Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 10
Abstract
The postpandemic period has seen a significant increase in traffic volume on freeways, necessitating the implementation of advanced traffic management systems, such as lane-level freeway tolling systems, to predict traffic patterns and alleviate congestion. Although deep learning models have proven effective in predicting traffic states, little research has focused on lane-level traffic prediction, which is crucial for emerging intelligent transportation applications. To address this gap, this study develops a lane-level road segment graph and proposes a lane-based road network traffic volume prediction model, GCN-LSTM, that combines graph convolution network (GCN) and long short-term memory (LSTM). The proposed model employs different graph Laplacian matrices, and the performance of these corresponding derived models is compared with that of existing traffic prediction models. The proposed model is evaluated using traffic volume data collected from inductive loop detectors installed on freeways in the Seattle area, including both high-occupancy toll lanes and general-purpose lanes. The results demonstrate that the GCN-LSTM model with the combinatorial Laplacian matrix outperforms other models. Additionally, the model’s prediction performance remains consistent when using input data with various temporal ranges. Furthermore, excluding high-occupancy toll lane data from the dataset improves the prediction accuracy, highlighting the importance of developing specialized models for lane-level traffic prediction tasks.
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 that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Pacific Northwest Transportation Consortium (PacTrans) and the University of Washington. Thanks to STAR Lab at the University of Washington for providing the research data sets.
Author contributions: The authors confirm contribution to the paper as follows: study conception and design: L. Liu, R. Ke, Y. Wang; data collection: L. Liu, Z. Cui; analysis and interpretation of results: L. Liu; and draft manuscript preparation: L. Liu, Z. Cui. All authors reviewed the results and approved the final version of the manuscript.
References
Bian, Z., Z. Zhang, X. Liu, and X. Qin. 2019. “Unobserved component model for predicting monthly traffic volume.” J. Transp. Eng. Part A Syst. 145 (12): 4019052. https://doi.org/10.1061/JTEPBS.0000281.
Chen, Y., and X. M. Chen. 2022. “A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction.” Transp. Res. Part C Emerging Technol. 143 (Oct): 103820. https://doi.org/10.1016/j.trc.2022.103820.
Cui, Z., M. Zhu, S. Wang, P. Wang, Y. Zhou, Q. Cao, C. Kopca, and Y. Wang. 2020. “Traffic performance score for measuring the impact of COVID-19 on urban mobility.” Preprint, submitted July 10, 2020. http://arxiv.org/abs/2007.00648.
Graves, A., and J. Schmidhuber. 2005. “Frame wise phoneme classification with bidirectional LSTM and other neural network architectures.” Neural Networks 18 (5–6): 602–610. https://doi.org/10.1016/j.neunet.2005.06.042.
Gu, Y., W. Lu, L. Qin, M. Li, and Z. Shao. 2019. “Short-term prediction of lane-level traffic speeds: A fusion deep learning model.” Transp. Res. Part C Emerging Technol. 106 (Sep): 1–16. https://doi.org/10.1016/j.trc.2019.07.003.
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.
Ke, R., W. Li, Z. Cui, and Y. Wang. 2020. “Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact.” Transp. Res. Rec. 2674 (4): 459–470. https://doi.org/10.1177/0361198120911052.
Kumar, S. V. 2017. “Traffic flow prediction using Kalman filtering technique.” Procedia Eng. 187 (Jan): 582–587. https://doi.org/10.1016/j.proeng.2017.04.417.
Lowry, M. 2014. “Spatial interpolation of traffic counts based on origin-destination centrality.” J. Transp. Geogr. 36 (Apr): 98–105. https://doi.org/10.1016/j.jtrangeo.2014.03.007.
Lu, W., Y. Rui, and B. Ran. 2020a. “Lane-level traffic speed forecasting: A novel mixed deep learning model.” IEEE Trans. Intell. Transp. Syst. 23 (4): 3601–3612. https://doi.org/10.1109/TITS.2020.3038457.
