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

Previous Articles    

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

RICH HTML

 18   

PDF (PC)

157

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"

1 Yuan Y F, Hoogendoorm S, Kessels F, et al. Network-wide traffic state estimation using loop detector and floating car data[J]. Journal of Intelligent Transportation Systems, 2014, 18(1):41-50.
2 Liu H X, Ma Wen-teng. A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials[J]. Transportation Research Part C: Emerging Technologies, 2009, 17(1): 11-26.
3 Box G E P, Jenkins G M, Reinsel G C, et al. Time Series Analysis: Forecasting and Control[M]. Hoboken, NJ: John Wiley & Sons, 2016.
4 Smith B L, Billy M W, Oswald R K. Comparison of parametric and nonparametric models for traffic flow forecasting[J]. Transportation Research Part C: Emerging Technologies, 2002, 10(4): 303-321.
5 Williams M B, Hoel L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results[J]. Journal of Transportation Engineering, 2003, 129(6): 664-672.
6 Zhang L, Liu Q C, Yang W C, et al. An improved k-nearest neighbor model for short-term traffic flow prediction[J]. Procedia-Social and Behavioral Sciences, 2013, 96: 653-662.
7 Chandra S R, Al-Deek H. Predictions of freeway traffic speeds and volumes using vector autoregressive models[J]. Journal of Intelligent Transportation Systems, 2009, 13(2): 53-72.
8 LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
9 Treiber M, Helbing D. Reconstructing the spatio-temporal traffic dynamics from stationary detector data[J/OL].[2020-09-25].
10 李月龙, 唐德华, 姜桂圆, 等. 基于维度加权的残差LSTM短期交通流量预测[J]. 计算机工程, 2019, 45(6): 1-5.
Li Yue-long, Tang De-hua, Jiang Gui-yuan, et al. Short term traffic flow forecasting based on dimension weighted residual LSTM[J]. Computer Engineering, 2019, 45(6):1-5.
11 Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3: 1137-1155.
12 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
13 Zhang Jun-bo, Zheng Yu, Qi De-kang. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]∥Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, 2017: 1655-1661.
14 Yu R, Li Y, Shahabi C, et al. Deep learning: a generic approach for extreme condition traffic forecasting[J/OL].[2020-09-25].
15 Yao Hua-xiu, Wu Fei, Ke Jin-tao, et al. Deep multi-view spatial-temporal network for taxi demand prediction[J/OL]. [2020-09-26].
16 Yao Hua-xiu, Tang Xian-feng, Wei Hua, et al. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction[C]∥AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, 2019: 5568-5675.
17 郭晟楠, 林友芳, 金文蔚, 等. 基于时空循环卷积网络的城市区域人口流量预测[J]. 计算机科学, 2019, 46(): 385-391.
Guo Sheng-nan, Lin You-fang, Jin Wen-wei, et al. Citywide crowd flows prediction based on spatio-temporal recurrent convolutional networks[J]. Computer Science, 2019, 46(Sup.1): 385-391.
[1] Tian-jun FENG,Xue-lu SUN,Jia-sheng HUANG,Xiu-juan TIAN,Xian-min SONG. Two-phase signal intersection delay based on three crossing modes [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 550-556.
[2] Long ZHANG,Tian-peng XU,Chao-bing WANG,Jian-yu YI,Can-zhuang ZHEN. Gearbox fault diagnosis baed on convolutional gated recurrent network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 368-376.
[3] Xiao⁃lei CHEN,Yong⁃feng SUN,Ce LI,Dong⁃mei LIN. Stable anti⁃noise fault diagnosis of rolling bearing based on CNN⁃BiLSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 296-309.
[4] Yan-feng JIA,Da-yi QU,Lu LIN,Rong-han YAO,Xiao-long MA. Coordinated speed control of connected mixed traffic flow based on trajectory [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2051-2060.
[5] Ya-hui ZHAO,Fei-yang YANG,Zhen-guo ZHANG,Rong-yi CUI. Korean text structure discovery based on reinforcement learning and attention mechanism [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1387-1395.
[6] De-xing WANG,Ruo-you WU,Hong-chun YUAN,Peng GONG,Yue WANG. Underwater image restoration based on multi-scale attention fusion and convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1396-1404.
[7] Yuan-ning LIU,Di WU,Xiao-dong ZHU,Qi-xian ZHANG,Shuang-shuang LI,Shu-jun GUO,Chao WANG. User interface components detection algorithm based on improved YOLOv3 [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1026-1033.
[8] Hai-ying ZHAO,Wei ZHOU,Xiao-gang HOU,Xiao-li ZHANG. Double-layer annotation of traditional costume images based on multi-task learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 293-302.
[9] Hong-wei ZHAO,Xiao-han LIU,Yuan ZHANG,Li-li FAN,Man-li LONG,Xue-bai ZANG. Clothing classification algorithm based on landmark attention and channel attention [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1765-1770.
[10] Chang-fu ZONG,Long WEN,Lei HE. Object detection based on Euclidean clustering algorithm with 3D laser scanner [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 107-113.
[11] Hong-wei ZHAO,Peng WANG,Li-li FAN,Huang-shui HU,Ping-ping LIU. Similarity retention instance retrieval method [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2045-2050.
[12] Liang JIANG,Yi HE. Risky driving behavior and influencing factors analysis for electric two⁃wheeler [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1107-1113.
[13] Dan⁃tong OUYANG,Jun XIAO,Yu⁃xin YE. Distant supervision for relation extraction with weakconstraints of entity pairs [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 912-919.
[14] Xiao⁃qin LUO,Dian⁃hai WANG,Sheng JIN. Traffic signal actuated control at isolated intersections for heterogeneous traffic [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 695-704.
[15] Zhi⁃hui LI,Tao ZHONG,Yong⁃hua ZHAO,Yong⁃li HU,Hai⁃tao LI,Jing⁃wei ZHAO. Pedestrian tracking algorithm for autonomous driving [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 680-687.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd