A Transfer Learning–Based LSTM for Traffic Flow Prediction with Missing Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 10
Abstract
Traffic flow prediction plays an important role in intelligent transportation systems (ITS) on freeways. However, incomplete traffic information tends to be collected by traffic detectors, which is a major constraint for existing methods to get precise traffic predictions. To overcome this limitation, this study aims to propose and evaluate a new advanced model, named transfer learning–based long short-term memory (LSTM) model for traffic flow forecasting with incomplete traffic information, that adopts traffic information from similar locations for the target location to increase the data quality. More specifically, dynamic time warping (DTW) is used to evaluate the similarity between the source and target domains and then transfer the most similar data to the target domain to generate a hybrid complete training sample for LSTM to improve the prediction performance. To evaluate the effectiveness of the transfer learning–based LSTM, this study implements empirical studies with a real-world data set collected from a stretch of I-15 freeway in Utah. Experimental study results indicate that the transfer learning–based LSTM network could effectively predict the traffic flow conditions with a training sample with missing values.
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 research is supported by the project “CMMI #2047268 CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems” funded by the National Science Foundation (NSF).
References
Cai, P., Y. Wang, G. Lu, P. Chen, C. Ding, and J. Sun. 2016. “A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting.” Transp. Res. Part C Emerging Technol. 62 (Jan): 21–34. https://doi.org/10.1016/j.trc.2015.11.002.
Castro-Neto, M., Y. S. Jeong, M. K. Jeong, and L. D. Han. 2009. “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions.” Expert Syst. Appl. 36 (3): 6164–6173. https://doi.org/10.1016/j.eswa.2008.07.069.
Chen, D. 2017. “Research on traffic flow prediction method based on BP neural network.” IEEE Trans. Ind. Inf. 13 (4): 2000–2008. https://doi.org/10.1109/TII.2017.2682855.
Chen, M., X. Yu, and Y. Liu. 2018. “PCNN: Deep convolutional networks for short-term traffic congestion prediction.” IEEE Trans. Intell. Transp. Syst. 19 (11): 3550–3559. https://doi.org/10.1109/TITS.2018.2835523.
Chen, Z., H. Xu, P. Jiang, S. Yu, G. Lin, I. Bychkov, A. Hmelnov, G. Ruzhnikov, N. Zhu, and Z. Liu. 2021. “A transfer learning-based LSTM strategy for imputing large-scale consecutive missing data and its application in a water quality prediction system.” J. Hydrol. 602 (Nov): 126573. https://doi.org/10.1016/j.jhydrol.2021.126573.
Dell’acqua, P., F. Bellotti, R. Berta, and A. De Gloria. 2015. “Time-aware multivariate nearest neighbor regression methods for traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 16 (6): 3393–3402. https://doi.org/10.1109/TITS.2015.2453116.
Dia, H. 2001. “An object-oriented neural network approach to short-term traffic forecasting.” Eur. J. Oper. Res. 131 (2): 253–261. https://doi.org/10.1016/S0377-2217(00)00125-9.
Do, L. N. N., H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung. 2019. “An effective spatial-temporal attention based neural network for traffic flow prediction.” Transp. Res. Part C Emerging Technol. 108 (Nov): 12–28. https://doi.org/10.1016/j.trc.2019.09.008.
Folgado, D., M. Barandas, R. Matias, R. Martins, M. Carvalho, and H. Gamboa. 2018. “Time alignment measurement for time series.” Pattern Recognit. 81 (Sep): 268–279. https://doi.org/10.1016/j.patcog.2018.04.003.
Fu, R., Z. Zhang, and L. Li. 2016. “Using LSTM and GRU neural network methods for traffic flow prediction.” In Proc., 31st Youth Academic Annual Conf. of Chinese Association of Automation (YAC), 324–328. New York: IEEE.
Fu, T. C. 2011. “A review on time series data mining.” Eng. Appl. Artif. Intell. 24 (1): 164–181. https://doi.org/10.1016/j.engappai.2010.09.007.
Gers, F. A., J. Schmidhuber, and F. Cummins. 1999. “Learning to forget: Continual prediction with LSTM.” Neural Comput. 12 (10): 2451–2471. https://doi.org/10.1162/089976600300015015.
Ghosh, B., B. Basu, and M. O’Mahony. 2007. “Bayesian time-series model for short-term traffic flow forecasting.” J. Transp. Eng. 133 (3): 180–189. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:3(180).
Giorgi, J. M., and G. D. Bader. 2018. “Transfer learning for biomedical named entity recognition with neural networks.” Bioinformatics 34 (23): 4087–4094. https://doi.org/10.1093/bioinformatics/bty449.
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.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short term memory.” Neural Comput. 9 (8): 1735. https://doi.org/10.1162/neco.1997.9.8.1735.
