint arXiv:1906.00560, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00560)\r\n\r\n* Yu B, Yin H, Zhu Z. \u003Cb>ST-UNet: A spatio-temporal U-network for graph-structured time series modeling[J]\u003C\u002Fb>. arXiv preprint arXiv:1903.05631, 2019. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.05631)\r\n\r\n# 2018\r\n\r\n## Journal\r\n* Lin L, He Z, Peeta S. \u003Cb>Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach[J]\u003C\u002Fb>. Transportation Research Part C: Emerging Technologies, 2018, 97: 258-276. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X18300974)\r\n\r\n## Conference\r\n* Chai D, Wang L, Yang Q. \u003Cb>Bike flow prediction with multi-graph convolutional networks[C]\u003C\u002Fb>\u002F\u002FProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2018: 397-400. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3274895.3274896) [Code](https:\u002F\u002Fgithub.com\u002FDi-Chai\u002FGraphCNN-Bike)\r\n\r\n* Liao B, Zhang J, Wu C, et al. \u003Cb>Deep sequence learning with auxiliary information for traffic prediction[C]\u003C\u002Fb>\u002F\u002FProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 537-546. [Link](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3219819.3219895) [Code](https:\u002F\u002Fgithub.com\u002FJingqingZ\u002FBaiduTraffic)\r\n\r\n* Li Y, Yu R, Shahabi C, Liu Y, \u003Cb>Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting[C]\u003C\u002Fb>, ICLR 2018. [Link](https:\u002F\u002Fopenreview.net\u002Fpdf?id=SJiHXGWAZ) [Code-tensorflow](https:\u002F\u002Fgithub.com\u002Fliyaguang\u002FDCRNN) [Code-pytorch](https:\u002F\u002Fgithub.com\u002Fchnsh\u002FDCRNN_PyTorch)\r\n\r\n* Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. (2018). \u003Cb>GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs\u003C\u002Fb>. UAI. [Link](http:\u002F\u002Fauai.org\u002Fuai2018\u002Fproceedings\u002Fpapers\u002F139.pdf) [Code](https:\u002F\u002Fgithub.com\u002Fjennyzhang0215\u002FGaAN)\r\n\r\n* Wu T, Chen F, Wan Y. \u003Cb>Graph Attention LSTM Network: A New Model for Traffic Flow Forecasting[C]\u003C\u002Fb>\u002F\u002F2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018: 241-245. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8612556)\r\n\r\n* Wang B, Luo X, Zhang F, et al. \u003Cb>Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data[C]\u003C\u002Fb>. MiLeTS’18, London, United Kingdom, 2018. [Link](https:\u002F\u002Fmilets18.github.io\u002Fpapers\u002Fmilets18_paper_6.pdf)\r\n\r\n* Li J, Peng H, Liu L, et al. \u003Cb>Graph CNNs for urban traffic passenger flows prediction[C]\u003C\u002Fb>\u002F\u002F2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\u002FSCALCOM\u002FUIC\u002FATC\u002FCBDCom\u002FIOP\u002FSCI). IEEE, 2018: 29-36. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8560019\u002F) [Code](https:\u002F\u002Fgithub.com\u002FRingBDStack\u002FGCNN-In-Traffic)\r\n\r\n* Mohanty S, Pozdnukhov A. \u003Cb>Graph cnn+ lstm framework for dynamic macroscopic traffic congestion prediction[C]\u003C\u002Fb>\u002F\u002FInternational Workshop on Mining and Learning with Graphs. 