[参文]GCN+交通
同类型的文章
- 一、流量预测
- 1.1 Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
- 1.2 Graph WaveNet for Deep Spatial-Temporal Graph Modeling
- 1.3 T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
- 1.4 Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
- 1.5 Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
一、流量预测
1.1 Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
俗称：STGCN
文章解析,很优秀啊！
代码解读，指路
摘要
及时准确的交通预测对于城市交通控制和引导至关重要。由于交通流的高度非线性和复杂性，传统方法不能满足中长期预测任务的要求，往往忽略了空间和时间依赖性。在本文中，我们提出了一种新的深度学习框架，时空图卷积网络（STGCN），以解决交通领域的时间序列预测问题。我们不使用正则卷积和递归单元，而是在图上描述问题，并建立具有完整卷积结构的模型，这使得训练速度更快，参数更少。实验表明，我们的模型STGCN通过对多尺度交通网络进行建模，有效地捕获了全面的时空相关性，并且在各种真实世界的交通数据集上始终优于最先进的基线。abstract
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.图片要点
bib
@article{2017Spatio,title={Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},author={ Yu, Bing and Yin, Haoteng and Zhu, Zhanxing },year={2017},
}
1.2 Graph WaveNet for Deep Spatial-Temporal Graph Modeling
文章解析，优秀啊
ACM文章
摘要
时空图建模是分析系统中组件的空间关系和时间趋势的一项重要任务。现有的方法大多捕捉固定图形结构上的空间依赖性，假设实体之间的潜在关系是预先确定的。然而，显式图结构（关系）不一定反映真实的依赖关系，并且由于数据中的不完整连接，可能会丢失真实的关系。此外，现有的方法对于捕捉时间趋势是无效的，因为这些方法中使用的RNN或CNN不能捕捉长程时间序列。为了克服这些局限性，本文提出了一种新的用于时空图建模的图神经网络结构graph WaveNet。通过开发一种新的自适应依赖矩阵并通过节点嵌入进行学习，我们的模型可以精确地捕获数据中隐藏的空间依赖。Graph WaveNet具有一个堆叠的扩展一维卷积组件，其感受野随着层数的增加呈指数增长，能够处理非常长的序列。这两个组件在一个统一的框架中无缝集成，整个框架以端到端的方式学习。在两个公共交通网络数据集METR-LA和PEMS-BAY上的实验结果证明了我们算法的优越性。ABSTRACT
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.衍化过程
model图片
bib
@article{2019Graph,title={Graph WaveNet for Deep Spatial-Temporal Graph Modeling},author={ Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi },year={2019},
}
1.3 T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
文章解析，优秀啊
IEEE文章
摘要：
准确、实时的交通预测在智能交通系统中发挥着重要作用，对城市交通规划、交通管理和交通控制具有重要意义。然而，由于城市道路网络拓扑结构的约束和时间的动态变化规律，交通预测一直被认为是一个开放的科学问题。为了同时捕捉空间和时间的依赖关系，我们提出了一种新的基于神经网络的流量预测方法，即时态图卷积网络（T-GCN）模型，该模型与图卷积网络（GCN）和选通递归单元（GRU）相结合。具体来说，GCN用于学习复杂的拓扑结构以获取空间相关性，而选通递归单元用于学习交通数据的动态变化以获取时间相关性。然后，将T-GCN模型应用于基于城市道路网的交通预测。实验表明，我们的T-GCN模型可以从交通数据中获得时空相关性，并且预测性能优于现实世界交通数据集上的最新基线。T-GCN的tensorflow实现可在https://www.github.com/lehaifeng/T-GCNAbstract:
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCNmodel图片
bib
@article{2019T,title={T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction},author={ Zhao, L. and Song, Y. and Zhang, C. and Liu, Y. and Li, H. },journal={IEEE Transactions on Intelligent Transportation Systems},volume={PP},number={99},pages={1-11},year={2019},
}
1.4 Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
俗称：STSGCN
原文翻译
- 摘要
时空网络数据预测在交通管理和城市规划的大量应用中具有重要意义。然而，潜在的复杂时空相关性和异质性使这个问题具有挑战性。现有的方法通常使用单独的组件来捕获空间和时间相关性，而忽略了时空数据中的异质性。在本文中，我们提出了一种新的时空同步图卷积网络（STSGCN）模型，用于时空网络数据预测。该模型通过精心设计的时空同步建模机制，能够有效地捕捉复杂的局部时空相关性。同时，该模型针对不同的时间段设计了多个模块，以有效地捕捉局部时空图中的异质性。在四个真实数据集上进行了大量实验，结果表明我们的方法达到了最先进的性能，并且始终优于其他基线。 - Abstract
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines. - model图片
- bib
@article{2020Spatial,title={Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting},author={ Song, C. and Lin, Y. and Guo, S. and Wan, H. },journal={Proceedings of the AAAI Conference on Artificial Intelligence},volume={34},number={1},pages={914-921},year={2020},
}
1.5 Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
俗称：STFGNN
原文
文章翻译
文章代码：https://github.com/MengzhangLI/STFGNN
摘要
交通流的时空数据预测是一项具有挑战性的任务，因为不同道路之间存在复杂的空间相关性和时间模式的动态趋势。现有的框架通常利用给定的空间邻接图和复杂的机制来建模空间和时间相关性。然而，不完全相邻连接的给定空间图结构的有限表示可能会限制这些模型的有效时空依赖学习。为了克服这些局限性，本文提出了用于交通流预测的时空融合图神经网络（STFGNN）。SFTGNN通过数据驱动的方法生成各种时空图，通过一种新的融合操作，可以有效地学习隐藏的时空依赖关系。同时，通过将该融合图模块和一个新的门卷积模块集成到一个统一的层中，SFTGNN可以处理长序列。在多个公共交通数据集上的实验结果表明，与其他基线相比，我们的方法一致地实现了最先进的性能。Abstract
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. To overcome those limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer, SFTGNN could handle long sequences. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.model图片
bib
@article{2020STFGNN,title={Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting},author={ Li, M. and Zhu, Z. },year={2020},
}
[参文]GCN+交通相关推荐
- 1609【115号文】交通运输部办公厅关于推进改革试点加快无车承运物流创新发展的意见
参考115号文如下: 交通运输部办公厅关于推进改革试点加快无车承运物流创新发展的意见 为贯彻落实党中央.国务院关于大力推进大众创业万众创新和推动实施"互联网+"行动计划的有关部署, ...
