Freeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 12
Abstract
In the literature, machine-learning techniques have been extensively implemented to capture the stochastic characteristics of freeway traffic speed. The deployment of intelligent transportation systems (ITSs) in recent decades offers much enriched and a wider range of traffic data, which makes it possible to adopt a variety of machine-learning methods to estimate traffic speed. However, an understanding of what type of machine-learning models to select for such applications and how to use probe vehicle data to estimate traffic conditions are still lacking. To fill this research gap, this study aims to utilize regression machine-learning algorithms to estimate traffic speed using probe vehicle and sensor detector data; also, the performance of the utilized machine-learning algorithms is compared using a novel traffic speed estimation framework. The results show that the proposed framework can effectively capture time-varying traffic patterns and has a superior ability to accurately estimate traffic speed in a timely manner. Using sensor detector data as the benchmark, the comparison results show that a random forest achieves the best performance in terms of traffic speed estimation.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article. The utilized data set and the programming code of the proposed models are available here: https://github.com/ZhaoZhangUU/Freeway-traffic-speed-estimation.
Acknowledgments
This research is supported by Project NITC 1298 and MPC-610, funded respectively by the National Institute for Transportation and the Communities and Mountain-Plains Consortium.
References
Asif, M. T., J. Dauwels, C. Y. Goh, A. Oran, E. Fathi, M. Xu, M. M. Dhanya, N. Mitrovic, and P. Jaillet. 2014. “Spatiotemporal patterns in large-scale traffic speed prediction.” IEEE Trans. Intell. Transp. Syst. 15 (2): 794–804. https://doi.org/10.1109/TITS.2013.2290285.
Bekiaris-Liberis, N., C. Roncoli, and M. Papageorgiou. 2016. “Highway traffic state estimation with mixed connected and conventional vehicles.” IEEE Trans. Intell. Transp. Syst. 17 (12): 3484–3497. https://doi.org/10.1109/TITS.2016.2552639.
Bekiaris-Liberis, N., C. Roncoli, and M. Papageorgiou. 2017. “Highway traffic state estimation per lane in the presence of connected vehicles.” Transp. Res. Part B. Methodol. 106 (Dec): 1–28. https://doi.org/10.1016/j.trb.2017.11.001.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD’16, 785–794. New York: Association for Computing Machinery.
Cutler, A., D. R. Cutler, and J. R. Stevens. 2012. “Random forests.” In Ensemble machine learning, 157–175. New York: Springer.
Ding, C., D. Wang, X. Ma, and H. Li. 2016. “Predicting short-term subway ridership and prioritizing its influential factors using gradient boosting decision trees.” Sustainability 8 (11): 1100. https://doi.org/10.3390/su8111100.
Du, S., T. Li, X. Gong, Z. Yu, and S.-J. Horng. 2018. “A hybrid method for traffic flow forecasting using multimodal deep learning.” Preprint, submitted March 6, 2018. https://arxiv.org/abs/1803.02099.
Fountoulakis, M., N. Bekiaris-Liberis, C. Roncoli, I. Papamichail, and M. Papageorgiou. 2017. “Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing.” Transp. Res. Part C: Emerging Technol. 78 (May): 13–33. https://doi.org/10.1016/j.trc.2017.02.015.
Friedman, J. H. 2002. “Stochastic gradient boosting.” Comput. Stat. Data Anal. 38 (4): 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2.
Hamner, B. 2010. “Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow.” In Proc., 2010 IEEE Int. Conf. on Data Mining Workshops, 1357–1359. New York: IEEE.
iPeMS. 2019. “Utah iPeMS.” Accessed March 28, 2019. https://udot3p.iteris-pems.com/.
Karlaftis, M. G., and E. I. Vlahogianni. 2011. “Statistical methods versus neural networks in transportation research: Differences, similarities and some insights.” Transp. Res. Part C: Emerging Technol. 19 (3): 387–399. https://doi.org/10.1016/j.trc.2010.10.004.
Kotsialos, A., and M. Papageorgiou. 2001. “The importance of traffic flow modeling for motorway traffic control.” Networks Spatial Econ. 1 (1–2): 179–203. https://doi.org/10.1023/A:1011537329508.
Kotsialos, A., M. Papageorgiou, C. Diakaki, Y. Pavlis, and F. Middelham. 2002. “Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET.” IEEE Trans. Intell. Transp. Syst. 3 (4): 282–292. https://doi.org/10.1109/TITS.2002.806804.
Leshem, G., and Y. Ritov. 2007. “Traffic flow prediction using adaboost algorithm with random forests as a weak learner.” In Proc., World Academy of Science, Engineering and Technology, 193–198. University Park, PA: CiteSeer.
Lv, Y., Y. Duan, W. Kang, Z. Li, and F. Y. Wang. 2014. “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., C. Ding, S. Luan, Y. Wang, and Y. Wang. 2017. “Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method.” IEEE Trans. Intell. Transp. Syst. 18 (9): 2303–2310. https://doi.org/10.1109/TITS.2016.2635719.
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.
Messmer, A., and M. Papageorgiou. 1990. “METANET: A macroscopic simulation program for motorway networks.” Traffic Eng. Control 31 (8–9): 466–470.
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.