2018. [Link](http:\u002F\u002Fwww.mlgworkshop.org\u002F2018\u002Fpapers\u002FMLG2018_paper_41.pdf) [Code](https:\u002F\u002Fgithub.com\u002Fsudatta0993\u002FDynamic-Congestion-Prediction)\r\n\r\n* Zhang Q, Jin Q, Chang J, et al. \u003Cb>Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting[C]\u003C\u002Fb>\u002F\u002F2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1018-1023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8545106\u002F)\r\n\r\n* Yu B, Yin H, Zhu Z. \u003Cb>Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting[C]\u003C\u002Fb>\u002F\u002FProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2018. [Link](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2018\u002F0505) [Code1](https:\u002F\u002Fgithub.com\u002FVeritasYin\u002FSTGCN_IJCAI-18) [Code2](https:\u002F\u002Fgithub.com\u002FDavidham3\u002FSTGCN) [Code3](https:\u002F\u002Fgithub.com\u002FPKUAI26\u002FSTGCN-IJCAI-18)\r\n\r\n## Preprint\r\n* Wang X, Chen C, Min Y, et al. \u003Cb>Efficient metropolitan traffic prediction based on graph recurrent neural network[J]\u003C\u002Fb>. arXiv preprint arXiv:1811.00740, 2018. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.00740) [Code](https:\u002F\u002Fgithub.com\u002FxxArbiter\u002Fgrnn)\r\n\r\n* Hu J, Guo C, Yang B, et al. \u003Cb>Recurrent Multi-Graph Neural Networks for Travel Cost Prediction[J]\u003C\u002Fb>. arXiv preprint arXiv:1811.05157, 2018. [Link](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.05157)\r\n","# GNN4Traffic\n这是用于收集交通预测图神经网络相关资源的仓库。\n\n如果您觉得本仓库有所帮助，欢迎引用我们的相关工作：\n* Jiang W, Luo J. \u003Cb>用于交通预测的图神经网络：综述[J]\u003C\u002Fb>. 专家系统及其应用，2022年。[链接](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417422011654)\n* Jiang W, Luo J. \u003Cb>用于交通估计与预测的大数据：数据与工具综述[J]\u003C\u002Fb>. 应用系统创新，2022年；5(1):23。[链接](https:\u002F\u002Fwww.mdpi.com\u002F2571-5577\u002F5\u002F1\u002F23)\n* Jiang W. \u003Cb>基于深度学习的共享单车使用量预测：综述[J]\u003C\u002Fb>. 神经计算与应用，2022年，34(18): 15369-15385。[链接](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-022-07380-5)\n* Jiang W, Luo J, He M, Gu W. \u003Cb>用于交通预测的图神经网络：研究进展[J]\u003C\u002Fb>. ISPRS国际地理信息期刊，2023年。[链接](https:\u002F\u002Fwww.mdpi.com\u002F2220-9964\u002F12\u002F3\u002F100)\n\n如需了解更多关于交通预测的深度学习资源，可查看：[DL4Traffic](https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FDL4Traffic)\n\n**广告**：我们诚挚邀请您向《信息融合》（SCI收录，影响因子：17.564）“用于交通预测的图神经网络”专题投稿。\n* 专题网站：[https:\u002F\u002Fwww.sciencedirect.com\u002Fjournal\u002Finformation-fusion\u002Fabout\u002Fcall-for-papers#graph-neural-network-for-traffic-forecasting](https:\u002F\u002Fwww.sciencedirect.com\u002Fjournal\u002Finformation-fusion\u002Fabout\u002Fcall-for-papers#graph-neural-network-for-traffic-forecasting)\n* 投稿截止日期：**2023年12月1日**。\n\n**广告**：我们诚挚邀请您向《神经计算与应用》（SCI收录，影响因子：6.0）“用于交通预测的深度神经网络”专题投稿。\n* 专题网站：[https:\u002F\u002Fwww.springer.com\u002Fjournal\u002F521\u002Fupdates\u002F26215426](https:\u002F\u002Fwww.springer.com\u002Fjournal\u002F521\u002Fupdates\u002F26215426)\n* 投稿截止日期：**2024年4月1日**。\n\n**广告**：如果您有兴趣维护本仓库，请随时给我发送邮件。\n\n以下是一些简单的论文统计结果。