Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (9): 2531-2539.doi: 10.13229/j.cnki.jdxbgxb.20221455

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Traffic speed prediction of regional complex road networks integrating CapsNet with D-BiLSTM

Jie CAO1,2(),Guang SU1,Hong ZHANG1(),Peng-hui LI1   

  1. 1.School of Computer and Communication，Lanzhou University of Technoogy，Lanzhou 730050，China
    2.School of Information Engineening，Lanzhou City University，Lanzhou 730070，China
  • Received:2022-11-14 Online:2024-09-01 Published:2024-10-28
  • Contact: Hong ZHANG E-mail:caoj@lut.edu.cn;zhanghong@lut.edu.cn

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Abstract:

Due to the complex and dynamic spatio-temporal correlation of traffic patterns leads to the inadequacy of existing methods to learn traffic evolution in terms of structural depth and prediction scale. a Deep learning model combining CapsNet and deep bi-directional LSTM （D-BiLSTM） was proposed. This model was used to identify the spatial topology of road networks and extract spatial features using CapsNet,was fused with the D-BiLSTM network, taking into account both the forward and backward dependencies of traffic states, and capturing the bi-directional temporal correlations of different historical periods, to forecast traffic on large-scale complex road networks in the target region. Experiments conducted on real traffic road network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average， outperforming other methods， with high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

Key words: capsule network, deep bidirectional LSTM, complex road network, backward dependence, traffic speed forecast

CLC Number: 

  • TP183

Fig.1

Conversion form traffic network data to image"

Fig.2

CapsNet model structure diagram"

Fig.3

Attention model structure diagram"

Fig.4

LSTM architecture diagram"

Fig.5

BiLSTM architecture diagram"

Fig.6

Structure of CapsNet+D-BiLSTM model"

Table 1

Structural parameters of the CapsNet+D-BiLSTM"

层名参数输出大小
输入-164×148×10
卷积1

卷积核=3×3

通道=128

步长=2

87×73×1281 280
卷积2

卷积核=3×3

通道=128

步长=2

40×36×128147 584
注意力-
主胶囊层

卷积核=3×3

通道=128

步长=4

9×8×128147 584
胶囊维度=81 152×8
高级胶囊层

高级胶囊=30

胶囊维度=16

1 152×8

9 216

0
双向LSTM隐藏单元=200（None，200）7 453 600
舍弃层0.2（None，200）0
全连接层-（None，278）55 875
参数总数7 808 230

Table 2

Performance comparison of CapsNet+D-BiLSTM model with other models"

模型2 min10 min20 min
MAEMAPE/%MAEMAPE/%MAEMAPE/%
CapsNet4.517 222.174 94.849 523.430 45.792 026.242 6
BiLSTM5.703 230.863 15.795 230.885 66.318 834.375 4
CNN+LSTM4.783 024.692 45.057 125.002 05.473 326.003 8
CapsNet+LSTM4.313 521.636 34.764 425.038 14.906 425.806 5
CNN+BiLSTM4.597 521.565 44.971 624.721 05.015 125.051 5
CapsNet+D-BiLSTM4.154 620.663 54.748 722.616 54.890 423.465 9

Fig.7

MAE results for each model at different time steps"

Fig.8

MAPE results for each model at different time steps"

Fig.9

Comparison of training time for each model"

Fig.10

MAE box plot at different time lag stages"

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