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Social Networks and Railway Passenger Capacity: An Empirical Study Based on Text Mining and Deep Learning

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Published:06 November 2018Publication History

ABSTRACT

Railway passenger transport is essential to modern transportation in China. The prediction of railway passenger capacity is of vital importance for ensuring the safety of railway transportation. This paper introduces social network text data into the prediction of railway passenger capacity. In the process of analyzing social network text data, text mining methods are used to analyze the text data, and the information related to railway passenger flow is extracted from the text and added to the prediction model. Meanwhile, in order to obtain better prediction results, this paper applies deep learning method on the data. The combination of text mining and deep learning method has greatly improved the accuracy of our prediction model. Experimental results show that a good accuracy rate has been achieved.

References

  1. Cox, T., Houdmont, J., & Griffiths, A. (2006). Rail passenger crowding, stress, health and safety in Britain. Transportation Research Part A: Policy and Practice, 40(3), 244--258.Google ScholarGoogle ScholarCross RefCross Ref
  2. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458--467.Google ScholarGoogle Scholar
  3. Liu, Y., Zhou, B., Feng, C., & Pu, S. (2012). Application of comprehensive evaluation method integrated by Delphi and GAHP in optimal siting of electric vehicle charging station. 2012 International Conference on In Control Engineering and Communication Technology (ICCECT), pp. 88--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Sun, Y., Leng, B., & Guan, W. (2015). A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 166, 109--121. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Zhuo, W., Li-Min, J., Yong, Q., & Yan-Hui, W. (2007). Railway passenger traffic volume prediction based on neural network. Applied Artificial Intelligence, 21(1), 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Tsai, T. H., Lee, C. K., & Wei, C. H. (2009). Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Systems with Applications, 36(2), 3728--3736. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Qi, F., Liu, X., & Ma, Y. (2009). Prediction of railway passenger traffic volume based on neural tree model. 2009 ICICTA'09. Second International Conference on Intelligent Computation Technology and Automation. Vol. 1, pp. 370--373. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Deng, W., Li, W., & Yang, X. H. (2011). A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. Expert Systems with Applications, 38(4), 4198--4205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wang, Y., Xu, W., & Jiang, H. (2015). Using text mining and clustering to group research proposals for research project selection. In 2015 48th Hawaii International Conference on System Sciences (HICSS). pp. 1256--1263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Wang, Y., & Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87--95.Google ScholarGoogle ScholarCross RefCross Ref
  11. Wang, Y., Wang, M., & Xu, W. (2018). A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework. Wireless Communications and Mobile Computing, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533.Google ScholarGoogle Scholar
  13. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.Google ScholarGoogle Scholar
  14. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85--117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th international conference on machine learning. pp. 689--696. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527--1554. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Social Networks and Railway Passenger Capacity: An Empirical Study Based on Text Mining and Deep Learning

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    • Published in

      cover image ACM Conferences
      Safety and Resilience'18: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Safety and Resilience
      November 2018
      129 pages
      ISBN:9781450360449
      DOI:10.1145/3284103

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 November 2018

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      Acceptance Rates

      Safety and Resilience'18 Paper Acceptance Rate22of38submissions,58%Overall Acceptance Rate22of38submissions,58%

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