Using Congestion to Improve Short-Term Velocity Forecasting with Machine Learning Models

References

• Bickel, P. J., C. Chen, J. Kwon, J. Rice, E. van Zwet, and P. Varaiya. 2007. Measuring traffic. Statistical Science 22 (4):581–97. doi:10.1214/07-STS238.
   Web of Science ®Google Scholar
• Boukerche, A., and J. Wang. 2020. Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks 181:107530. doi:10.1016/j.comnet.2020.107530.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• 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. Transportation Research Part C: Emerging Technologies 62:21–34. doi:10.1016/j.trc.2015.11.002.
   Web of Science ®Google Scholar
• Cho, K., B. Merrienboer, D. Bahdanau, and Y. Bengio. 2014. On the properties of neural machine translation: Encoder–decoder approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, 103–111, Doha, Qatar.
   Google Scholar
• Cui, Z., K. Henrickson, R. Ke, and Y. Wang. 2020. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems 21 (11):4883–94. doi:10.1109/TITS.2019.2950416.
   Web of Science ®Google Scholar
• Daganzo, C. F. 1997. Fundamentals of transportation and traffic operations. Oxford, UK: Pergamon.
   Google Scholar
• Elman, J. L. 1990. Finding structure in time. Cognitive Science 14 (2):179–211. doi:10.1207/s15516709cog1402_1.
   Web of Science ®Google Scholar
• Ermagun, A., and D. Levinson. 2018. Spatiotemporal traffic forecasting: Review and proposed directions. Transport Reviews 38 (6):786–814. doi:10.1080/01441647.2018.1442887.
   Web of Science ®Google Scholar
• Géron, A. 2019. Hands-on machine learning with Scikit-Learn, Keras and TensorFlow. 2nd ed. CA: O’Reilly.
   Google Scholar
• 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. Transportation Research Part C: Emerging Technologies 106:1–16. doi:10.1016/j.trc.2019.07.003.
   Web of Science ®Google Scholar
• Haghighat, A. K., V. R. Mouli, P. Chakraborty, Y. Esfandiari, S. Arabi, and A. Sharma. 2020. Applications of deep learning in intelligent transportation systems. Journal of Big Data Analytics in Transportation 2 (2):115–45. doi:10.1007/s42421-020-00020-1.
   Google Scholar
• Hamner, B. 2010. Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow. In 2010 IEEE International Conference on Data Mining Workshops, 1357–9.
   Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Jia, Y., J. Wu, and Y. Du. 2016. Traffic speed prediction using deep learning method. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1217–22.
   Google Scholar
• Karlaftis, M., and E. Vlahogianni. 2011. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies 19 (3):387–99. doi:10.1016/j.trc.2010.10.004.
   Web of Science ®Google Scholar
• Kerner, B. S. 2009. Introduction to modern traffic flow theory and control: The long road to three-phase traffic theory. Berlin, Heidenberg: Springer.
   Google Scholar
• Kerner, B. S., and H. Rehborn. 1996a. Experimental characteristics of traffic flow for evaluation of traffic modelling. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 53 (2):R1297–R1300. doi:10.1103/physreve.53.r1297.
   PubMed Web of Science ®Google Scholar
• Kerner, B. S., and H. Rehborn. 1996b. Experimental properties of complexity in traffic flow. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 53 (5):R4275–R4278. doi:10.1103/physreve.53.r4275.
   PubMed Web of Science ®Google Scholar
• 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. Transportation Research Part C: Emerging Technologies 54:187–97. doi:10.1016/j.trc.2015.03.014.
   Web of Science ®Google Scholar
• Mihaita, A. S., L. Haowen, H. Zongyang, and M.-A. Rizoiu. 2019. Motorway traffic flow prediction using advanced deep learning. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 1683–1690.
   Google Scholar
• Polson, N. G., and V. O. Sokolov. 2017. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies 79:1–17. doi:10.1016/j.trc.2017.02.024.
   Web of Science ®Google Scholar
• Prasad, K., and S. Ramakrishna. 2014. An efficient traffic forecasting system based on spatial data and decision trees. International Arab Journal of Information Technology 11:186–94.
   Web of Science ®Google Scholar
• Rzeszótko, J., and N. Hoa. 2012. Machine learning for traffic prediction. Fundamenta Informaticae 119 (3–4):407–20. doi:10.3233/FI-2012-745.
   Web of Science ®Google Scholar
• Transportation Research Board. 2010. Highway capacity manual. 6th ed. Washington, DC: National Research Council.
   Google Scholar
• Vlahogianni, E. I. 2009. Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. Journal of Intelligent Transportation Systems 13 (2):73–84. doi:10.1080/15472450902858384.
   Web of Science ®Google Scholar
• Yi, H., K.-H N. Bui, and H. Jung. 2019. Implementing a deep learning framework for short term traffic flow prediction. In Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics, WIMS2019. Association for Computing Machinery, doi:10.1145/3326467.3326492.
   Google Scholar
• Zou, Y., X. Zhu, Y. Zhang, and X. Zeng. 2014. A space–time diurnal method for short-term freeway travel time prediction. Transportation Research Part C: Emerging Technologies 43:33–49. Special Issue on Short-term Traffic Flow Forecasting. doi:10.1016/j.trc.2013.10.007.
   Web of Science ®Google Scholar