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Deep Learning and Density Based Clustering Methods for Road Traffic Prediction

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1378))

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Abstract

Road traffic prediction is a necessary requirement for traffic management. Deep neural networks can perform multiple vehicle detection leading to traffic prediction. Neural networks trained on vehicles dataset detects multiple vehicles on road. Large area occlusion and vehicles that are there at far distances have lesser probability of detection. We propose a technique for improved estimate of traffic, despite presence of occlusion and poor detection probability in the video frame based on density based clustering. Grid averaged density estimated maps representing spatial-temporal traffic data are fed to a trained convolutional LSTM network to predict the road traffic. The output predictions are chosen from a 10 min horizon. The validation when done on 30 h of traffic video yielded a mean absolute percentage error equal to 4.55.

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References

  1. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  2. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  3. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  4. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  5. Wang, X., Hua, X., Xiao, F., Li, Y., Hu, X., Sun, P.: Multi-object detection in traffic scenes based on improved SSD. Electronics 7(11), 302 (2018)

    Article  Google Scholar 

  6. Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Woo, W.-C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

  7. Toropov, E., Gui, L., Zhang, S., Kottur, S., Moura, J.M.: Traffic flow from a low frame rate city camera. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3802–3806. IEEE (2015)

    Google Scholar 

  8. Zheng, Y., Peng, S.: Model based vehicle localization for urban traffic surveillance using image gradient based matching. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 945–950. IEEE (2012)

    Google Scholar 

  9. Chen, Y.-L., Wu, B.-F., Huang, H.-Y., Fan, C.-J.: A real-time vision system for nighttime vehicle detection and traffic surveillance. IEEE Trans. Ind. Electron. 58(5), 2030–2044 (2010)

    Article  Google Scholar 

  10. Chen, Z., Ellis, T., Velastin, S.A.: Vehicle detection, tracking and classification in urban traffic. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 951–956. IEEE (2012)

    Google Scholar 

  11. Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C Emerg. Technol. 79, 1–7 (2017)

    Article  Google Scholar 

  12. Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841 (2015)

    Google Scholar 

  13. Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)

    Google Scholar 

  14. Oñoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 615–629. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_38

    Chapter  Google Scholar 

  15. Zhao, Z., Li, H., Zhao, R., Wang, X.: Crossing-line crowd counting with two-phase deep neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 712–726. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_43

    Chapter  Google Scholar 

  16. Zhang, S., Wu, G., Costeira, J.P., Moura, J.M.: Understanding traffic density from large-scale web camera data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5898–5907 (2017)

    Google Scholar 

  17. Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory. Transp. Res. Part B Methodol. 18(1), 1–11 (1984)

    Article  Google Scholar 

  18. Xie, Y., Zhang, Y., Ye, Z.: Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Comput.-Aided Civ. Infrastruct. Eng. 22(5), 326–334 (2007)

    Article  Google Scholar 

  19. Yu, G., Hu, J., Zhang, C., Zhuang, L., Song, J.: Short-term traffic flow forecasting based on Markov chain model. In: IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No. 03TH8683), pp. 208–212. IEEE (2003)

    Google Scholar 

  20. Zhang, Y., Xie, Y.: Forecasting of short-term freeway volume with v-support vector machines. Transp. Res. Rec. 2024(1), 92–99 (2007)

    Article  Google Scholar 

  21. Tan, H., Xuan, X., Wu, Y., Zhong, Z., Ran, B.: A comparison of traffic flow prediction methods based on DBN. In: CICTP 2016, pp. 273–283 (2016)

    Google Scholar 

  22. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layerwise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153–160 (2007)

    Google Scholar 

  23. Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)

    Article  Google Scholar 

  24. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328. IEEE (2016)

    Google Scholar 

  25. Wang, J., Hu, F., Li, L.: Deep bi-directional long short-term memory model for short-term traffic flow prediction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 306–316. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_31

    Chapter  Google Scholar 

  26. 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. 16(2), 865–873 (2014)

    Google Scholar 

  27. Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)

    Article  Google Scholar 

  28. Wu, Y., Tan, H.: Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework, arXiv preprint arXiv:1612.01022 (2016)

  29. Yu, H., Wu, Z., Wang, S., Wang, Y., Ma, X.: Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7), 1501 (2017)

    Article  Google Scholar 

  30. Jagadish, D.N., Mahto, L., Chauhan, A.: Density based clustering methods for road traffic estimation. In: IEEE Region 10 Conference (TENCON), pp. 885–890 (2020)

    Google Scholar 

  31. Sochor, J., Herout, A.: Unsupervised processing of vehicle appearance for automatic understanding in traffic surveillance. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)

    Google Scholar 

  32. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, no. 34, pp. 226–231 (1996)

    Google Scholar 

  33. Zapata-Impata, B.S., Gil, P., Torres, F.: Learning spatio temporal tactile features with a convlstm for the direction of slip detection. Sensors 19(3), 523 (2019)

    Article  Google Scholar 

  34. Grigorev, A.: Nevsky prospect traffic surveillance video (movement by the opposite lane cases hours). figshare, 25 December 2018. https://figshare.com/articles/Nevsky_prospect_traffic_surveillance_video_MOOL-cases_hours_/5841267/6. Accessed 25 May 2020

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Authors and Affiliations

  1. Indian Institute of Information Technology Dharwad, Dharwad, India

    D. N. Jagadish, Lakshman Mahto & Arun Chauhan

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  1. D. N. Jagadish
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  2. Lakshman Mahto
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  3. Arun Chauhan
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Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Jagadish, D.N., Mahto, L., Chauhan, A. (2021). Deep Learning and Density Based Clustering Methods for Road Traffic Prediction. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1378. Springer, Singapore. https://doi.org/10.1007/978-981-16-1103-2_29

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  • DOI: https://doi.org/10.1007/978-981-16-1103-2_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1102-5

  • Online ISBN: 978-981-16-1103-2

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