- 一文解析交通大数据是如何解决拥堵问题的
前言 现在的社会是一个高速发展的社会,科技发达,信息流通,人们之间的交流越来越密切,生活也越来越方便,大数据就是这个高科技时代的产物.随着中国经济的快速发展,汽车行业空前繁荣,私人小汽车拥有量迅速增加 ...
- ieee39节点系统介绍_太原理工大学 秦文萍，逯瑞鹏等：大规模新能源接入对电气耦合综合能源系统稳定性有何影响？...
<电网技术>,值得信赖的电力学术期刊 戳题目,看全文! 计及大规模新能源接入的电-气耦合综合能源系统稳定性评估/秦文萍,逯瑞鹏,高蒙楠,景祥,郑惠萍,程雪婷 <电网技术>202 ...
- “守法规知礼让、安全文明出行”背后需要良好的交通环境支撑 | 聚焦守法知礼...
导读:用路人遵循交通规则.尊重道路和控制设施的前提是规则与设施的科学合理,言究社邀请资深交通工程师梁康之讲述职业生涯中亲历的3个真实故事,让我们以第一视角感受交通工程师眼中的"守法规知礼让. ...
- 【交通数据（1）——加州高速路网PeMS交通数据】
交通数据(1)--加州高速路网PeMS交通数据 一.PeMS数据介绍 1. 数据来源 2. 数据特点 3. 数据诊断与处理 4. PeMS数据格式 二.相关数据下载 1. 相关数据链接 2. 数据爬虫 ...
- Python爬虫下载加州高速路网PeMS交通流量数据集以及交通公开数据集分享
由于PeMS(PeMS)下载交通数据集一次只能下载一周数据,并且要一次一次的点击,比较麻烦. 故写出了一个小的爬虫程序,自动下载PeMS路网上VDS的数据集(前提需要FQ,需要挂全局代理) 下载为5分 ...
- 大模型的1000+篇文章总结
大模型的1000+篇文章总结 本文收集和总结了有关大模型的1000+篇文章,由于篇幅有限只能总结近期的内容,想了解更多内容可以访问:http://www.ai2news.com/, 其分享了有关AI的 ...
- 长短期记忆网络_昆明理工大学刘可真、苟家萁、骆钊等：粒子群优化长短期记忆网络模型如何进一步提高变压器油中溶解气体浓度的精准预测？...
<电网技术>,值得信赖的电力学术期刊 戳题目,看全文! 基于粒子群优化-长短期记忆网络模型的变压器油中溶解气体浓度预测方法/刘可真,苟家萁,骆钊,王科,徐肖伟,赵勇军. <电网技术& ...
- Python 十六进制转Base64_python基础day03笔记
嗨,大家上午好,今天又是分享笔记的一天,希望大家多多关注,谢谢. 希望大家喜欢,多关注,谢谢! 字符串str 作用: 用来记录文本(文字)信息 字符串的表示方式: 在非注释中,凡是用引号括起来的部分都 ...
最新文章
- 题解 P4692 【[Ynoi2016]谁的梦】
- 对称密码算法Rijndael解析—加密
- Linux中OBS在Wayland环境下黑屏只显示鼠标的应对措施
- 使用 RequireJS 优化 Web 应用前端
- Asp.Net用户控件编程实例
- FFmpeg 音视频处理总纲
- 如何使用单例EJB，Ehcache和MBean构建和清除参考数据缓存
- c语言disp是什么意思及用法,disp(disp是什么功能)
- 有助于改变你命运的5个处世潜规则
- XAMPP中mysql升级到5.7.36版本
- 2021新版外卖CPS 外卖侠CPS小程序
- 制作一个模拟Windows启动界面的文本进度条python
- 180729 5行命令win10企业版永久激活方法
- 整体大于部分_整体叶盘球头鼓锥形铣刀五轴加工技术
- 用计算机怎么算加权标准差,Tableau，如何计算加权标准差
- java 数独游戏_java数独游戏完整版分享
- 适合新手小白的苹果CMS安装与配置
- 生成自己的app key文件
- OpenCV轮廓最大内接矩形(带角度)-计算与绘制(Python / C++源码)
- 四色原型的学习心得分享