[1]殷 齐,丁 飞,朱 跃,等.基于 CNN 与多尺度特征融合的城市交通流预测模型[J].计算机技术与发展,2022,32(10):175-181.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 029]
　YIN Qi,DING Fei,ZHU Yue,et al.An Urban Traffic Flow Prediction Model Based on CNN and Multi-scale Feature Fusion[J].,2022,32(10):175-181.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 029]
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基于 CNN 与多尺度特征融合的城市交通流预测模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
175-181
栏目:
新型计算应用系统
出版日期:
2022-10-10

文章信息/Info

Title:
An Urban Traffic Flow Prediction Model Based on CNN and Multi-scale Feature Fusion
文章编号:
1673-629X(2022)10-0175-07
作者:
殷 齐1; 2 ; 丁 飞1; 2 ; 朱 跃1; 2 ; 李静宜1 ; 沙宇晨1; 2
1. 南京邮电大学 物联网学院,江苏 南京 210003;
2. 南京邮电大学 江苏省宽带无线通信和物联网重点实验室,江苏 南京 210003
Author(s):
YIN Qi1; 2 ; DING Fei1; 2 ; ZHU Yue1; 2 ; LI Jing-yi1 ; SHA Yu-chen1; 2
1. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things,Nanjing University of Posts and Telecommunications, Nanjing 210003
关键词:
交通流; 卷积神经网络; 残差自校验网络; 多尺度特征; 门控循环单元
Keywords:
traffic stream; convolutional neural network; residual self checking network; multi-scale feature; gated recurrent unit
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 029
摘要:
随着出租车和网约车的日益普及,GPS 数据生成大量的时空视频流数据,对城市交通流预测提供坚实的数据价值。 传统城市流量预测方法存在精度低,目标区域受周围区域影响等问题。 卷积神经网络在交通流预测上表现出色, 但仍存在目标区域受全局信息的干扰、低层网络的特征表 征能力弱及高层下采样损失过多特征等问题。 该文提出一种基于卷积神经网络( Convolutional Neural Network,CNN) 与多尺度融合机制的交通流预测模型 MS-RSCNet( Multi-scale ResidualSelf Checking Network) 。 该模型采用了一种残差自校验网络 (Residual Self Checking Network, RSCNet) 结构,并引入融合多尺度特征的双向门控循环单元设计方案。 通过公开数据集对交通流预测性能进行测试验证,相较于 ST-ResNet、ARIMA、STAR 等模型,MS-RSCNet 模型具有更优的交通流预测性能。
Abstract:
With the increasing popularity of taxis and online car hailing,GPS data generates a large number of spatio - temporal video stream data, which provides solid data value for urban traffic flow prediction. The traditional urban flow prediction method has some problems,such as low accuracy and target area affected by surrounding area. Convolutional neural network performs well in traffic flow prediction,but there are still some problems, such as the interference of global information in the target area, the weak feature representation ability of low-level network and too much loss of high-level down sampling. We propose a traffic flow prediction modelMS-RSCNet ( multi-scale residual self checking network) based on convolutional neural network ( CNN) and multi-scale fusion mechanism. The model adopts a residual self checking network ( RSCNet) structure and introduces a design scheme of two-way gating cycleunit integrating multi-scale features. The traffic flow prediction performance is tested and verified through the public data set. Compared with St RESNET,ARIMA,STAR and other models,MS-RSCNet model has better traffic flow prediction performance.

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更新日期/Last Update: 2022-10-10