Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 6
Abstract
Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters.
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Data Availability Statement
Some data that supports the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Grant 71861017), in partby the General Project of Basic Research Program of Yunnan Province (Grant 202001AT070030), and in part by the Innovation-Driven Project of Central South University (Grant 2020CX013).
References
Bogaerts, T., A. D. Masegosa, J. S. Angarita-Zapata, E. Onieva, and P. Hellinckx. 2020. “A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data.” Transp. Res. Part C: Emerging Technol. 112 (Mar): 62–77. https://doi.org/10.1016/j.trc.2020.01.010.
Cai, L., Q. Chen, W. Cai, X. Xu, T. Zhou, and J. Qin. 2019. “SVRGSA: A hybrid learning based model for short-term traffic flow forecasting.” IET Intel. Transp. Syst. 13 (9): 1348–1355. https://doi.org/10.1049/iet-its.2018.5315.
Cheng, S., F. Lu, P. Peng, and S. Wu. 2018. “Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity.” Comput. Environ. Urban Syst. 71 (Sep): 186–198. https://doi.org/10.1016/j.compenvurbsys.2018.05.009.
Dai, G., C. Ma, and X. Xu. 2019. “Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU.” IEEE Access 7: 143025–143035. https://doi.org/10.1109/ACCESS.2019.2941280.
Ding, Q. Y., X. F. Wang, X. Y. Zhang, and Z. Q. Sun. 2011. “Forecasting traffic volume with space-time ARIMA model.” In Advanced materials research, 979–983. Freienbach, Switzerland: Trans Tech Publication.
Do, L. N. N., N. Taherifar, and H. L. Vu. 2019. “Survey of neural network-based models for short-term traffic state prediction.” Wiley Interdiscip. Rev.: Data Min. Knowl. Discovery 9 (1): e1285. https://doi.org/10.1002/widm.1285.
Duan, Z., Y. Yang, K. Zhang, Y. Ni, and S. Bajgain. 2018. “Improved deep hybrid networks for urban traffic flow prediction using trajectory data.” IEEE Access 6: 31820–31827. https://doi.org/10.1109/ACCESS.2018.2845863.
Fang, W., W. Zhuo, J. Yan, Y. Song, D. Jiang, and T. Zhou. 2022. “Attention meets long short-term memory: A deep learning network for traffic flow forecasting.” Physica A 587 (Feb): 0378–4371. https://doi.org/10.1016/j.physa.2021.126485.
Feng, X., X. Ling, H. Zheng, Z. Chen, and Y. Xu. 2019. “Adaptive multi-kernel SVM with spatial–temporal correlation for short-term traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 20 (6): 2001–2013. https://doi.org/10.1109/TITS.2018.2854913.
Gers, F. A., N. N. Schraudolph, and J. Schmidhuber. 2003. “Learning precise timing with LSTM recurrent networks.” J. Mach. Learn. Res. 3 (1): 115–143.
Guo, S., Y. Lin, S. Li, Z. Chen, and H. Wan. 2019. “Deep spatial–temporal 3D convolutional neural networks for traffic data forecasting.” IEEE Trans. Intell. Transp. Syst. 20 (10): 3913–3926. https://doi.org/10.1109/TITS.2019.2906365.
Habtemichael, F. G., and M. Cetin. 2016. “Short-term traffic flow rate forecasting based on identifying similar traffic patterns.” Transp. Res. Part C Emerging Technol. 66 (May): 61–78. https://doi.org/10.1016/j.trc.2015.08.017.
Kong, F., J. Li, B. Jiang, and H. Song. 2019. “Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network.” Future Gener. Comput. Syst. 93 (Apr): 460–472. https://doi.org/10.1016/j.future.2018.10.052.
Kumar, S. V., and L. Vanajakshi. 2015. “Short-term traffic flow prediction using seasonal ARIMA model with limited input data.” Eur. Transp. Res. Rev. 7 (3): 1–9. https://doi.org/10.1007/s12544-015-0170-8.
Lin, L., Y. Li, and A. Sadek. 2013. “A k nearest neighbor based local linear wavelet neural network model for on-line short-term traffic volume prediction.” Procedia–Soc. Behav. Sci. 96 (Nov): 2066–2077. https://doi.org/10.1016/j.sbspro.2013.08.233.
Lv, Y., Y. Duan, W. Kang, Z. Li, and F.-Y. Wang. 2014. “Traffic flow prediction with big data: A deep learning approach.” IEEE Trans. Intell. Transp. Syst. 16 (2): 865–873. https://doi.org/10.1109/TITS.2014.2345663.
Mou, L., P. Zhao, H. Xie, and Y. Chen. 2019. “T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction.” IEEE Access 7: 98053–98060. https://doi.org/10.1109/ACCESS.2019.2929692.
