Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 5
Abstract
For both travelers and traffic operation centers, especially under extremely large traffic volumes, full-coverage traffic state monitoring of a major corridor is urgently needed. In the present paper, a traffic speed estimation method is proposed using a big data and deep learning approach under extreme traffic conditions. Particularly, a geospatial mapping method is proposed in this paper. This method ensures the scalability and easy-deployment, extracts phone speed (PSP) and phone count (PC) from raw cellular data, and estimates the traffic speed using a deep long short-term memory (DLSTM) neural network. The proposed method is used to estimate traffic speed for a major expressway in China that is installed with limited roadside equipment. The field test, which gives a promising performance, was performed during the Golden Week, the Chinese national holiday in 2014 (00:00 October 1 to 23:59 October 7) on the nearly 250-km-long busy freeway, G42, for both directions. The results suggest that the proposed cellular-based system can be an alternative and supplement solution for monitoring various practical traffic states, especially when only limited conventional roadside equipment is installed.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The Titan XP used for this paper is donated by the NVIDIA Corporation. Furthermore, the deep learning framework involved in this paper is Deeplearning4j, an open-source, distributed deep learning project spearheaded by the people at Skymind.
References
Astarita, V., R. L. Bertini, S. d’Elia, and G. Guido. 2006. “Motorway traffic parameter estimation from mobile phone counts.” Eur. J. Oper. Res. 175 (3): 1435–1446. https://doi.org/10.1016/j.ejor.2005.02.020.
Bar-Gera, H. 2007. “Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel.” Transp. Res. Part C Emerging Technol. 15 (6): 380–391. https://doi.org/10.1016/j.trc.2007.06.003.
Caceres, N., J. Wideberg, and F. G. Benitez. 2008. “Review of traffic data estimations extracted from cellular networks.” IET Intell. Transp. Syst. 2 (3): 179. https://doi.org/10.1049/iet-its:20080003.
Calabrese, F., M. Diao, G. Di Lorenzo, J. Ferreira, and C. Ratti. 2013. “Understanding individual mobility patterns from urban sensing data: A mobile phone trace example.” Transp. Res. Part C Emerging Technol. 26: 301–313. https://doi.org/10.1016/j.trc.2012.09.009.
Cheng, P., Z. Qiu, and B. Ran. 2006. “Particle filter based traffic state estimation using cell phone network data.” In Proc., 2006 IEEE Intelligent Transportation Systems Conf., 1047–1052. Piscataway, NJ: IEEE.
Cleveland, R. B., W. S. Cleveland, J. E. McRae, and I. Terpenning. 1990. “STL: A seasonal-trend decomposition procedure based on loess.” J. Off. Stat. 6 (1): 3–33.
Demissie, M. G., G. H. de Almeida Correia, and C. Bento. 2013. “Intelligent road traffic status detection system through cellular networks handover information: An exploratory study.” Transp. Res. Part C Emerging Technol. 32: 76–88. https://doi.org/10.1016/j.trc.2013.03.010.
Domhan, T., J. T. Springenberg, and F. Hutter. 2015. “Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves.” In Proc., IJCAI Int. Joint Conf. on Artificial Intelligence, 3460–3468. Palo Alto, CA: AAAI.
Dong, H., M. Wu, X. Ding, L. Chu, L. Jia, Y. Qin, and X. Zhou. 2015. “Traffic zone division based on big data from mobile phone base stations.” Transp. Res. Part C Emerging Technol. 58: 278–291. https://doi.org/10.1016/j.trc.2015.06.007.
Fusco, G., and S. Gori. 1995. “The use of artificial neural networks in advanced traveler information and traffic management systems.” In Applications of advanced technologies in transportation engineering, 341–345. Reston, VA: ASCE.
Gao, H., and F. Liu. 2013. “Estimating freeway traffic measures from mobile phone location data.” Eur. J. Oper. Res. 229 (1): 252–260. https://doi.org/10.1016/j.ejor.2013.02.044.
