Abstract
In Intelligent Transportation Systems (ITS), it is widely used to extract a fixed-size feature vector from raw traffic data for high-level traffic analysis. In several existing works, the statistical approach has been used for extracting feature vectors, which directly extracts features by averaging speed or travel time of each vehicle. However, we can achieve a better representation by taking advantage of state-of-the-art machine learning algorithms instead of the statistical approach. In this paper, we propose a two-phase framework named embed-and-aggregate framework for extracting features from raw traffic data, and a feature extraction algorithm (Traffic2Vec) based on our framework exploiting state-of-the-art machine learning algorithms such as deep learning. We also implement a traffic flow prediction system based on Traffic2Vec as a proof-of-concept. We conducted experiments to evaluate the applicability of the proposed algorithm, and show its superior performance in comparison with the prediction system based on the statistical feature extraction method.
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References
Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Heidelberg (2013)
Gao, S., et al.: Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environ. Plann. B Plann. Des. 40(1), 135–153 (2013)
Zhang, L., Liu, Q., Yang, W., Wei, N., Dong, D.: An improved K-nearest neighbor model for short-term traffic flow prediction. Procedia Soc. Behav. Sci. 96, 653–662 (2013)
Wu, S., Yang, Z., Zhu, X., Yu., B.: Improved K-nn for short-term traffic forecasting using temporal and spatial information. J. Trans. Eng. 140(7), 04014026 (2014)
Tak, S., Kim, S., Jang, K., Yeo, H.: Real-time travel time prediction using multi-level K-nearest neighbor algorithm and data fusion method. In: Computing in Civil and Building Engineering, pp. 1861–1868 (2014)
Kim, H., Park, S.H., Jang, K.: Short-term traffic states prediction using K-nearest neighbor algorithm: focused on urban expressway. J. Korean Soc. Transp. 34(2), 158–167 (2016)
Yu, B., Song, X., Guan, F., Yang, Z., Yao, B.: k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J. Transp. Eng. 142, 04016018 (2016)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. (ITS) 16(2), 865–873 (2015)
Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C-Emerg. Technol. (Transport. Res. C-Emerg.) 79, 1–17 (2017)
Kumar, K., Parida, M., Katiyar, V.K.: Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport 30(4), 397–405 (2015)
Jeong, Y.-S., Byon, Y.-J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. (ITS) 14(4), 1700–1707 (2013)
Vasantha Kumar, S., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. (ETRR) 7, 1–9 (2015)
Konga, X., Xua, Z., Shenb, G., Wanga, J., Yanga, Q., Zhanga, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener. Comput. Syst. (FGCS) 61, 97–107 (2016)
Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16, 653–662 (2015)
Fu, Z., Hu, W., Tan, T.: Similarity based vehicle trajectory clustering and anomaly detection. In: 2005 IEEE International Conference on Image Processing, ICIP 2005 (2005)
Zhang, Z., He, Q., Tong, H., Gou, J., Li, X.: Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network. Transp. Res. Part C Emerg. Technol. 71, 284–302 (2016)
Bengio, Y., Simard, P.Y., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Yao, D., Zhang, C., Zhu, Z., Huang, J., Bi, J.: Trajectory clustering via deep representation learning. In: International Joint Conference on Neural Networks (IJCNN), (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 3104–3112 (2014)
Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: CVPR (2007)
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311. IEEE (2010)
Jegou, H., et al.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)
Arandjelovic, R., Zisserman, A.: All about VLAD. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1578–1585. IEEE, Piscataway (2013)
Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local images descriptors into compact codes. IEEE PAMI 34, 1704–1716 (2012)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the ICCV (2003)
KDD Cup 2017: Highway Tollgates Traffic Flow Prediction. http://www.kdd.org/kdd2017/announcements/view/announcing-kdd-cup-2017-highway-tollgates-traffic-flow-prediction
KDD CUP 2017. https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100068.5678.1.LTxGCd&raceId=231597
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2016R1A2B1014013).”
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Choi, W., Min, J., Lee, T., Hyun, K., Lim, T., Jung, S. (2018). Deep Representation of Raw Traffic Data: An Embed-and-Aggregate Framework for High-Level Traffic Analysis. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_220
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DOI: https://doi.org/10.1007/978-981-10-7605-3_220
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