Improving Short-Term Traffic Flow Prediction using Grey Relational Analysis for Data Filtering and Stacked LSTM Modeling
DOI:
https://doi.org/10.15837/ijccc.2024.1.6149Keywords:
Traffic flow prediction, GRA-SLSTM, Grey Relation Analysis, Long Short-Term Memory Network, Deep LearningAbstract
Traffic flow prediction is one of the critical measures to alleviate traffic congestion. Currently, traffic flow prediction research has made some achievements, but there are still some deficiencies. In order to solve the problems of low prediction accuracy, poor real-time performance, and high data dimensions. This paper proposes a new traffic flow prediction method that employs Grey Relation Analysis (GRA) to detect the correlation between detection points, remove insignificant or uncorrelated traffic flow data points, and hence reduce the data dimensionality of the prediction model. Multiple Long Short-Term Memory (LSTM) models are then stacked to establish the traffic flow prediction model, considering that traffic flow is affected by multi-dimensional spatiotemporal factors, incorporating vehicle speed, occupancy, and traffic volume as inputs. We conducted experiments on real datasets, and the results showed that our GRA-SLSTM model improved prediction accuracy by 3.6% compared to other models, while reducing model prediction time by 27.33%. The proposed model’s generalization ability is validated by predicting other detection points, which provides significant references for traffic flow prediction research and practical applications.References
Yuan" Z.; Yuan, X.; Yang, Y.; et al. (2023). Greenhouse gas emission analysis and measurement for urban rail transit: a review of research progress and prospects, Digital Transportation and Safety, 1(1), 37-52, 2023.
https://doi.org/10.48130/DTS-2023-0004
Yang, Y.; Yin, Y. X.; Wang, Y. P.; et al. (2023). Modeling of freeway real-time traffic crash risk based on dynamic traffic flow considering temporal effect difference, Journal of Transportation Engineering Part A Systems, 149(7), 04023063, 2023.
https://doi.org/10.1061/JTEPBS.TEENG-7717
Ahmed, M. S.; Cook, A. R. (2023). Analysis of Freeway Traffic Time-Series Data by Using Box- Jenkins Techniques, 1979.
Williams, B. M.; Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results, Journal of Transportation Engineering, 129(06), 664-672, 2003.
https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
Kumar, S. V. (2017). Traffic flow prediction using Kalman filtering technique, Procedia Engineering, 187, 582-587, 2017.
https://doi.org/10.1016/j.proeng.2017.04.417
Zhang, S. Y.; Yang, Y. H.; Chen, C.; Yang, Y. C.; Li, F. L. (2021). Spatio-temporal prediction of ship traffic flow in waterway regulation area based on regression Kalman Filter combination model, Journal of Dalian Maritime University, 47(1), 37-44, 2021.
Yang, Y.; He, K.; Wang, Y. P.; Yuan, Z. Z.; Yin, Y. H.; Guo, M. Z. (2022). Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods, Physica A: Statistical Mechanics and its Applications, 595(2022), 127083, 2022.
https://doi.org/10.1016/j.physa.2022.127083
Davis, G. A.; Nihan, N. L. (1991). Nonparametric regression and short-term freeway traffic forecasting, Journal of Transportation Engineering, 117(2), 178-188, 1991.
https://doi.org/10.1061/(ASCE)0733-947X(1991)117:2(178)
Liang, Y. P.; Mao, Z. Y.; Zou, W. B.; Xu, R. (2018). Short-term traffic flow prediction based on similar data aggregation and KNN with varying K-value, Journal of Geo-information Science, 20(10), 1403-1411, 2018.
Lin, H.; Li, L. X.; Wang, H. (2020). SPGAPSO-SVM: a prediction algorithm of urban bus passenger flow, Journal of Chinese Computer Systems, 41(11), 2458-2464, 2020.
Fu, C. H.; Yang, S. M.; Zhang, Y. (2019). Promoted short-term traffic flow prediction model based on deep learning and support vector regression, Journal of Transportation Systems Engineering and Information Technology, 19(4), 130-134, 2019.
Hu, W. B.; Yan, L. P.; Lin, K. Z.; et al. (2016). A Short-term traffic flow forecasting method based on the hybrid PSO-SVR, Neural Processing Letters, 43(1), 155-172, 2016.
https://doi.org/10.1007/s11063-015-9409-6
Chen, X. Q.; Zhou, L. X.; Cao, Z. (2020). Short-term network-wide traffic prediction based on graph convolutional network, Journal of Transportation Systems Engineering and Information Technology, 20(4), 49-55, 2020.
Li, G.; Peng, L.; Li, X.; et al. (2019). Short-term traffic forecast of urban bus stations based on long short-term memory, Journal of Highway and Transportation Research and Development (English Edition), 13(2), 65-72, 2019.
https://doi.org/10.1061/JHTRCQ.0000680
Luo, X. L.; Li, D. Y.; Yang, H. (2018). Short-term traffic flow prediction based on KNN-LSTM, Journal of Beijing University of Technology, 44(12), 1521-1527, 2018.
Yan, Z.; Yu, C. C.; Han, L.; Su, W. J.; Liu, P. (2019). Short-term traffic flow forecasting method based on CNN+LSTM, Computer Engineering and Design, 40(9), 2620-2624+2659, 2019.
Luo, W. H.; Dong, B. T.; Wang, Z. S. (2017). Short-term traffic flow prediction based on cnnsvr hybrid deep learning model, Journal of Transportation Systems Engineering and Information Technology, 17(5), 68-74, 2017.
Li, L.; Zhang, Q. M.; Zhao, J. H.; Nie, Y. W. (2021). Short-term traffic flow prediction method of different periods based on improved CNN-LSTM, Journal of Applied Sciences, 39(2), 185-198, 2021.
Yu, B.; Yin, H. T.; Zhu, Z. X. (2018). Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), 3634-3640, 2018.
https://doi.org/10.24963/ijcai.2018/505
He, W. W.; Pei, B. Y.; Li, Y. T.; Liu, X. Y.; Xu, S. B. (2023). Traffic flow forecasting based on bi-directional adaptive gating graph convolutional networks, Journal of Transportation Systems Engineering and Information Technology, 23(1), 187-197, 2023.
Cui, W. Y.; Gu, Y. L.; Zhao, S. L.; Rui, X. P. (2023). A method of predicting short-term traffic flows based on a DGC-GRU model, Journal of Transport Information and Safety, 41(2), 121-128, 2023.
Gao, H.; Jia, H.; Yang, L.; Li, R. (2022). An improved CEEMDAN-FE-TCN model for highway traffic flow prediction, Journal of Advanced Transportation, 1-20, 2022.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Zhizhu Wu, Mingxia Huang, Zhibo Xing, Tao Yang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
