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Traffic Flow Correlation Analysis of K Intersections Based on Deep Learning

  • Hung-Chi Chu
  • Chi-Kun Wang
  • Yi-Xiang Liao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 110)

Abstract

An Intelligent transportation system is one of the indispensable systems of smart cities. The most important goal of an intelligent transportation system is to effectively reduce traffic congestion. This paper presents an analysis of traffic congestion based on traffic flows. According to the result of this analysis, the intersection correlation in a specific area can be deduced. This analysis method more effectively than the traditional method finds the relationship between the intersections according to traffic information, so through deep neural network classify intersection congestion levels, the accuracy rate is higher than 96.7%.

Keywords

Intelligent transportation system Deep neural network 

Notes

Acknowledgement

This research was supported in part by the Ministry of Science and Technology, Taiwan, ROC, under grant MOST 105-2221-E-324-009-MY2 and MOST 107-2221-E-324-003-MY2.

References

  1. 1.
    Guerrero-ibanez, J.A., Zeadally, S., Contreras-Castillo, J.: Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and Internet of Things technologies. IEEE Wirel. Commun. 22(6), 122–128 (2015)CrossRefGoogle Scholar
  2. 2.
    Centenaro, M., Vangelista, L., Zanella, A., Zorzi, M.: Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios. IEEE Wirel. Commun. 23(5), 60–67 (2016)CrossRefGoogle Scholar
  3. 3.
    Smart cities USA Homepage. http://smartamerica.org/teams/smart-cities-usa/. Accessed 30 June 2018
  4. 4.
    Festag, A.: Cooperative intelligent transport systems standards in Europe. IEEE Commun. Mag. 52(12), 166–172 (2014)CrossRefGoogle Scholar
  5. 5.
    Taipei City Government’s Public Database Homepage. http://data.taipei/. Accessed 15 Feb 2018
  6. 6.
    LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  7. 7.
    Cheng, Y., Wang, D., Zhou, P., Zhang, T.: Model compression and acceleration for deep neural networks: the principles, progress, and challenges. IEEE Signal Process. Mag. 35(1), 126–136 (2018)CrossRefGoogle Scholar
  8. 8.
    Chen, J., Liu, Z., Wang, H., Núñez, A., Han, Z.: Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans. Instrum. Meas. 67(2), 257–269 (2018)CrossRefGoogle Scholar
  9. 9.
    Maggiori, E., Charpiat, G., Tarabalka, Y., Alliez, P.: Recurrent neural networks to correct satellite image classification maps. IEEE Trans. Geosci. Remote Sens. 55(9), 4962–4971 (2017)CrossRefGoogle Scholar
  10. 10.
    Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B.: Learning contextual dependence with convolutional hierarchical recurrent neural networks. IEEE Trans. Image Process. 25(7), 2983–2996 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., Paisley, J.: Hyperspectral image classification with Markov random fields and a convolutional neural network. IEEE Trans. Image Process. 27(5), 2354–2367 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Abdelaziz, A.H.: Comparing fusion models for DNN-based audiovisual continuous speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 26(3), 475–484 (2018)CrossRefGoogle Scholar
  13. 13.
    Xu, L., Jiang, C., Ren, Y., Chen, H.H.: Microblog dimensionality reduction—a deep learning approach. IEEE Trans. Knowl. Data Eng. 28(7), 1779–1789 (2016)CrossRefGoogle Scholar
  14. 14.
    Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)CrossRefGoogle Scholar
  15. 15.
    Keras Homepage. https://github.com/fchollet/keras. Accessed 20 Feb 2018
  16. 16.
    Bernaś, M., Płaczek, B., Porwik, P., Pamuła, T.: Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction. IET Intel. Transp. Syst. 9(3), 264–274 (2015)CrossRefGoogle Scholar
  17. 17.
    Guilbert, D., Ieng, S.S., Bastard, C.L., Wang, Y.: Robust blind deconvolution process for vehicle reidentification by an inductive loop detector. IEEE Sens. J. 14(12), 4315–4322 (2014)CrossRefGoogle Scholar
  18. 18.
    Jeng, S.L., Chieng, W.H., Lu, H.P.: Estimating speed using a side-looking single-radar vehicle detector. IEEE Trans. Intell. Transp. Syst. 15(2), 607–614 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chaoyang University of TechnologyTaichungTaiwan

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