Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 3283-3291.DOI: 10.11772/j.issn.1001-9081.2022010002
• Frontier and comprehensive applications • Previous Articles Next Articles
Haiwen XU1, Jiacai SHI2, Teng WANG2
Received:
2022-01-06
Revised:
2022-04-25
Accepted:
2022-04-27
Online:
2022-10-14
Published:
2022-10-10
Contact:
Haiwen XU
About author:
XU Haiwen, born in 1978, Ph. D. , professor. His research interests include optimization theory and algorithms, transportation planning and management.Supported by:
徐海文1, 史家财2, 汪腾2
通讯作者:
徐海文
作者简介:
第一联系人:徐海文(1978—),男,山东菏泽人,教授,博士,主要研究方向:优化理论与算法、交通运输规划与管理; xuhaiwen_dream@163.com基金资助:
CLC Number:
Haiwen XU, Jiacai SHI, Teng WANG. Departure flight delay prediction model based on deep fully connected neural network[J]. Journal of Computer Applications, 2022, 42(10): 3283-3291.
徐海文, 史家财, 汪腾. 基于深度全连接神经网络的离港航班延误预测模型[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3283-3291.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022010002
研究对象 | 数据集 | 样本数比例/% |
---|---|---|
离港航班相关数据470 403条 | 训练集 | 60 |
验证集 | 20 | |
测试集 | 20 |
Tab. 1 Experimental data information of Chengdu Shuangliu Airport departure flights
研究对象 | 数据集 | 样本数比例/% |
---|---|---|
离港航班相关数据470 403条 | 训练集 | 60 |
验证集 | 20 | |
测试集 | 20 |
数据集合A | 数据项 | 维度 | |||
---|---|---|---|---|---|
A1 | 有 | 有 | 无 | 无 | 470 403×32 |
A2 | 有 | 有 | 有 | 无 | 470 403×36 |
A3 | 有 | 有 | 有 | 有 | 470 403×37 |
Tab. 2 Datasets after merging data items
数据集合A | 数据项 | 维度 | |||
---|---|---|---|---|---|
A1 | 有 | 有 | 无 | 无 | 470 403×32 |
A2 | 有 | 有 | 有 | 无 | 470 403×36 |
A3 | 有 | 有 | 有 | 有 | 470 403×37 |
激活函数 | 损失值 | 精确度 |
---|---|---|
tanh | 0.218 6 | 0.830 0 |
ReLU | 0.278 4 | 0.817 4 |
ELU | 0.216 2 | 0.830 2 |
Tab. 3 Experimental prediction results of different activation parameters
激活函数 | 损失值 | 精确度 |
---|---|---|
tanh | 0.218 6 | 0.830 0 |
ReLU | 0.278 4 | 0.817 4 |
ELU | 0.216 2 | 0.830 2 |
激活函数 | 输入数据 | 损失值 | 精确度 |
---|---|---|---|
tanh | A1 | 0.218 6 | 0.830 0 |
A2 | 0.219 2 | 0.829 7 | |
A3 | 0.182 7 | 0.858 8 | |
ELU | A1 | 0.216 2 | 0.830 2 |
A2 | 0.218 2 | 0.830 1 | |
A3 | 0.184 3 | 0.861 3 |
Tab. 4 Experimental prediction results of different input data
激活函数 | 输入数据 | 损失值 | 精确度 |
---|---|---|---|
tanh | A1 | 0.218 6 | 0.830 0 |
A2 | 0.219 2 | 0.829 7 | |
A3 | 0.182 7 | 0.858 8 | |
ELU | A1 | 0.216 2 | 0.830 2 |
A2 | 0.218 2 | 0.830 1 | |
A3 | 0.184 3 | 0.861 3 |
TH/min | 损失值 | 精确度 |
---|---|---|
15 | 0.184 3 | 0.861 3 |
30 | 0.183 5 | 0.861 8 |
60 | 0.181 6 | 0.862 1 |
Tab. 5 Experimental prediction results of different thresholds
TH/min | 损失值 | 精确度 |
---|---|---|
15 | 0.184 3 | 0.861 3 |
30 | 0.183 5 | 0.861 8 |
60 | 0.181 6 | 0.862 1 |
隐含层层数 | 损失值 | 精确度 |
---|---|---|
5 | 0.181 6 | 0.862 1 |
10 | 0.187 8 | 0.858 5 |
15 | 0.188 3 | 0.858 3 |
Tab. 6 Experimental prediction results of DFCNN with different hidden layers
隐含层层数 | 损失值 | 精确度 |
---|---|---|
5 | 0.181 6 | 0.862 1 |
10 | 0.187 8 | 0.858 5 |
15 | 0.188 3 | 0.858 3 |
隐含层层数 | Dropout层参数p | 损失值 | 精确度 |
---|---|---|---|
5 | 0.3 | 0.109 3 | 0.923 9 |
0.5 | 0.181 6 | 0.862 1 | |
0.7 | 0.213 5 | 0.847 9 | |
10 | 0.3 | 0.114 8 | 0.919 4 |
0.5 | 0.187 8 | 0.858 5 | |
0.7 | 0.226 2 | 0.843 6 | |
15 | 0.3 | 0.121 2 | 0.914 1 |
0.5 | 0.188 3 | 0.858 3 | |
0.7 | 0.224 8 | 0.842 1 |
Tab. 7 Experimental prediction results of different Dropout parameters under different layers
隐含层层数 | Dropout层参数p | 损失值 | 精确度 |
---|---|---|---|
5 | 0.3 | 0.109 3 | 0.923 9 |
0.5 | 0.181 6 | 0.862 1 | |
0.7 | 0.213 5 | 0.847 9 | |
10 | 0.3 | 0.114 8 | 0.919 4 |
0.5 | 0.187 8 | 0.858 5 | |
0.7 | 0.226 2 | 0.843 6 | |
15 | 0.3 | 0.121 2 | 0.914 1 |
0.5 | 0.188 3 | 0.858 3 | |
0.7 | 0.224 8 | 0.842 1 |
模型 | 输入数据集 | |
---|---|---|
C4.5决策树 | 航班数据(国内) | 82.00 |
贝叶斯网络 | 航班数据(国内) | 88.00 |
随机森林 | 航班数据、气象数据(国内) | 60.00 |
RNN | 航班数据、气象数据(国外) | 87.42 |
5LSTM | 航班数据、气象数据(国外) | 88.63 |
5ANN | 航班数据、气象数据(国外) | 88.64 |
改进5层DFCNN | A3(国内) | 92.39 |
Tab. 8 Comparison of prediction accuracy of different flight delay prediction models
模型 | 输入数据集 | |
---|---|---|
C4.5决策树 | 航班数据(国内) | 82.00 |
贝叶斯网络 | 航班数据(国内) | 88.00 |
随机森林 | 航班数据、气象数据(国内) | 60.00 |
RNN | 航班数据、气象数据(国外) | 87.42 |
5LSTM | 航班数据、气象数据(国外) | 88.63 |
5ANN | 航班数据、气象数据(国外) | 88.64 |
改进5层DFCNN | A3(国内) | 92.39 |
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