Sun, Z., and G. Fox. 2014. “Traffic flow forecasting based on combination of multidimensional scaling and SVM.” Int. J. Intell. Transp. Syst. Res. 12 (1): 20–25. https://doi.org/10.1007/s13177-013-0065-9.
Tedjopurnomo, D. A., Z. Bao, B. Zheng, F. Choudhury, and A. K. Qin. 2020. “A survey on modern deep neural network for traffic prediction: Trends, methods and challenges.” IEEE Trans. Knowl. Data Eng. 34 (4): 1544–1561. https://doi.org/10.1109/TKDE.2020.3001195.
Tian, Y., K. Zhang, J. Li, X. Lin, and B. Yang. 2018. “LSTM-based traffic flow prediction with missing data.” Neurocomputing 318 (Nov): 297–305. https://doi.org/10.1016/j.neucom.2018.08.067.
Tong, M., and H. Xue. 2008. “Highway traffic volume forecasting based on seasonal ARIMA model.” J. Highway Transp. Res. Dev. (English Ed.). 25 (1): 124–128. https://doi.org/10.1061/JHTRCQ.0000255.
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2014. “Short-term traffic forecasting: Where we are and where we’re going.” Transp. Res. Part C: Emerging Technol. 43 (Part 1): 3–19. https://doi.org/10.1016/j.trc.2014.01.005.
Wang, H., L. Liu, S. Dong, Z. Qian, and H. Wei. 2016. “A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework.” Transportmetrica B: Transp. Dyn. 4 (3): 159–186. https://doi.org/10.1080/21680566.2015.1060582.
Wang, Z., S. Ji, and B. Yu. 2019. “Short-term traffic volume forecasting with asymmetric loss based on enhanced KNN method.” Math. Problems Eng. 2019: 1–11. https://doi.org/10.1155/2019/4589437.
Wu, Y., H. Tan, L. Qin, B. Ran, and Z. Jiang. 2018. “A hybrid deep learning based traffic flow prediction method and its understanding.” Transp. Res. Part C Emerging Technol. 90 (May): 166–180. https://doi.org/10.1016/j.trc.2018.03.001.
Xie, P., T. Li, J. Liu, S. Du, X. Yang, and J. Zhang. 2020. “Urban flow prediction from spatiotemporal data using machine learning: A survey.” Inf. Fusion 59 (Jul): 1–12. https://doi.org/10.1016/j.inffus.2020.01.002.
Yang, L., Q. Yang, Y. Li, and Y. Feng. 2019. “K-Nearest neighbor model based short-term traffic flow prediction method.” In Proc., 2019 18th Int. Symp. on Distributed Computing and Applications for Business Engineering and Science (DCABES), 27–30. New York: IEEE. https://doi.org/10.1109/DCABES48411.2019.00014.
Zhan, H., G. Gomes, X. S. Li, K. Madduri, A. Sim, and K. Wu. 2018. “Consensus ensemble system for traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 19 (12): 3903–3914. https://doi.org/10.1109/TITS.2018.2791505.
Zhang, L., N. R. Alharbe, G. Luo, Z. Yao, and Y. Li. 2018. “A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction.” Tsinghua Sci. Technol. 23 (4): 479–492. https://doi.org/10.26599/TST.2018.9010045.
Zhang, T., and G. Guo. 2020. “Graph attention LSTM: A spatio-temperal approach for traffic flow forecasting.” IEEE Intell. Transp. Syst. Mag. 14 (2): 190–196. https://doi.org/10.1109/MITS.2020.2990165.
Zhao, F., G.-Q. Zeng, and K.-D. Lu. 2020. “EnLSTM-WPEO: Short-term traffic flow prediction by ensemble LSTM, NNCT weight integration, and population extremal optimization.” IEEE Trans. Veh. Technol. 69 (1): 101–113. https://doi.org/10.1109/TVT.2019.2952605.
Zhao, L., Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li. 2019. “T-GCN: A temporal graph convolutional network for traffic prediction.” IEEE Trans. Intell. Transp. Syst. 21 (9): 3848–3858. https://doi.org/10.1109/TITS.2019.2935152.
Zhao, Z., W. Chen, X. Wu, P. C. Y. Chen, and J. Liu. 2017. “LSTM network: A deep learning approach for short-term traffic forecast.” IET Intel. Transp. Syst. 11 (2): 68–75. https://doi.org/10.1049/iet-its.2016.0208.
Zhou, P., W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, and B. Xu. 2016. “Attention-based bidirectional long short-term memory networks for relation classification.” In Proc., 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 207–212. Berlin: Association for Computational Linguistics. https://doi.org/10.18653/v1/p16-2034.
Zhou, T., G. Han, X. Xu, Z. Lin, C. Han, Y. Huang, and J. Qin. 2017. “δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting.” Neurocomputing 247 (Jul): 31–38. https://doi.org/10.1016/j.neucom.2017.03.049.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 26, 2021
Accepted: Dec 20, 2021
Published online: Mar 30, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 30, 2022
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