Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 258-264.DOI: 10.11772/j.issn.1001-9081.2021010080
• Frontier and comprehensive applications • Previous Articles Next Articles
Yinxin BAO1, Yang CAO1,2, Quan SHI1,2()
Received:
2021-01-15
Revised:
2021-04-20
Accepted:
2021-04-29
Online:
2021-05-12
Published:
2022-01-10
Contact:
Quan SHI
About author:
BAO Yinxin, born in 1996, Ph. D. candidate. His research interests include intelligent information processing.Supported by:
通讯作者:
施佺
作者简介:
包银鑫(1996—),男,江苏淮安人,博士研究生,主要研究方向:智能信息处理基金资助:
CLC Number:
Yinxin BAO, Yang CAO, Quan SHI. Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction[J]. Journal of Computer Applications, 2022, 42(1): 258-264.
包银鑫, 曹阳, 施佺. 基于改进时空残差卷积神经网络的城市路网短时交通流预测[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 258-264.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021010080
数据集 | 成都市出租车数据 |
---|---|
地点 | 中国四川省成都市 |
时间 | 2014年8月3日到2014年8月30日 |
栅格面积 | 648 km2 |
区域经度(最小值) | 103.945 689 |
区域经度(最大值) | 104.204 976 |
区域纬度(最小值) | 30.786 707 |
区域纬度(最大值) | 30.585 958 |
时间间隔 | 5 min |
栅格数据尺寸 | 24×24 |
栅格数据数量 | 6 048 |
Tab. 1 Dataset parameters
数据集 | 成都市出租车数据 |
---|---|
地点 | 中国四川省成都市 |
时间 | 2014年8月3日到2014年8月30日 |
栅格面积 | 648 km2 |
区域经度(最小值) | 103.945 689 |
区域经度(最大值) | 104.204 976 |
区域纬度(最小值) | 30.786 707 |
区域纬度(最大值) | 30.585 958 |
时间间隔 | 5 min |
栅格数据尺寸 | 24×24 |
栅格数据数量 | 6 048 |
参数 | 取值 |
---|---|
输入尺寸 | [BATCH_SIZE,1,24,24] |
残差单元数量 | 3 |
卷积核尺寸 | 3×3 |
卷积核步长 | 1 |
卷积补零圈数 | 1 |
LSTM输入层维度 | 1×576 |
LSTM隐藏层层数 | 12 |
LSTM输出层维度 | 1×576 |
激活函数 | 残差单元:ReLU,其余为Sigmoid |
Tab. 2 Structural parameters of improved spatio-temporal residual convolutional neural network model
参数 | 取值 |
---|---|
输入尺寸 | [BATCH_SIZE,1,24,24] |
残差单元数量 | 3 |
卷积核尺寸 | 3×3 |
卷积核步长 | 1 |
卷积补零圈数 | 1 |
LSTM输入层维度 | 1×576 |
LSTM隐藏层层数 | 12 |
LSTM输出层维度 | 1×576 |
激活函数 | 残差单元:ReLU,其余为Sigmoid |
模型类别 | 成都市出租车数据(测试集) | |
---|---|---|
RMSE(平均) | MAE(平均) | |
LSTM(路网) | 9.282 | 5.444 |
CNN(路网) | 7.972 | 3.588 |
ST-ResNet(路网) | 7.139 | 4.188 |
改进ST-ResNet(路网) | 6.909 | 3.110 |
Tab. 3 Model performance comparison results
模型类别 | 成都市出租车数据(测试集) | |
---|---|---|
RMSE(平均) | MAE(平均) | |
LSTM(路网) | 9.282 | 5.444 |
CNN(路网) | 7.972 | 3.588 |
ST-ResNet(路网) | 7.139 | 4.188 |
改进ST-ResNet(路网) | 6.909 | 3.110 |
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