Huang, Y., X. Song, S. Zhang, and J. J. Q. Yu. 2021. “Transfer learning in traffic prediction with graph neural networks.” In Proc., IEEE Int. Intelligent Transportation Systems Conf. (ITSC), 3732–3737. New York: IEEE.
Kang, D., Y. Lv, and Y. Chen. 2017. “Short-term traffic flow prediction with LSTM recurrent neural network.” In Proc., 20th Int. Conf. on Intelligent Transportation Systems (ITSC), 1–6. New York: IEEE.
Kasundra, K., V. Garikapati, C. Hoehne, Y. Hou, and S. Young. 2022. Spatial transferability of machine learning based volume estimation models. Golden, CO: National Renewable Energy Lab.
Li, H., J. Liu, Z. Yang, R. W. Liu, K. Wu, and Y. Wan. 2020. “Adaptively constrained dynamic time warping for time series classification and clustering.” Inf. Sci. 534 (Sep): 97–116. https://doi.org/10.1016/j.ins.2020.04.009.
Liu, Y., J. J. Q. Yu, J. Kang, D. Niyato, and S. Zhang. 2020. “Privacy-preserving traffic flow prediction: A federated learning approach.” IEEE Internet Things J. 7 (8): 7751–7763. https://doi.org/10.1109/JIOT.2020.2991401.
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.
Lv, M., Y. Li, L. Chen, and T. Chen. 2019. “Air quality estimation by exploiting terrain features and multi-view transfer semi-supervised regression.” Inf. Sci. 483 (May): 82–95. https://doi.org/10.1016/j.ins.2019.01.038.
Lv, Y., Y. Duan, W. Kang, Z. Li, and F. Y. 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. Tao, Y. Wang, H. Yu, and Y. Wang. 2015. “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data.” Transp. Res. Part C Emerging Technol. 54 (May): 187–197. https://doi.org/10.1016/j.trc.2015.03.014.
Mackenzie, J., J. F. Roddick, and R. Zito. 2019. “An evaluation of HTM and LSTM for short-term arterial traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 20 (5): 1847–1857. https://doi.org/10.1109/TITS.2018.2843349.
Ni, D., and J. D. Leonard. 2005. “Markov chain Monte Carlo multiple imputation using Bayesian networks for incomplete intelligent transportation systems data.” Transp. Res. Rec. 1935 (1): 57–67. https://doi.org/10.1177/0361198105193500107.
Ojeda, L. L., A. Y. Kibangou, and C. C. De Wit. 2013. “Adaptive Kalman filtering for multi-step ahead traffic flow prediction.” In Proc., American Control Conf., 4724–4729. New York: IEEE.
Pan, S. J., and Q. Yang. 2009. “A survey on transfer learning.” IEEE Trans. Knowl. Data Eng. 22 (10): 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Qu, L., L. Li, Y. Zhang, and J. Hu. 2009. “PPCA-based missing data imputation for traffic flow volume: A systematical approach.” IEEE Trans. Intell. Transp. Syst. 10 (3): 512–522. https://doi.org/10.1109/TITS.2009.2026312.
Qu, Z., H. Li, Z. Li, and T. Zhong. 2022. “Short-term traffic flow forecasting method with M-B-LSTM hybrid network.” IEEE Trans. Intell. Transp. Syst. 23 (1): 225–235. https://doi.org/10.1109/TITS.2020.3009725.
Shi, G., J. Guo, W. Huang, and B. M. Williams. 2014. “Modeling seasonal heteroscedasticity in vehicular traffic condition series using a seasonal adjustment approach.” J. Transp. Eng. 140 (5): 04014012. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000656.
Smith, B., and M. J. Demetsky. 1994. “Short-term traffic flow prediction models-A comparison of neural network and nonparametric regression approaches.” In Proc., IEEE Int. Conf. on Systems, Man and Cybernetics. New York: IEEE.
Sun, B., W. Cheng, P. Goswami, and G. Bai. 2018. “Short-term traffic forecasting using self-adjusting k-nearest neighbours.” IET Intell. Transp. Syst. 12 (1): 41–48. https://doi.org/10.1049/iet-its.2016.0263.
Tak, S., S. Woo, and H. Yeo. 2016. “Data-driven imputation method for traffic data in sectional units of road links.” IEEE Trans. Intell. Transp. Syst. 17 (6): 1762–1771. https://doi.org/10.1109/TITS.2016.2530312.
Tian, Y., K. Zhang, J. Li, X. Lin, and B. Yang. 2018a. “LSTM-based traffic flow prediction with missing data.” Neurocomputing 318 (Nov): 297–305. https://doi.org/10.1016/j.neucom.2018.08.067.
Tian, Y., K. Zhang, J. Li, X. Lin, and B. Yang. 2018b. “Neurocomputing LSTM-based traffic flow prediction with missing data.” Neurocomputing 318 (Nov): 297–305. https://doi.org/10.1016/j.neucom.2018.08.067.
