Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (3): 557-563.doi: 10.13229/j.cnki.jdxbgxb20200803

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Route travel time prediction on deep learning model through spatiotemporal features

Xian-tong LI(),Wei QUAN,Hua WANG(),Peng-cheng SUN,Peng-jin AN,Yong-xing MAN   

  1. School of Transportation Science and Engineering，Harbin Institute of Technology，Harbin 150001，China
  • Received:2020-08-03 Online:2022-03-01 Published:2022-03-08
  • Contact: Hua WANG E-mail:lxt@hit.edu.cn;wanghua@hit.edu.cn

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Abstract:

To process the important problem of path travel time prediction， this paper proposes a deep learning network model on spatiotemporal feature. It is combined with long-short-term memory network （LSTM） and convolutional neural network. At the same time， it considers the spatial dependence of road sections， timing dependence， and time drift in coarse granularity to predict the path travel time of the next time slot. In the experiments of this paper， the dataset of Harbin taxi trajectory is taken as the test dataset. The comparison results show that spatiotemporal characteristics based deep learning network model outperforms that based on the machine learning model with multiple evaluation indicators. On the indicators of MAE and R2， the performance of the algorithm in this paper is better than other algorithms by 18.6% and 22.46% respectively. At last， the prediction accuracy of the model proposed in this paper is over 90%， and the efficiency is at the leading level among similar algorithms.

Key words: engineering of communication and transportation system, spatiotemporal character, convolutional neural network（CNN）, long-short-term memory network（LSTM）, attention mechanism

CLC Number: 

  • U491

Fig.1

Structure of the propoesd model"

Table 1

Comparison of prediction and evaluation results of different models"

模 型MAER2

差分自回归移动平均

单向LSTM

双向LSTM

DLSF

28.22

24.34

24.48

22.97

0.641

0.711

0.719

0.785

Table 2

Efficiency of different models"

模型MAER2

移除CNN，忽略空间依赖关系

CNN+长期LSTM（无短期LSTM）

CNN+短期LSTM（无长期LSTM）

时空特征深度学习网络

26.98

24.65

24.05

22.97

0.733

0.737

0.744

0.785

Fig.2

Prediction results of route travel time"

Fig.3

Comparison on precisions betweendifferent algorithms"

Fig.4

Route travel time in different routes"

Fig.5

Relationship between length of short-LSTM and MAE"

Fig.6

Relationship between length oflong-LSTM and MAE"

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