Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 3275-3282.DOI: 10.11772/j.issn.1001-9081.2021091613
• Frontier and comprehensive applications • Previous Articles
Jingyi QU1, Liu YANG1, Xuyang CHEN1, Qian WANG2
Received:
2021-09-13
Revised:
2022-01-05
Accepted:
2022-01-11
Online:
2022-04-15
Published:
2022-10-10
Contact:
Jingyi QU
About author:
QU Jingyi, born in 1978, Ph. D. , professor. Her research interests include big data of air transportation, deep learning.Supported by:
通讯作者:
屈景怡
作者简介:
第一联系人:屈景怡(1978—),女,天津人,教授,博士,主要研究方向:航空运输大数据、深度学习; qujingyicauc@163.com基金资助:
CLC Number:
Jingyi QU, Liu YANG, Xuyang CHEN, Qian WANG. Flight delay prediction model based on Conv-LSTM with spatiotemporal sequence[J]. Journal of Computer Applications, 2022, 42(10): 3275-3282.
屈景怡, 杨柳, 陈旭阳, 王茜. 基于时空序列的Conv-LSTM航班延误预测模型[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3275-3282.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021091613
网络层 | 输出维度 | 参数量 |
---|---|---|
Reshape | (15,8,9,1) | 0 |
Conv-LSTM-1 | (15,8,9,8) | 1 472 |
Batch-Normalization-1 | (15,8,9,8) | 32 |
Conv-LSTM-2 | (15,8,9,16) | 9 280 |
Batch-Normalization-2 | (15,8,9,16) | 64 |
Conv-LSTM-3 | (15,8,9,8) | 5 792 |
Batch-Normalization-3 | (15,8,9,8) | 32 |
Flatten | (8 640) | 0 |
Dense | (5) | 43 205 |
Tab. 1 Composition of network structure and number of parameters
网络层 | 输出维度 | 参数量 |
---|---|---|
Reshape | (15,8,9,1) | 0 |
Conv-LSTM-1 | (15,8,9,8) | 1 472 |
Batch-Normalization-1 | (15,8,9,8) | 32 |
Conv-LSTM-2 | (15,8,9,16) | 9 280 |
Batch-Normalization-2 | (15,8,9,16) | 64 |
Conv-LSTM-3 | (15,8,9,8) | 5 792 |
Batch-Normalization-3 | (15,8,9,8) | 32 |
Flatten | (8 640) | 0 |
Dense | (5) | 43 205 |
参数 | 值 | 参数 | 值 |
---|---|---|---|
损失函数 | 交叉熵 | 卷积核大小 | 3×3 |
学习率 | 0.000 01 | 序列长度 | 15 |
优化器 | Adam( | 批处理大小 | 256 |
批归一化 | 迭代次数 | 100 | |
每层滤波器的个数 | 4/8/4 |
Tab. 2 Parameters of experimental environment
参数 | 值 | 参数 | 值 |
---|---|---|---|
损失函数 | 交叉熵 | 卷积核大小 | 3×3 |
学习率 | 0.000 01 | 序列长度 | 15 |
优化器 | Adam( | 批处理大小 | 256 |
批归一化 | 迭代次数 | 100 | |
每层滤波器的个数 | 4/8/4 |
数据集 | 数据量 | 准确率/% |
---|---|---|
航班数据 | 59 724 | 83.58 |
航班融合1 min气象 | 58 586 | 85.26 |
航班融合10 min气象 | 591 858 | 99.22 |
Tab. 3 Influence of meteorological data on accuracy
数据集 | 数据量 | 准确率/% |
---|---|---|
航班数据 | 59 724 | 83.58 |
航班融合1 min气象 | 58 586 | 85.26 |
航班融合10 min气象 | 591 858 | 99.22 |
序列长度 | 每轮训练时间/s | 准确率/% |
---|---|---|
1 | 71 | 98.94 |
3 | 126 | 98.95 |
5 | 180 | 98.89 |
7 | 239 | 98.99 |
9 | 297 | 99.02 |
11 | 351 | 99.06 |
13 | 422 | 99.15 |
15 | 465 | 99.22 |
17 | 748 | 99.20 |
Tab. 4 Influence of sequence length on accuracy and training time
序列长度 | 每轮训练时间/s | 准确率/% |
---|---|---|
1 | 71 | 98.94 |
3 | 126 | 98.95 |
5 | 180 | 98.89 |
7 | 239 | 98.99 |
9 | 297 | 99.02 |
11 | 351 | 99.06 |
13 | 422 | 99.15 |
15 | 465 | 99.22 |
17 | 748 | 99.20 |
数据集 | Conv-2D | Bi-LSTM | Conv-LSTM | ||||
---|---|---|---|---|---|---|---|
LSTM-1 | LSTM-2 | LSTM-3 | Conv-LSTM-1 | Conv-LSTM-2 | Conv-LSTM-3 | ||
平均值 | 95.85 | 96.63 | 97.40 | 97.56 | 96.99 | 97.84 | 98.21 |
ZBAA | 96.21 | 97.01 | 97.57 | 97.85 | 97.23 | 98.07 | 98.36 |
ZBAD | 97.06 | 96.52 | 97.25 | 97.48 | 97.37 | 97.88 | 98.02 |
ZBTJ | 92.70 | 95.26 | 96.15 | 96.20 | 95.02 | 96.45 | 97.25 |
ZBSJ | 97.44 | 97.74 | 98.64 | 98.72 | 98.35 | 98.96 | 99.22 |
Tab. 5 Accuracy comparison of different models on different datasets
数据集 | Conv-2D | Bi-LSTM | Conv-LSTM | ||||
---|---|---|---|---|---|---|---|
LSTM-1 | LSTM-2 | LSTM-3 | Conv-LSTM-1 | Conv-LSTM-2 | Conv-LSTM-3 | ||
平均值 | 95.85 | 96.63 | 97.40 | 97.56 | 96.99 | 97.84 | 98.21 |
ZBAA | 96.21 | 97.01 | 97.57 | 97.85 | 97.23 | 98.07 | 98.36 |
ZBAD | 97.06 | 96.52 | 97.25 | 97.48 | 97.37 | 97.88 | 98.02 |
ZBTJ | 92.70 | 95.26 | 96.15 | 96.20 | 95.02 | 96.45 | 97.25 |
ZBSJ | 97.44 | 97.74 | 98.64 | 98.72 | 98.35 | 98.96 | 99.22 |
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