\n\n论文年度分布：\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_26d8e467fa62.png)\n\n发表论文最多的顶级会议：\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_a58526a2304e.png)\n\n发表论文最多的顶级期刊：\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_readme_259ed3056698.png)\n\n# 相关仓库\n* 深度学习时间序列预测 [链接](https:\u002F\u002Fgithub.com\u002FAlro10\u002Fdeep-learning-time-series)\n\n* 时空数据挖掘研究合集 [链接](https:\u002F\u002Fgithub.com\u002Fxiepeng21\u002Fresearch_spatio-temporal-data-mining)\n\n* 一些交通流量预测解决方案 [链接](https:\u002F\u002Fgithub.com\u002Fxiaoxiong74\u002FTrafficFlowForecasting)\n\n* 城市计算论文 [链接](https:\u002F\u002Fgithub.com\u002FKnowledge-Precipitation-Tribe\u002FSpatio-Temporal-papers)\n\n* 优秀移动机器学习资源 [链接](https:\u002F\u002Fgithub.com\u002Fzzsza\u002FAwesome-Mobility-Machine-Learning-Contents\u002Fblob\u002Fmaster\u002FREADME.md)\n\n* 交通预测 [链接](https:\u002F\u002Fgithub.com\u002Faprbw\u002Ftraffic_prediction)\n\n* 时空数据挖掘论文、代码与数据集合集。[链接](https:\u002F\u002Fgithub.com\u002FNickHan-cs\u002FSpatio-Temporal-Data-Mining-Survey)\n\n# 相关数据仓库\n* 战略交通规划数据集 [链接](https:\u002F\u002Fgithub.com\u002Fnikita68\u002FTransportPlanningDataset)\n> 描述：基于图的战略交通规划数据集，旨在构建下一代用于迁移学习的深度图神经网络。基于PTV Visum中四步法模型的仿真结果。\n> 相关论文：[四步交通模型的深度学习代理开发](https:\u002F\u002Fmediatum.ub.tum.de\u002Fdoc\u002F1638691\u002Fdwz10x0l0w38xdklv9zkrprqs.pdf)\n* Zhang Y, Gong Q, Chen Y等。\u003Cb>通过LBSLab收集的人类移动数据集[J]\u003C\u002Fb>. 数据简报，2023年：108898。[链接](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2352340923000161) [数据](https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.15000384.v3)\n* Jiang R, Cai Z, Wang Z等。\u003Cb>雅虎防灾众包数据：东京和大阪的大规模人群密度与流量数据[C]\u003C\u002Fb>\u002F\u002F2022年IEEE大数据国际会议（Big Data）。IEEE，2022年：6676-6677。[链接](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10020886\u002F) [数据](https:\u002F\u002Fgithub.com\u002Fdeepkashiwa20\u002FDeepCrowd)\n\n# 2024\n## 期刊\n* Ju W, Zhao Y等。\u003Cb>COOL：一种结合视角的时空图神经网络用于交通预测[J]\u003C\u002Fb>. 信息融合，2024年。[链接](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1566253524001192)\n* Fang S, Ji W, Xiang S等。\u003Cb>PreSTNet：用于交通预测的预训练时空网络[J]\u003C\u002Fb>. 信息融合，2024年，第106卷：102241。[链接](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1566253524000198) [代码](https:\u002F\u002Fgithub.com\u002FWoodSugar\u002FPreSTNet)\n\n## 预印本\n* Li H, Zhao Y等。\u003Cb>智能交通系统中的图神经网络综述[J]\u003C\u002Fb>. arXiv预印本arXiv:2401.00713，2024年。[链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.00713)\n\n# 2023\n\n## Journal\r\n* Qi X, Yao J, Wang P, et al. \u003Cb>Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach[J]\u003C\u002Fb>. IET Intelligent Transport Systems, 2023. [Link](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1049\u002Fitr2.12401)\r\n* Tian R, Wang C, Hu J, et al. \u003Cb>MFSTGN: a multi-scale spatial-temporal fusion graph network for traffic prediction[J]\u003C\u002Fb>. Applied Intelligence, 2023: 1-20. [Link](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-023-04703-4)\r\n* Zhao W, Zhang S, Zhou B, et al. \u003Cb>Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting[J]\u003C\u002Fb>. Engineering Applications of Artificial Intelligence, 2023, 124: 106615. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197623007996)\r\n* Zhou J, Qin X, Ding Y, et al. \u003Cb>Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting[J]\u003C\u002Fb>. Mathematics, 2023, 11(13): 2867. