Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 16, 2024 - Issue 5
326
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A multi-directional recurrent graph convolutional network model for reconstructing traffic spatiotemporal diagram

ORCID Icon, ORCID Icon, , &
Pages 405-415 | Received 18 Oct 2022, Accepted 30 Mar 2023, Published online: 03 Apr 2023

References

  • Arjona Martinez, J., M. P. Linares, and J. Casanovas. 2021. “Characterizing Parking Systems from Sensor Data Through a Data-Driven Approach.” Transportation Letters 13 (3): 183–192. doi:10.1080/19427867.2020.1866331.
  • Asadi, R., and A. Regan. 2019. “A Convolution Recurrent Autoencoder for Spatio-Temporal Missing Data Imputation.” arXiv preprint arXiv: 190412413. doi:10.48550/arXiv.1904.12413.
  • Asif, M. T., N. Mitrovic, J. Dauwels, and P. Jaillet. 2016. “Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks.” IEEE Transactions on Intelligent Transportation Systems 17 (7): 1816–1825. doi:10.1109/TITS.2015.2507259.
  • Atwood, J., and D. Towsley. 2016. “Diffusion-Convolutional Neural Networks.” in Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2001–2009.
  • Bai, L., L. Yao, S. Kanhere, X. Wang, and Q. Sheng. 2019. “Stg2seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting.” arXiv preprint arXiv: 190510069. doi:10.48550/arXiv.1905.10069.
  • Ban, X., L. Chu, and H. Benouar. 2007. “Bottleneck Identification and Calibration for Corridor Management Planning.” Transportation Research Record 1999 (1): 40–53. doi:10.3141/1999-05.
  • Benkraouda, O., B. T. Thodi, H. Yeo, M. Menéndez, and S. E. Jabari. 2020. “Traffic Data Imputation Using Deep Convolutional Neural Networks.” IEEE Access 8: 104740–104752. doi:10.1109/ACCESS.2020.2999662.
  • Chen, M., and S. I. Chien. 2001. “Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based versus Path Based.” Transportation Research Record 1768 (1): 157–161. doi:10.3141/1768-19.
  • Chen, X., Z. He, and L. Sun. 2019. “A Bayesian Tensor Decomposition Approach for Spatiotemporal Traffic Data Imputation.” Transportation Research Part C: Emerging Technologies 98: 73–84. doi:10.1016/j.trc.2018.11.003.
  • Chen, X., and L. Sun. 2022. “Bayesian Temporal Factorization for Multidimensional Time Series Prediction.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (9): 4659–4673. doi:10.1109/TPAMI.2021.3066551.
  • Chen, C., Y. Wang, L. Li, J. Hu, and Z. Zhang. 2012. “The Retrieval of Intra-Day Trend and Its Influence on Traffic Prediction.” Transportation Research Part C: Emerging Technologies 22: 103–118. doi:10.1016/j.trc.2011.12.006.
  • Geng, X., Y. Li, L. Wang, L. Zhang, Q. Yang, J. Ye, and Y. Liu 2019. “Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting.” in Proceedings of the AAAI conference on artificial intelligence, 3656–3663. doi:10.1609/aaai.v33i01.33013656.
  • Graves, A., S. Fernández, and J. Schmidhuber. 2005. “Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition.” in 15th International Conference on Artificial Neural Networks, Warsaw, Poland, 799–804.
  • Han, Y., A. Hegyi, Y. Yuan, S. Hoogendoorn, M. Papageorgiou, and C. Roncoli. 2017. “Resolving Freeway Jam Waves by Discrete First-Order Model-Based Predictive Control of Variable Speed Limits.” Transportation Research Part C: Emerging Technologies 77: 405–420. doi:10.1016/j.trc.2017.02.009.
  • He, Z., S. He, and W. Guan. 2015. “A Figure-Eight Hysteresis Pattern in Macroscopic Fundamental Diagrams and Its Microscopic Causes.” Transportation Letters 7 (3): 133–142. doi:10.1179/1942787514Y.0000000041.
  • Henrickson, K., Y. Zou, and Y. Wang. 2015. “Flexible and Robust Method for Missing Loop Detector Data Imputation.” Transportation Research Record 2527 (1): 29–36. doi:10.3141/2527-04.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. doi:10.1109/CVPR.2016.90.
  • Joelianto, E., M. F. Fathurrahman, H. Y. Sutarto, I. Semanjski, A. Putri, and S. Gautama. 2022. “Analysis of Spatiotemporal Data Imputation Methods for Traffic Flow Data in Urban Networks.” ISPRS International Journal of Geo-Information 11 (5): 310. doi:10.3390/ijgi11050310.
  • Kessler, L., B. Karl, and K. Bogenberger. 2019. “Spatiotemporal Traffic Speed Reconstruction from Travel Time Measurements Using Bluetooth Detection.” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 4275–4280. doi:10.1109/ITSC.2019.8917084.
  • Khajeh Hosseini, M., and A. Talebpour. 2019. “Traffic Prediction Using Time-Space Diagram: A Convolutional Neural Network Approach.” Transportation Research Record 2673 (7): 425–435. doi:10.1177/0361198119841291.
  • Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv preprint arXiv: 1412 6980. doi:10.48550/arXiv.1412.6980.