Tipping, M. E., and C. M. Bishop. 1999. “Mixtures of probabilistic principal component analyzers.” Neural Comput. 11 (2): 443–482. https://doi.org/10.1162/089976699300016728.
Van Der Voort, M., M. Dougherty, and S. Watson. 1996. “Combining Kohonen maps with ARIMA time series models to forecast traffic flow.” Transp. Res. Part C Emerging Technol. 4 (5): 307–318. https://doi.org/10.1016/S0968-090X(97)82903-8.
Vermeire, F. H., and W. H. Green. 2021. “Transfer learning for solvation free energies: From quantum chemistry to experiments.” Chem. Eng. J. 418 (Aug): 129307. https://doi.org/10.1016/j.cej.2021.129307.
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2005. “Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach.” Transp. Res. Part C Emerging Technol. 13 (3): 211–234. https://doi.org/10.1016/j.trc.2005.04.007.
Wan, X., H. Liu, H. Xu, and X. Zhang. 2022. “Network traffic prediction based on LSTM and transfer learning.” IEEE Access 10 (Aug): 86181–86190. https://doi.org/10.1109/ACCESS.2022.3199372.
Wang, J., W. Deng, and Y. Guo. 2014. “New Bayesian combination method for short-term traffic flow forecasting.” Transp. Res. Part C Emerging Technol. 43 (Jun): 79–94. https://doi.org/10.1016/j.trc.2014.02.005.
Wei, Y., Y. Zheng, and Q. Yang. 2016. “Transfer knowledge between cities.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 1905–1914. New York: Association for Computing Machinery.
Weiss, K., T. M. Khoshgoftaar, and D. Wang. 2016. “A survey of transfer learning.” J. Big Data 3 (1): 1–40. https://doi.org/10.1186/s40537-016-0043-6.
Williams, B. M., and L. A. Hoel. 2003. “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results.” J. Transp. Eng. 129 (6): 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664).
Wu, S., Z. Yang, X. Zhu, and B. Yu. 2014. “Improved k-NN for short-term traffic forecasting using temporal and spatial information.” J. Transp. Eng. 140 (7): 04014026. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000672.
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.
Xu, Y., Q. J. Kong, R. Klette, and Y. Liu. 2014. “Accurate and interpretable Bayesian MARS for traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 15 (6): 2457–2469. https://doi.org/10.1109/TITS.2014.2315794.
Yang, B., S. Sun, J. Li, X. Lin, and Y. Tian. 2019. “Traffic flow prediction using LSTM with feature enhancement.” Neurocomputing 332 (Mar): 320–327. https://doi.org/10.1016/j.neucom.2018.12.016.
Yu, J., H. Wei, H. Guo, and Y. Cai. 2021. “Urban traffic state prediction based on SA-LSTM.” IOP Conf. Ser. Earth Environ. Sci. 783 (1): 012153. https://doi.org/10.1088/1755-1315/783/1/012153.
Yuan, Y., Z. Zhang, X. T. Yang, and S. Zhe. 2021. “Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation.” Transp. Res. Part B Methodol. 146 (Apr): 88–110. https://doi.org/10.1016/j.trb.2021.02.007.
Zhang, C., S. Sun, and G. Yu. 2004. “A Bayesian network approach to time series forecasting of short-term traffic flows.” In Proc., 7th Int. IEEE Conf. on Intelligent Transportation Systems, 216–221. New York: IEEE.
Zhang, C., H. Zhang, J. Qiao, D. Yuan, and M. Zhang. 2019. “Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data.” IEEE J. Sel. Areas Commun. 37 (6): 1389–1401. https://doi.org/10.1109/JSAC.2019.2904363.
Zhang, Y., and H. Ge. 2013. “Freeway travel time prediction using Takagi-Sugeno-Kang fuzzy neural network.” Comput.-Aided Civ. Infrastruct. Eng. 28 (8): 594–603. https://doi.org/10.1111/mice.12014.
Zhong, M., S. Sharma, and P. Lingras. 2004. “Genetically designed models for accurate imputation of missing traffic counts.” Transp. Res. Rec. 1879 (1): 71–79. https://doi.org/10.3141/1879-09.
Zhong, M., S. Sharma, and Z. Liu. 2005. “Assessing robustness of imputation models based on data from different jurisdictions examples of Alberta and Saskatchewan, Canada.” Transp. Res. Rec. 1917 (1): 116–126. https://doi.org/10.1177/0361198105191700114.
Zou, Y., X. Zhu, Y. Zhang, and X. Zeng. 2014. “A space-time diurnal method for short-term freeway travel time prediction.” Transp. Res. Part C Emerging Technol. 43 (Jun): 33–49. https://doi.org/10.1016/j.trc.2013.10.007.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 31, 2022
Accepted: Jun 5, 2023
Published online: Jul 18, 2023
Published in print: Oct 1, 2023
Discussion open until: Dec 18, 2023
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.