González, M. C., C. A. Hidalgo, and A.-L. Barabási. 2008. “Understanding individual human mobility patterns.” Nature 453 (7196): 779–782. https://doi.org/10.1038/nature06958.
Gundlegård, D., and J. M. Karlsson. 2009. “Handover location accuracy for travel time estimation in GSM and UMTS.” IET Intell. Transp. Syst. 3 (1): 87. https://doi.org/10.1049/iet-its:20070067.
Han, J., and C. Moraga. 1995. “The influence of the sigmoid function parameters on the speed of backpropagation learning.” In Proc., Int. Workshop on Artificial Neural Networks, 195–201. Dordrecht, Netherlands: Springer.
Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A K-Means clustering algorithm.” Appl. Stat. 28 (1): 100. https://doi.org/10.2307/2346830.
He, S., Y. Cheng, G. Zhong, and B. Ran. 2017. “A data-driven study on the sample size of cellular handoff probe system.” Adv. Mech. Eng. 9 (4): 168781401769844. https://doi.org/10.1177/1687814017698442.
He, S., J. Zhang, Y. Cheng, X. Wan, and B. Ran. 2016. “Freeway multisensor data fusion approach integrating data from cellphone probes and fixed sensors.” J. Sensors 2016: 1. https://doi.org/10.1155/2016/7269382.
Herrera, J. C., D. B. Work, R. Herring, X. J. Ban, Q. Jacobson, and A. M. Bayen. 2010. “Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment.” Transp. Res. Part C Emerging Technol. 18 (4): 568–583. https://doi.org/10.1016/j.trc.2009.10.006.
Hochreiter, S., Y. Bengio, P. Frasconi, and J. Schmidhuber. 2009. “Gradient flow in recurrent nets: The difficulty of learning longterm dependencies.” In A field guide to dynamical recurrent networks. New York: IEEE.
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.
Huang, W., G. Song, H. Hong, and K. Xie. 2014. “Deep architecture for traffic flow prediction: Deep belief networks with multitask learning.” IEEE Trans. Intell. Transp. Syst. 15 (5): 2191–2201. https://doi.org/10.1109/TITS.2014.2311123.
Ioffe, S., and C. Szegedy. 2015. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” Preprint, submitted. February 11, 2015. http://arXiv:1502.03167.
Janecek, A., D. Valerio, K. A. Hummel, F. Ricciato, and F. Hlavacs. 2012. “Cellular data meet vehicular traffic theory: Location area updates and cell transitions for travel time estimation.” In Proc., 2012 ACM Conf. on Ubiquitous Computing ACM, 361–370. New York: ACM.
Kanhere, S. S. 2013. “Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces.” In Proc., Int. Conf.; 9th, Distributed Computing and Internet Technology; ICDCIT 2013, 19–26. New York: IEEE.
Leduc, G. 2008. “Road traffic data: Collection methods and applications.” In Vol. 1 of Proc., Working Papers on Energy, Transport and Climate Change. Brussels, Belgium: Joint Research Centre.
Li, Q., Y. Cheng, F. Ding, X. Wan, and B. Ran. 2016. “Citywide hourly traffic emissions estimation using cellular activity data.” In Proc., Transportation Research Board 95th Annual Meeting, 15. Washington, DC: Transportation Research Board.
Li, X., J. Cui, S. An, and M. Parsafard. 2014. “Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation.” Transp. Res. Part B Methodol. 70: 319–339. https://doi.org/10.1016/j.trb.2014.09.014.
Liu, H. X., A. Danczyk, R. Brewer, and R. Starr. 2008. “Evaluation of cell phone traffic data in Minnesota.” Transp. Res. Rec. 2086 (1): 1–7. https://doi.org/10.3141/2086-01.
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. 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.
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: 187–197. https://doi.org/10.1016/j.trc.2015.03.014.
NHTSA (National Highway Traffic Safety Administration). 2016. Speed-measuring device performance specifications: Radar module. Washington, DC: NHTSA.
