|本期目录/Table of Contents|

[1]张海静,姚博彬＊,武奇生.基于差分数据图和深度学习的短时交通流预测[J].工业仪表与自动化装置,2020,(02):3-7.
　ZHANG Haijing,YAO Bobin,WU Qisheng.Short-term traffic flow prediction based on the differential data graph and deep learning[J].Industrial Instrumentation & Automation,2020,(02):3-7.
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基于差分数据图和深度学习的短时交通流预测

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年02期
页码:
3-7
栏目:
出版日期:
2020-04-15

文章信息/Info

Title:
Short-term traffic flow prediction based on the differential data graph and deep learning
文章编号:
1000-0682(2020)02-0000-00
作者:
张海静; 姚博彬＊; 武奇生
长安大学 电子与控制工程学院，西安 710064
Author(s):
ZHANG Haijing;  YAO Bobin＊;  WU Qisheng
School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
关键词:
交通流量预测; 卷积神经网络; 支持向量回归; 数据差分; 交通数据图
Keywords:
traffic flow prediction;  convolutional neural network;  support vector regression;  data differential;  traffic data graph
分类号:
TP181
DOI:
-
文献标志码:
A
摘要:
交通流量预测(TFP)是提高交通出行效率，减少智能交通系统(ITS)交通拥堵的重要问题。为了从交通流特征提取的角度提高TFP精度，该文提出了一种新的方法DG-CNN-SVR。这种方法的基本思想依赖于2个方面：交通流数据趋势和不同工作日的细微差别。因此，引入差分运算来消除趋势分量，然后构建交通数据图以帮助解释复杂特征。通过卷积神经网络(CNN)进行特征学习，并使用支持向量回归(SVR)进行交通流量预测。通过测试数据集的实验结果表明，所提出的TFP方法比单独使用CNN和SVR的情况更有效。
Abstract:
Traffic flow prediction(TFP) is an very important issue for successfully promoting traffic efficiency and reducing traffic congestion in intelligent transportation system(ITS).In order to improve the TFP accuracy from the perspective of traffic flow features extraction,this paper propose a new method named DG-CNN-SVR.The basic thought behind relies on two aspects:the inherit trend in traffic flow data and the some subtle difference at different weekdays.Therefore,introduce differential operation to eliminate the trend component and then construct a traffic data graph to help interpret the complex features.Uses convolutional neural network(CNN) for features learning and support vector regression(SVR) for traffic flow predicting.The experimental results by the test data set show that the proposed TFP method performs more effective than the cases when CNN and SVR are used individually.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2019-07-05
基金项目：国家自然科学基金项目(61601058)；河南省交通运输厅科技计划项目（2019G-2-5）；中央高校基本科研业务费专项基金(300102329106)
作者简介：张海静(1995)，长安大学电子与控制工程学院硕士研究生，研究方向为大数据处理等。
更新日期/Last Update: 1900-01-01