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F11\u002F13\u002F2867)\r\n* Wang C, Wang L, Wei S, et al. \u003Cb>STN-GCN: Spatial and Temporal Normalization Graph Convolutional Neural Networks for Traffic Flow Forecasting[J]\u003C\u002Fb>. Electronics, 2023, 12(14): 3158. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2079-9292\u002F12\u002F14\u002F3158)\r\n* Cheng X, He Y, Zhang P, et al. \u003Cb>Traffic flow prediction based on information aggregation and comprehensive temporal-spatial synchronous graph neural network[J]\u003C\u002Fb>. IEEE Access, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10068539\u002F)\r\n* Zhao Z, Shen G, Zhou J, et al. \u003Cb>Spatial-temporal hypergraph convolutional network for traffic forecasting[J]\u003C\u002Fb>. PeerJ Computer Science, 2023, 9: e1450. [Link](https:\u002F\u002Fpeerj.com\u002Farticles\u002Fcs-1450\u002F) [Code](http:\u002F\u002Fdx.doi.org\u002F10.7717\u002Fpeerj-cs.1450#supplemental-information)\r\n* Liang G, Kintak U, Ning X, et al. \u003Cb>Semantics-aware dynamic graph convolutional network for traffic flow forecasting[J]\u003C\u002Fb>. IEEE Transactions on Vehicular Technology, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10032116\u002F) [Code](https:\u002F\u002Fgithub.com\u002Fgorgen2020\u002FSDGCN)\r\n* Wen Y, Li Z, Wang X, et al. \u003Cb>Traffic demand prediction based on spatial-temporal guided multi graph Sandwich-Transformer[J]\u003C\u002Fb>. Information Sciences, 2023: 119269. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS002002552300854X) [Code](https:\u002F\u002Fgithub.com\u002FYanJieWen\u002FSTGMT-Tensorflow-implementation)\r\n* Hu S, Ye Y, Hu Q, et al. \u003Cb>A Federated Learning-Based Framework for Ride-sourcing Traffic Demand Prediction[J]\u003C\u002Fb>. IEEE Transactions on Vehicular Technology, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10155190\u002F)\r\n* Ouyang X, Yang Y, Zhou W, et al. \u003Cb>CityTrans: Domain-Adversarial Training with Knowledge Transfer for Spatio-Temporal Prediction across Cities[J]\u003C\u002Fb>. IEEE Transactions on Knowledge and Data Engineering, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10145833\u002F)\r\n* Hu C, Liu X, Wu S, et al. \u003Cb>Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure[J]\u003C\u002Fb>. Applied Sciences, 2023, 13(12): 7271. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F13\u002F12\u002F7271)\r\n* Ma C, Sun K, Chang L, et al. \u003Cb>Enhanced Information Graph Recursive Network for Traffic Forecasting[J]\u003C\u002Fb>. Electronics, 2023, 12(11): 2519. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2079-9292\u002F12\u002F11\u002F2519)\r\n* García-Sigüenza J, Llorens-Largo F, Tortosa L, et al. \u003Cb>Explainability techniques applied to road traffic forecasting using Graph Neural Network models[J]\u003C\u002Fb>. Information Sciences, 2023: 119320. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523009052)\r\n* Liu T, Jiang A, Zhou J, et al. \u003Cb>GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction[J]\u003C\u002Fb>. IEEE Transactions on Intelligent Transportation Systems, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10143385\u002F)\r\n* Yu W, Huang X, Qiu Y, et al. \u003Cb>GSTC-Unet: A U-shaped multi-scaled spatiotemporal graph convolutional network with channel self-attention mechanism for traffic flow forecasting[J]\u003C\u002Fb>. Expert Systems with Applications, 2023: 120724. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417423012265)\r\n* Li Z, Han Y, Xu Z, et al. \u003Cb>PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting[J]\u003C\u002Fb>. ISPRS International Journal of Geo-Information, 2023, 12(6): 241. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2220-9964\u002F12\u002F6\u002F241)\r\n* Ning T, Wang J, Duan X. \u003Cb>Research on expressway traffic flow prediction model based on MSTA-GCN[J]\u003C\u002Fb>. Journal of Ambient Intelligence and Humanized Computing, 2022: 1-12. [Link](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12652-022-04431-6)\r\n* Zhang Q, Li C, Su F, et al. \u003Cb>Spatio-Temporal Residual Graph Attention Network for Traffic Flow Forecasting[J]\u003C\u002Fb>. IEEE Internet of Things Journal, 2023. [Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10040629\u002F)\r\n* Chang Z, Liu C, Jia J. \u003Cb>STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction[J]\u003C\u002Fb>. Applied Sciences, 2023, 13(11): 6796. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F13\u002F11\u002F6796)\r\n* Yin L, Liu P, Wu Y, et al. \u003Cb>ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-temporal Multimodality[J]\u003C\u002Fb>. IEEE Access, 2023. 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[Link](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F23\u002F10\u002F4803) [Code](https:\u002F\u002Fgithub.com\u002Fcorrir\u002FTransformers-and-Graph-Convolutional-Networks-for-Human-Mobility-Modeling)\r\n* Lablack M, Shen Y. \u003Cb>Spatio-temporal graph mixformer for traffic forecasting[J]\u003C\u002Fb>. Expert Systems with Applications, 2023, 228: 120281. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0957417423007832) [Code](https:\u002F\u002Fgithub.com\u002Fmouradost\u002Fstgm)\r\n* Zhao J, Zhang R, Sun Q, et al. \u003Cb>Adaptive graph convolutional network-based short-term passenger flow prediction for metro[J]\u003C\u002Fb>. Journal of Intelligent Transportation Systems, 2023: 1-10. 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[Link](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10113777\u002F)\r\n* Feng X, Chen Y, Li H, et al. \u003Cb>Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction[J]\u003C\u002Fb>. Sustainability, 2023, 15(9): 7696. [Link](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F15\u002F9\u002F7696)\r\n* Ni Q, Peng W, Zhu Y, et al. \u003Cb>Graph dropout self-learning hierarchical graph convolution network for traffic prediction[J]\u003C\u002Fb>. Engineering Applications of Artificial Intelligence, 2023, 123: 106460. [Link](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197623006449)\r\n* Hu Y, Peng T, Guo K, et al. \u003Cb>Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting[J]\u003C\u002Fb>. IET Intelligent Transport Systems, 2023. 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[链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.06656)\n\n* Lee D, Jung S, Cheon Y, 等. \u003Cb>利用图网络和时间引导嵌入从时空数据中进行需求预测[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1905.10709, 2019. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10709) [代码](https:\u002F\u002Fgithub.com\u002FLeeDoYup\u002FTGNet-keras)\n\n* Lee K, Rhee W. \u003Cb>用于交通预测的图卷积模块[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1905.12256, 2019. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12256)\n\n* Lu M, Zhang K, Liu H, 等. \u003Cb>用于车辆状态预测的图层次卷积循环神经网络（GHCRNN）[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1903.06261, 2019. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.06261)\n\n* Shleifer S, McCreery C, Chitters V. \u003Cb>逐步提升图 WaveNet 在交通预测中的性能[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1912.07390, 2019. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.07390) [代码](https:\u002F\u002Fgithub.com\u002Fsshleifer\u002FGraph-WaveNet)\n\n* Geng X, Wu X, Zhang L, 等. \u003Cb>城市时空预测中多图卷积网络的多模态图交互[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1905.11395, 2019. 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[链接](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8560019\u002F) [代码](https:\u002F\u002Fgithub.com\u002FRingBDStack\u002FGCNN-In-Traffic)\n\n* Mohanty S, Pozdnukhov A. \u003Cb>图 CNN+LSTM 框架用于动态宏观交通拥堵预测[C]\u003C\u002Fb>\u002F\u002F国际图挖掘与学习研讨会。2018. [链接](http:\u002F\u002Fwww.mlgworkshop.org\u002F2018\u002Fpapers\u002FMLG2018_paper_41.pdf) [代码](https:\u002F\u002Fgithub.com\u002Fsudatta0993\u002FDynamic-Congestion-Prediction)\n\n* Zhang Q, Jin Q, Chang J, 等. \u003Cb>核加权图卷积网络：一种用于交通预测的深度学习方法[C]\u003C\u002Fb>\u002F\u002F2018 第 24 届国际模式识别大会（ICPR）。IEEE，2018：1018–1023. [链接](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8545106\u002F)\n\n* Yu B, Yin H, Zhu Z. \u003Cb>时空图卷积网络：一种用于交通预测的深度学习框架[C]\u003C\u002Fb>\u002F\u002F第 28 届国际人工智能联合会议（IJCAI）论文集. 2018. [链接](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2018\u002F0505) [代码1](https:\u002F\u002Fgithub.com\u002FVeritasYin\u002FSTGCN_IJCAI-18) [代码2](https:\u002F\u002Fgithub.com\u002FDavidham3\u002FSTGCN) [代码3](https:\u002F\u002Fgithub.com\u002FPKUAI26\u002FSTGCN-IJCAI-18)\n\n## 预印本\n* Wang X, Chen C, Min Y, 等. \u003Cb>基于图循环神经网络的高效都市交通预测[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1811.00740, 2018. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.00740) [代码](https:\u002F\u002Fgithub.com\u002FxxArbiter\u002Fgrnn)\n\n* Hu J, Guo C, Yang B, 等. \u003Cb>用于旅行成本预测的循环多图神经网络[J]\u003C\u002Fb>. arXiv 预印本 arXiv:1811.05157, 2018. [链接](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.05157)","# GNN4Traffic 快速上手指南\n\nGNN4Traffic 是一个专注于**交通预测领域图神经网络（GNN）**的论文与资源集合库。它主要收录了相关的学术论文、数据集链接以及代码实现索引，旨在为研究人员和开发者提供该领域的最新进展综述。\n\n> **注意**：本仓库主要作为“文献列表”和“资源索引”，而非一个可直接调用的单一 Python 软件包。以下指南将指导您如何获取资源、复现论文代码以及使用相关数据集。\n\n## 1. 环境准备\n\n由于本仓库包含指向多个不同论文代码实现的链接，具体的系统要求取决于您选择复现哪一篇论文。但大多数基于深度学习的交通预测模型通常具备以下通用前置依赖：\n\n*   **操作系统**: Linux (推荐 Ubuntu 18.04\u002F20.04) 或 macOS\n*   **Python 版本**: 3.7 - 3.9 (多数旧版论文代码兼容性较好)\n*   **核心框架**: PyTorch 或 TensorFlow (需根据具体论文代码确定)\n*   **图神经网络库**: PyTorch Geometric (PyG) 或 DGL\n*   **其他依赖**: NumPy, Pandas, Scikit-learn, Matplotlib\n\n**建议前置操作：**\n在开始之前，请确保已安装基础的 CUDA 驱动（如需 GPU 加速）和 Git。\n\n```bash\n# 检查 Python 版本\npython --version\n\n# 检查 Git\ngit --version\n```\n\n## 2. 安装步骤\n\n由于 GNN4Traffic 是资源集合，您首先需要克隆该仓库以获取最新的论文列表和数据集索引。随后，您需要根据感兴趣的论文，跳转到对应的子仓库进行具体环境的安装。\n\n### 第一步：克隆主仓库\n获取最新的论文列表和资源索引。\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FGNN4Traffic.git\ncd GNN4Traffic\n```\n\n### 第二步：选择并配置具体模型环境\n浏览仓库中的 `README.md` 或年份分类（如 `# 2023`, `# 2024`），找到您感兴趣的论文（例如 *PreSTNet* 或 *SDGCN*）。点击其提供的 **[Code]** 链接进入具体实现仓库。\n\n以复现 **PreSTNet** (2024) 为例，假设其代码仓库已打开，通用的安装流程如下：\n\n```bash\n# 1. 创建虚拟环境 (推荐)\nconda create -n gnn_traffic python=3.8\nconda activate gnn_traffic\n\n# 2. 安装 PyTorch (根据官网选择对应 CUDA 版本，此处以 CPU 或通用版为例)\n# 国内用户推荐使用清华源加速\npip install torch torchvision torchaudio --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu118\n\n# 3. 安装 PyTorch Geometric (图神经网络核心库)\n# 使用国内镜像源加速安装\npip install torch-geometric -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n\n# 4. 安装该特定模型的其他依赖\n# 进入具体代码目录后执行\npip install -r requirements.txt\n```\n\n## 3. 基本使用\n\n本仓库的核心价值在于**数据获取**与**模型复现**。以下是两个最典型的使用场景。\n\n### 场景一：获取交通预测数据集\n仓库中整理了多个高质量数据集（如 *Strategic Transport Planning Dataset*, *Yahoo! Bousai Crowd Data* 等）。\n\n1.  在 `GNN4Traffic` 的 README 中找到 **# Relevant Data Repositories** 部分。\n2.  点击下载链接或访问对应的 DOI 地址。\n3.  将下载的数据集放置在您具体模型项目的 `data\u002F` 目录下。\n\n**示例：准备数据目录结构**\n```bash\nmkdir -p data\u002Fraw\n# 假设您下载了 csv 格式的交通流数据\nmv downloaded_traffic_data.csv data\u002Fraw\u002F\n```\n\n### 场景二：运行一个基准模型 (以具体论文代码为例)\n一旦您进入了具体论文的代码仓库（例如从列表中找到的 *SDGCN* 或 *DDGCRN*），通常的使用流程如下：\n\n1.  **数据预处理**：运行脚本生成图结构或归一化数据。\n    ```bash\n    python preprocess.py --dataset your_dataset_name\n    ```\n\n2.  **训练模型**：使用默认参数启动训练。\n    ```bash\n    python train.py --config config\u002Fdefault.yaml --gpu 0\n    ```\n\n3.  **评估与预测**：在测试集上验证效果。\n    ```bash\n    python evaluate.py --checkpoint logs\u002Fbest_model.pth\n    ```\n\n### 进阶：查阅最新论文\n您可以直接在克隆后的本地 `README.md` 中搜索关键词（如 \"Transformer\", \"Federated Learning\", \"Dynamic Graph\"），快速定位 2023-2024 年的最新研究成果及其对应的代码链接，从而跟进最前沿的技术方案。\n\n```bash\n# 在本地搜索包含 \"Transformer\" 的论文条目\ngrep -i \"Transformer\" README.md\n```","某大型智慧城市的交通指挥中心正试图优化早晚高峰的信号灯配时策略，以缓解核心商圈的常态化拥堵。\n\n### 没有 GNN4Traffic 时\n- **模型选型迷茫**：面对海量的图神经网络论文，研发团队难以快速甄别哪些算法真正适用于复杂的城市路网拓扑，耗费数周时间进行文献调研。\n- **复现成本高昂**：缺乏统一的代码基准，工程师需从零搭建数据预处理管道，且不同论文的数据格式不兼容，导致实验环境配置频繁报错。\n- **预测精度受限**：传统时间序列模型无法有效捕捉路口间的空间依赖关系（如上游拥堵对下游的传导效应），导致短时流量预测误差率高达 25%。\n- **评估标准混乱**：缺乏权威的对比基线，团队难以量化新算法的实际提升效果，项目进度因反复验证而严重滞后。\n\n### 使用 GNN4Traffic 后\n- **技术路线清晰**：直接查阅 GNN4Traffic 整理的综述与分类列表，团队迅速锁定了适合动态路网的 ST-GCN 和 Graph WaveNet 等前沿模型作为候选方案。\n- **开发效率倍增**：利用仓库中集成的标准化数据集接口和参考实现，将原本需要两周的环境搭建与数据清洗工作缩短至两天内完成。\n- **时空特征精准捕获**：基于成熟的图神经网络架构，系统成功建模了路口间的空间关联性，将未来 15 分钟的流量预测误差率降低至 12% 以内。\n- **科学决策有据**：依托仓库提供的权威性能统计图表，团队快速完成了多模型横向对比，确立了最优部署方案并顺利上线。\n\nGNN4Traffic 通过提供一站式的算法集合与基准测试，将交通预测的研发周期从“月级”压缩至“周级”，显著提升了城市治堵的智能化水平。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjwwthu_GNN4Traffic_07b1f6b0.png","jwwthu",null,"https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjwwthu_9752916c.png","https:\u002F\u002Fgithub.com\u002Fjwwthu",1197,193,"2026-04-10T23:44:05",5,"","未说明",{"notes":87,"python":85,"dependencies":88},"该仓库是一个用于交通预测的图神经网络（GNN）论文和代码合集，而非单一的独立软件工具。README 中未提供具体的运行环境配置、依赖库版本或硬件需求。用户需根据仓库中列出的具体子项目（如 PreSTNet, SDGCN 等）的独立文档来配置相应的运行环境。",[],[18],"2026-03-27T02:49:30.150509","2026-04-11T23:24:19.860174",[],[]]