Polson, N. G., and V. O. Sokolov. 2017. “Deep learning for short-term traffic flow prediction.” Transp. Res. Part C Emerging Technol. 79: 1–17. https://doi.org/10.1016/j.trc.2017.02.024.
Qiu, Z., and P. Cheng. 2007. “State of the art and practice: Cellular probe technology applied in advanced traveler information system.” In Proc., 86th Annual Meeting on Transportation Research. Washington, DC: Transportation Research Board.
Rose, G. 2006. “Mobile phones as traffic probes: Practices, prospects and issues.” Transp. Rev. 26 (3): 275–291. https://doi.org/10.1080/01441640500361108.
Sak, H., A. Senior, and F. Beaufays. 2014. “Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition.” Preprint, submitted February 5, 2014. http://arxiv.org/abs/1402.1128.
Sanwal, K. K., and J. Walrand. 1995. Vehicles as probes. Berkeley, CA: Univ. of California, Berkeley.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Networks 61 (Jan): 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Silver, D., et al. 2017. “Mastering the game of Go without human knowledge.” Nature 550 (7676): 354–359. https://doi.org/10.1038/nature24270.
Snoek, J., H. Larochelle, and R. P. Adams. 2012. “Practical Bayesian optimization of machine learning algorithms.” In Proc., 25th Int. Conf. on Neural Information Processing Systems (NIPS’12), 2951–2959. Red Hook, NY: Curran Associates, Inc.
Tieleman, T., and G. Hinton. 2012. “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.” COURSERA Neural networks Mach. Learn. 4: 26–31.
Wiering, M. A. 2000. “Multi-agent reinforcement learning for traffic light control.” In Proc., Seventeenth Int. Conf. on Machine Learning (ICML’2000), 1151–1158. Burlington, MA: Morgan Kaufmann Publishers Inc.
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: 166–180. https://doi.org/10.1016/j.trc.2018.03.001.
Yang, F., Y. Cheng, J. Jin, J. Xia, D. Yang, and B. Ran. 2012. “Wireless communication simulation model for traffic monitoring systems based on dynamic cellular handoffs: Framework and evaluation.” Transp. Res. Rec. 2291 (1): 26–34. https://doi.org/10.3141/2291-04.
Ygnace, J., C. Drane, Y. B. Yim, and R. De Lacvivier. 2000. Travel time estimation on the San Francisco Bay Area network using cellular phones as probes. Berkeley, CA: Univ. of California, Berkeley.
Yu, R., Y. Li, C. Shahabi, U. Demiryurek, and Y. Liu. 2017. “Deep learning: A generic approach for extreme condition traffic forecasting.” In Proc., 2017 SIAM Int. Conf. on Data Mining, 777–785. Philadelphia: Society for Industrial and Applied Mathematics.
Zhang, J., F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen. 2011. “Data-driven intelligent transportation systems: A survey.” IEEE Trans. Intell. Transp. Syst. 12 (4): 1624–1639. https://doi.org/10.1109/TITS.2011.2158001.
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 Intell. Transp. Syst. 11 (2): 68–75. https://doi.org/10.1049/iet-its.2016.0208.
Zhong, G., X. Wan, J. Zhang, T. Yin, and B. Ran. 2017. “Characterizing passenger flow for a transportation hub based on mobile phone data.” IEEE Trans. Intell. Transp. Syst. 18 (6): 1109–1121. https://10.1109/TITS.2016.2607760.
Zhou, M., X. Qu, and X. Li. 2017. “A recurrent neural network based microscopic car following model to predict traffic oscillation.” Transp. Res. Part C 84: 245–264. https://doi.org/10.1016/j.trc.2017.08.027.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 145 • Issue 5 • May 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Feb 13, 2018
Accepted: Oct 9, 2018
Published online: Feb 20, 2019
Published in print: May 1, 2019
Discussion open until: Jul 20, 2019
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.