Knowledge Commons of Institute of Automation,CAS
Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting | |
Zhang Qi1,2; Jin Qizhao1; Chang Jianlong1,2; Xiang Shiming1,2; Pan Chunhong1 | |
2018-08 | |
会议名称 | International Conference on Pattern Recognition |
会议日期 | 2018-08 |
会议地点 | Beijing, China |
摘要 | Traffic forecasting is of great significance and has many applications in Intelligent Traffic System (ITS). In spite of many thoughtful attempts in the past decades, this task still remains far from being solved, due to the diversity, complexity and nonlinearity of traffic situations. Technically, it can be cast on the framework of regressions with spatial-template data. Typically, one may consider to employ the Convolutional Neural Network (CNN) to achieve this goal. Unfortunately, the traditional CNN is developed for grid data. By contrast, here we are facing with non-grid traffic data points that are observed spatially at locations of interest. To this end, this paper proposes a novel Kernel-Weighted Graph Convolutional Network (KW-GCN) for traffic forecasting, which learns simultaneously a group of convolutional kernels and their linear combination weights for each of the nodes in the graph. This yields a mechanism that is able to learn the features locally and exploit the structure information of traffic road-network globally. By introducing additional parameters, our KW-GCN can relax the restriction of weight sharing in classical CNN to better handle the traffic data of non-stationarity. Furthermore, it has been illustrated that the proposed linear weighting of kernels can be viewed as the low-rank decomposition of the well-known locally-connected networks, and thus it avoids over-fitting to some degree. We apply our approach to the real-world GPS data set of about 30,000 taxis in seven months in Beijing. Experiments on both taxi-flow forecasting and road-speed forecasting demonstrate that our method significantly outperforms the state-of-the-art ones. |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91646207] |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44366 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Zhang Qi |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang Qi,Jin Qizhao,Chang Jianlong,et al. Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting[C],2018. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
icpr_张奇.pdf(4041KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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