  • Liang, Y., Z. Zhao, and L. Sun. 2021. “Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns.” Transportation Research Part C: Emerging Technologies 143: 103826. doi:10.1016/j.trc.2022.103826.
  • Li, Y., Z. Li, and L. Li. 2014. “Missing Traffic Data: Comparison of Imputation Methods.” IET Intelligent Transport Systems 8 (1): 51–57. doi:10.1049/iet-its.2013.0052.
  • Lin, L., Z. He, and S. Peeta. 2018. “Predicting Station-Level Hourly Demand in a Large-Scale Bike-Sharing Network: A Graph Convolutional Neural Network Approach.” Transportation Research Part C: Emerging Technologies 97: 258–276. doi:10.1016/j.trc.2018.10.011.
  • Long, Q., J. F. Zhang, and Z. M. Zhou. 2015. “Multi-Objective Traffic Signal Control Model for Traffic Management.” Transportation Letters 7 (4): 196–200. doi:10.1179/1942787515Y.0000000002.
  • Luo, A., B. Shangguan, C. Yang, F. Gao, Z. Fang, and D. Yu. 2022. “Spatial-Temporal Diffusion Convolutional Network: A Novel Framework for Taxi Demand Forecasting.” ISPRS International Journal of Geo-Information 11 (3): 193. doi:10.3390/ijgi11030193.
  • Lv, Y., Y. Duan, W. Kang, Z. Li, and F. -Y. Wang. 2014. “Traffic Flow Prediction with Big Data: A Deep Learning Approach.” IEEE Transactions on Intelligent Transportation Systems 16 (2): 865–873. doi:10.1109/TITS.2014.2345663.
  • Qu, L., L. Li, Y. Zhang, and J. Hu. 2009. “PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach.” IEEE Transactions on Intelligent Transportation Systems 10 (3): 512–522. doi:10.1109/TITS.2009.2026312.
  • Ran, B., H. Tan, Y. Wu, and P. J. Jin. 2016. “Tensor Based Missing Traffic Data Completion with Spatial–Temporal Correlation.” Physica A: Statistical Mechanics and Its Applications 446: 54–63. doi:10.1016/j.physa.2015.09.105.
  • Rempe, F., P. Franeck, and K. Bogenberger. 2022. “On the Estimation of Traffic Speeds with Deep Convolutional Neural Networks Given Probe Data.” Transportation Research Part C: Emerging Technologies 134: 103448. doi:10.1016/j.trc.2021.103448.
  • Rempe, F., P. Franeck, U. Fastenrath, and K. Bogenberger. 2017. “A Phase-Based Smoothing Method for Accurate Traffic Speed Estimation with Floating Car Data.” Transportation Research Part C: Emerging Technologies 85: 644–663. doi:10.1016/j.trc.2017.10.015.
  • Smith, B. L., W. T. Scherer, and J. H. Conklin. 2003. “Exploring Imputation Techniques for Missing Data in Transportation Management Systems.” Transportation Research Record 1836 (1): 132–142. doi:10.3141/1836-17.
  • Treiber, M., and D. Helbing. 2003. “An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information.” Interface and Transport Dynamics 32:343–360.
  • Treiber, M., A. Kesting, and R. E. Wilson. 2011. “Reconstructing the Traffic State by Fusion of Heterogeneous Data.” Computer‐aided Civil and Infrastructure Engineering 26 (6): 408–419. doi:10.1111/j.1467-8667.2010.00698.x.
  • Van Lint, J., and S. P. Hoogendoorn. 2010. “A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways.” Computer‐aided Civil and Infrastructure Engineering 25 (8): 596–612. doi:10.1111/j.1467-8667.2009.00617.x.
  • Visin, F., K. Kastner, K. Cho, M. Matteucci, A. Courville, and Y. Bengio. 2015. “Renet: A Recurrent Neural Network Based Alternative to Convolutional Networks.” arXiv preprint arXiv: 150500393. doi:10.48550/arXiv.1505.00393.
  • Wu, Z., S. Pan, G. Long, J. Jiang, and C. Zhang. 2019. “Graph Wavenet for Deep Spatial-Temporal Graph Modeling.” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, 1907–1913.
  • Yang, B., Y. Kang, Y. Yuan, H. Li, and F. Wang. 2022. “ST-FVGAN: Filling Series Traffic Missing Values with Generative Adversarial Network.” Transportation Letters 14 (4): 407–415. doi:10.1080/19427867.2021.1879624.
  • Yu, B., H. Yin, and Z. Zhu. 2018. “Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, 3634–3640. doi:10.24963/ijcai.2018/505.
  • Zhang, K., X. Feng, N. Jia, L. Zhao, and Z. He. 2022. “TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams.” Physica A: Statistical Mechanics and Its Applications 591: 126788. doi:10.1016/j.physa.2021.126788.
  • Zhang, K., L. Zheng, Z. Liu, and N. Jia. 2020. “A Deep Learning Based Multitask Model for Network-Wide Traffic Speed Prediction.” Neurocomputing 396: 438–450. doi:10.1016/j.neucom.2018.10.097.
  • Zhong, M., P. Lingras, and S. Sharma. 2004. “Estimation of Missing Traffic Counts Using Factor, Genetic, Neural, and Regression Techniques.” Transportation Research Part C: Emerging Technologies 12 (2): 139–166. doi:10.1016/j.trc.2004.07.006.
  • Zhou, W., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:10.1109/TIP.2003.819861.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.