Lu, W., Y. Rui, Z. Yi, B. Ran, and Y. Gu. 2020b. “A hybrid model for lane-level traffic flow forecasting based on complete ensemble empirical mode decomposition and extreme gradient boosting.” IEEE Access 8 (Feb): 42042–42054. https://doi.org/10.1109/ACCESS.2020.2977219.
Luo, X., D. Li, Y. Yang, and S. Zhang. 2019. “Spatiotemporal traffic flow prediction with KNN and LSTM.” J. Adv. Transp. 2019 (Feb): 10. https://doi.org/10.1155/2019/4145353.
Ma, T., C. Antoniou, and T. Toledo. 2020. “Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast.” Transp. Res. Part C Emerging Technol. 111 (Feb): 352–372. https://doi.org/10.1016/j.trc.2019.12.022.
Rajabzadeh, Y., A. H. Rezaie, and H. Amindavar. 2017. “Short-term traffic flow prediction using time-varying Vasicek model.” Transp. Res. Part C Emerging Technol. 74 (Jan): 168–181. https://doi.org/10.1016/j.trc.2016.11.001.
Rossi, R., M. Gastaldi, and G. Gecchele. 2014. “Comparison of clustering methods for road group identification in FHWA traffic monitoring approach: Effects on AADT estimates.” J. Transp. Eng. 140 (7): 4014025. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000676.
Shen, G., K. Yu, M. Zhang, and X. Kong. 2021. “ST-AFN: A spatial-temporal attention based fusion network for lane-level traffic flow prediction.” PeerJ Comput. Sci. 7 (Apr): e470. https://doi.org/10.7717/peerj-cs.470.
Sun, P., A. Boukerche, and Y. Tao. 2020. “SSGRU: A novel hybrid stacked GRU-based traffic volume prediction approach in a road network.” Comput. Commun. 160 (Jul): 502–511. https://doi.org/10.1016/j.comcom.2020.06.028.
Tang, J., X. Chen, Z. Hu, F. Zong, C. Han, and L. Li. 2019. “Traffic flow prediction based on combination of support vector machine and data denoising schemes.” Physica A 534 (Nov): 120642. https://doi.org/10.1016/j.physa.2019.03.007.
Vanajakshi, L., and L. R. Rilett. 2007. “Support vector machine technique for the short term prediction of travel time.” In Proc., 2007 IEEE Intelligent Vehicles Symp., 600–605. New York: IEEE.
WSDOH (Washington State Department of Health). 2022a. Forward: The next phase of WA’s COVID-19 Response (through 2022). Tumwater, WA: WSDOH.
WSDOH (Washington State Department of Health). 2022b. I-405 express toll lanes. Tumwater, WA: WSDOH.
Xu, C., Z. Li, and W. Wang. 2016. “Short-term traffic flow prediction using a methodology based on autoregressive integrated moving average and genetic programming.” Transport 31 (3): 343–358. https://doi.org/10.3846/16484142.2016.1212734.
Yang, B., S. Wang, and Y. Bao. 2011. “Efficient local AADT estimation via SCAD variable selection based on regression models.” In Proc., 2011 Chinese Control and Decision Conf., 1898–1902. New York: IEEE.
Zarei, N., M. A. Ghayour, and S. Hashemi. 2013. “Road traffic prediction using context-aware random forest based on volatility nature of traffic flows.” In Proc., Intelligent Information and Database Systems: 5th Asian Conf., ACIIDS 2013, Kuala Lumpur, Malaysia, March 18–20, 2013, Proc., Part I 5, 196–205. Heidelberg, Germany: Springer.
Zhang, Z., M. Li, X. Lin, Y. Wang, and F. He. 2018. “Multistep speed prediction on traffic networks: A graph convolutional sequence-to-sequence learning approach with attention mechanism.” Preprint, submitted January 30, 2030. http://arxiv.org/abs/1810.10237.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 23, 2022
Accepted: Jun 12, 2023
Published online: Aug 4, 2023
Published in print: Oct 1, 2023
Discussion open until: Jan 4, 2024
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.