Papageorgiou, M. 1983. Application of automatic control concepts to traffic flow modeling and control. New York: Springer.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in python.” J. Mach. Learn. Res. 12: 2825–2830.
Seo, T., A. M. Bayen, T. Kusakabe, and Y. Asakura. 2017. “Traffic state estimation on highway: A comprehensive survey.” Annu. Rev. Control 43: 128–151. https://doi.org/10.1016/j.arcontrol.2017.03.005.
Smola, A. J., and B. Schölkopf. 2004. “A tutorial on support vector regression.” Stat. Comput. 14 (3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
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.
Tang, J., G. Zhang, Y. Wang, H. Wang, and F. Liu. 2015. “A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation.” Transp. Res. Part C: Emerging Technol. 51 (Feb): 29–40. https://doi.org/10.1016/j.trc.2014.11.003.
Taylor, C., and D. Meldrum. 1995. “Freeway traffic data prediction using neural networks.” In Proc., 6th 1995 Vehicle Navigation and Information Systems Conf., 225–230. New York: IEEE.
UDOT (Utah Department of Transportation). 2019. “Performance measurement system.” Accessed March 28, 2019. https://udot.iteris-pems.com.
van Lint, J. W. C., S. P. Hoogendoorn, and H. J. van Zuylen. 2005. “Accurate freeway travel time prediction with state-space neural networks under missing data.” Transp. Res. Part C: Emerging Technol. 13 (5–6): 347–369. https://doi.org/10.1016/j.trc.2005.03.001.
Vapnik, V. 2013. The nature of statistical learning theory. New York: Springer.
Wang, D., Q. Zhang, S. Wu, X. Li, and R. Wang. 2016. “Traffic flow forecast with urban transport network.” In Proc., 2016 IEEE Int. Conf. on Intelligent Transportation Engineering, 139–143. New York: IEEE.
Wang, J., R. Chen, and Z. He. 2019. “Traffic speed prediction for urban transportation network: A path based deep learning approach.” Transp. Res. Part C: Emerging Technol. 100 (Mar): 372–385. https://doi.org/10.1016/j.trc.2019.02.002.
Wang, J., and Q. Shi. 2013. “Short-term traffic speed forecasting hybrid model based on Chaos-wavelet analysis-support vector machine theory.” Transp. Res. Part C: Emerging Technol. 27 (Feb): 219–232. https://doi.org/10.1016/j.trc.2012.08.004.
Wang, Y., and M. Papageorgiou. 2005. “Real-time freeway traffic state estimation based on extended Kalman filter: A general approach.” Transp. Res. Part B: Methodol. 39 (2): 141–167. https://doi.org/10.1016/j.trb.2004.03.003.
Wu, C., J. Ho, and D. T. Lee. 2004. “Travel-time prediction with support vector regression.” IEEE Trans. Intell. Transp. Syst. 5 (4): 276–281. https://doi.org/10.1109/TITS.2004.837813.
Yang, S., J. Wu, Y. Du, Y. He, and X. Chen. 2017. “Ensemble learning for short-term traffic prediction based on gradient boosting machine.” J. Sens. 2017: 1–15. https://doi.org/10.1155/2017/7074143.
Yin, W., P. Murray-Tuite, and H. Rakha. 2012. “Imputing erroneous data of single-station loop detectors for nonincident conditions: Comparison between temporal and spatial methods.” J. Intell. Transp. Syst. 16 (3): 159–176. https://doi.org/10.1080/15472450.2012.694788.
Yuan, Y., J. W. C. Van Lint, R. E. Wilson, F. Van Wageningen-Kessels, and S. P. Hoogendoorn. 2012. “Real-time Lagrangian traffic state estimator for freeways.” IEEE Trans. Intell. Transp. Syst. 13 (1): 59–70. https://doi.org/10.1109/TITS.2011.2178837.
Zeng, D., J. Xu, J. Gu, L. Liu, and G. Xu. 2008. “Short term traffic flow prediction using hybrid ARIMA and ANN models.” In Proc., 2008 Workshop on Power Electronics and Intelligent Transportation System, 621–625. New York: IEEE.
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.
Zhang, K., L. Zheng, Z. Liu, and N. Jia. 2019. “A deep learning based multitask model for network-wide traffic speed predication.” Neurocomputing 396 (Jul): 438–450. https://doi.org/10.1016/j.neucom.2018.10.097.
Zhang, Y., and A. Haghani. 2015. “A gradient boosting method to improve travel time prediction.” Transp. Res. Part C: Emerging Technol. 58 (Sep): 308–324. https://doi.org/10.1016/j.trc.2015.02.019.
Zhang, Y., and Y. Liu. 2009. “Traffic forecasting using least squares support vector machines.” Transportmetrica 5 (3): 193–213. https://doi.org/10.1080/18128600902823216.
Zhong, M., P. Lingras, and S. Sharma. 2004. “Estimation of missing trafficc counts using factor, genetic, neural, and regression techniques.” Transp. Res. Part C: Emerging Technol. 12 (2): 139–166. https://doi.org/10.1016/j.trc.2004.07.006.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Nov 21, 2019
Accepted: Jul 6, 2020
Published online: Sep 30, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 28, 2021
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