预测交通流量[Appl. Sci.专栏第六篇发表论文]

已有 1088 次阅读 2023-1-4 22:22 |系统分类:论文交流

我在Applied Sciences(综合性、交叉性期刊,CiteScore=3.70IF=2.84)组织了一个Special Issue,大题目是“大数据分析进展”,比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎(但不限于)下面四类稿件:(1)数据分析中的基础理论分析,例如一个系统的可预测性(比如时间序列的可预测性)、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析;(2)数据分析的新方法,例如挖掘因果关系的新方法(这和Topic 1也是相关的)、多模态分析的新方法、隐私计算的新方法等等;(3)推出新的、高价值的数据集、数据分析平台、数据分析工具等等;(4)把大数据分析的方法用到自然科学和社会科学的各个分支(并获得洞见),我们特别喜欢用到那些原来定量化程度不高的学科。




Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting


The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone function, which are then input to the multi-graph neural network with the goal of extracting and aggregating features in order to realize the complex spatial dependence of each station’s passenger flow. Furthermore, based on periodic features of historical traffic flows, we categorize the flow data into three time patterns. Specifically, we propose two different self-attention mechanisms to fuse high-order spatiotemporal features of traffic flow. The final step is to integrate the two modules and obtain the output results using a gated convolution and a fully connected neural network. The experimental results show that the proposed model has better performance than eight other baseline models at 10 min, 15 min and 30 min time intervals.


下一篇:表面缺陷检测的牛逼方法[Appl. Sci.专栏第七篇发表论文]
收藏 IP: 182.150.53.*| 热度|

2 杨正瓴 彭真明

该博文允许注册用户评论 请点击登录 评论 (0 个评论)


Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2023-3-26 20:51

Powered by

Copyright © 2007- 中国科学报社