Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party (Loop detector data in Jinan, China). Direct requests for these materials may be made to the provider as indicated in the Acknowledgements.
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies (Li, W., Ban, X., Zheng, J., Liu, H., Gong, C. 2018. Traffic volume simulation data. https://drive.google.com/drive/u/1/folders/14uXbW9d1RlZlWa6SYiX_CtLewr8AVe7X).
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (DISTN code).
Acknowledgments
This research was partially supported by a research grant from DiDi Chuxing to the University of Washington. The results and opinions in the paper are the authors’, which do not necessarily reflect those of the sponsor.
References
Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, and M. Kudlur. 2016. “Tensorflow: A system for large-scale machine learning.” In Proc., 12th Symp. on Operating Systems Design and Implementation, 265–283. Berkeley, CA: USENIX.
Ahmed, M. S., and A. R. Cook. 1979. “Analysis of freeway traffic time-series data by using Box-Jenkins techniques.” Transport. Res. Rec. 722: 1–9.
Chang, H., Y. Lee, B. Yoon, and S. Baek. 2012. “Dynamic near-term traffic flow prediction: System-oriented approach based on past experiences.” IET Intel. Transport Syst. 6 (3): 292–305. https://doi.org/10.1049/iet-its.2011.0123.
Coogan, S., C. Flores, and P. Varaiya. 2017. “Traffic predictive control from low-rank structure.” Transp. Res. Part B: Methodol. 97 (Mar): 1–22. https://doi.org/10.1016/j.trb.2016.11.013.
Cui, Z., K. Henrickson, R. Ke, and Y. Wang. 2019. “High-order graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting.” IEEE Trans. Intell. Transp. Syst.: 1–12. https://doi.org/10.1109/TITS.2019.2950416.
Cui, Z., R. Ke, and Y. Wang. 2016. “Deep stacked bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction.” In Proc., 6th Int. Workshop on Urban Computing.
Davis, G. A., and N. L. Nihan. 1991. “Nonparametric regression and short-term freeway traffic forecasting.” J. Transp. Eng. 117 (2): 178–188. https://doi.org/10.1061/(ASCE)0733-947X(1991)117:2(178).
Duan, Y., Y. Lv, and F. Y. Wang. 2016. “Travel time prediction with LSTM neural network.” In Proc., 19th Int. Conf. on Intelligent Transportation Systems, 1053–1058. New York: IEEE.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
Greff, K., R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber. 2017. “LSTM: A search space odyssey.” IEEE Trans. Neural Networks Learn. Syst. 28 (10): 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924.
Guo, J., W. Huang, and B. M. Williams. 2014. “Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification.” Transp. Res. Part C: Emerging Technol. 43 (Jun): 50–64. https://doi.org/10.1016/j.trc.2014.02.006.
Henaff, M., J. Bruna, and Y. LeCun. 2015. “Deep convolutional networks on graph-structured data.” Preprint, submitted June 16, 2015. http://arxiv.org/abs/1506.05163.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Jeong, Y. S., Y. J. Byon, M. M. Castro-Neto, and S. M. Easa. 2013. “Supervised weighting-online learning algorithm for short-term traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 14 (4): 1700–1707. https://doi.org/10.1109/TITS.2013.2267735.
Jia, Y., J. Wu, and M. Xu. 2017. “Traffic flow prediction with rainfall impact using a deep learning method.” J. Adv. Transp. : 1–10. https://doi.org/10.1155/2017/6575947.
Kamarianakis, Y., and P. Prastacos. 2003. “Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches.” Transp. Res. Rec. 1857 (1): 74–84. https://doi.org/10.3141/1857-09.
Karpathy, A., J. Johnson, and L. Fei-Fei. 2015. “Visualizing and understanding recurrent networks.” Preprint, submitted June 5, 2015. http://arxiv.org/abs/1506.02078.
Ke, R., W. Li, Z. Cui, and Y. Wang. 2020. “Two-stream multi-channel convolutional neural network (TM-CNN) for multi-lane traffic speed prediction considering traffic volume impact.” Transp. Res. Rec. https://doi.org/10.1177/0361198120911052.
Ke, R., Z. Li, J. Tang, Z. Pan, and Y. Wang. 2018. “Real-time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow.” IEEE Trans. Intell. Transp. Syst. 20 (1): 54–64. https://doi.org/10.1109/TITS.2018.2797697.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2015. http://arxiv.org/abs/1412.6980.
Kutz, J. N. 2017. “Deep learning in fluid dynamics.” J. Fluid Mech. 814 (Mar): 1–4. https://doi.org/10.1017/jfm.2016.803.
Lee, S., and D. Fambro. 1999. “Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting.” Transp. Res. Rec. 1678 (1): 179–188. https://doi.org/10.3141/1678-22.
Leonel, J. 2019. “Hyperparameters in machine/deep learning.” Accessed September 17, 2019. https://medium.com/@jorgesleonel/hyperparameters-in-machine-deep-learning-ca69ad10b981.
Li, W., and X. Ban. 2019. “A deep learning approach for lane-based short-term traffic volume prediction at signalized intersections.” In Proc., 98th Annual Meeting of Transportation Research Board. Washington, DC: Transportation Research Board.
Li, W., and X. J. Ban. 2017. “Traffic signal timing optimization in connected vehicles environment.” In Proc., 2017 IEEE Intelligent Vehicles Symposium (IV), 1330–1335. New York: IEEE.
Li, W., and X. J. Ban. 2018. “Connected vehicle-based traffic signal timing optimization.” IEEE Trans. Intell. Transp. Syst. 20 (12): 4354–4366. https://doi.org/10.1109/TITS.2018.2883572.
Li, W., J. Wang, R. Fan, Q. Guo, Y. Zhang, N. Siddique, and X. Ban. 2020. “Short-term traffic state prediction from latent structures: accuracy vs. efficiency.” Transp. Res. Part C 111: 72–90.
Lv, Y., Y. Duan, W. Kang, Z. Li, and F. Wang. 2015. “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.
Ma, X., Z. Dai, Z. He, J. Ma, Y. Wang, and Y. Wang. 2017. “Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction.” Sensors 17 (4): 818. https://doi.org/10.3390/s17040818.
Mishra, R. 2017. “Concatenating two lists in Python.” Accessed on January 19, 2019. https://blog.usejournal.com/concatenating-two-lists-in-python-3cf9051da17f.
Ng, A. 2017. “Machine learning yearning.” Accessed on September 17, 2019. https://github.com/yennlh/ml-yearning/blob/master/Ng_MLY01_13.pdf.
Ojeda, L. R. L., A. Y. Kibangou, and C. C. De Wit. 2013. “Adaptive Kalman filtering for multi-step-ahead traffic flow prediction.” In Proc., 2013 American Control Conf. New York: IEEE.
Polson, N. G., and V. O. Sokolov. 2017. “Deep learning for short-term traffic flow prediction.” Transp. Res. Part C: Emerging Technol. 79 (Jun): 1–17. https://doi.org/10.1016/j.trc.2017.02.024.
Radhakrishnan, P. 2017. “What are hyperparameters? And how to tune the hyperparameters in a deep neural network?” Accessed on September 17, 2019. https://towardsdatascience.com/what-are-hyperparameters-and-how-to-tune-the-hyperparameters-in-a-deep-neural-network-d0604917584a.
Sanjeevi, M. 2018. “DeepNLP — LSTM (long short term memory) networks with math.” Accessed September 17, 2019. https://medium.com/deep-math-machine-learning-ai/chapter-10-1-deepnlp-lstm-long-short-term-memory-networks-with-math-21477f8e4235.
Sun, S., C. Zhang, and G. Yu. 2006. “A Bayesian network approach to traffic flow forecasting.” IEEE Trans. Intell. Transp. Syst. 7 (1): 124–132. https://doi.org/10.1109/TITS.2006.869623.
Williams, B., P. Durvasula, and D. Brown. 1998. “Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models.” Transp. Res. Rec. 1644 (1): 132–141. https://doi.org/10.3141/1644-14.
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, Y., Y. Zhang, and Z. Ye. 2007. “Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition” Comput. Aided Civ. Infrastruct. Eng. 22 (5): 326–334. https://doi.org/10.1111/j.1467-8667.2007.00489.x.
Yao, H., X. Tang, H. Wei, G. Zheng, Y. Yu, and Z. Li. 2018a. “Modeling spatial-temporal dynamics for traffic prediction.” Preprint, submitted March 3, 2018. http://arxiv.org/abs/1803.01254.
Yao, H., F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, and J. Ye. 2018b. “Deep multi-view spatial-temporal network for taxi demand prediction.” Proc., 32nd AAAI Conf. on Artificial Intelligence. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 22, 2019
Accepted: Feb 11, 2020
Published online: Jun 10, 2020
Published in print: Aug 1, 2020
Discussion open until: Nov 10, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.