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Real-Time Analysis and Prediction System for Rail Transit Passenger Flow Based on Deep Learning

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Data Science (ICPCSEE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1880))

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Abstract

With the rapid development of urban rail transit, rail transit plays an important role in alleviating city congestion. In recent years, with increasing passenger flow, there has been huge pressure on passenger flow management. To address this problem, we propose a novel system to provide real-time statistics and predictions of passenger flow based on big data technology and deep learning technology. Moreover, the passenger flow is visualized efficiently in this system. It can provide refined passenger flow information so that people can make more rational decisions in terms of operation and planning, deploy contingency plans to avoid emergency situations, and integrate passenger flow analysis with train production, scheduling and operation to achieve cost reduction and efficiency enhancement.

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References

  1. China Association of Metros: Development outline of smart urban rail in China’s urban rail. China Metros. 8, 8–23 (2020)

    Google Scholar 

  2. Nanjing Institute of City & Transport Planning Company Limited and Hangzhou City Planning and Design Academy: Hangzhou comprehensive transportation special planning (2021)

    Google Scholar 

  3. Liu, Y., Wang, T., Ding, H., Wu, Z.: Research and application on risk assessment DEA model of crowd crushing and trampling accidents in subway stations. In: Procedia Engineering, pp. 494–498. Elsevier Ltd. (2012). https://doi.org/10.1016/j.proeng.2012.08.085

  4. Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: distributed data-parallel programs from sequential building blocks. In: Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, pp. 59–72 (2007)

    Google Scholar 

  5. Ghazi, M.R., Gangodkar, D.: Hadoop, mapreduce and HDFS: A developers perspective. In: Procedia Computer Science, pp. 45–50. Elsevier B.V (2015). https://doi.org/10.1016/j.procs.2015.04.108

  6. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: others: spark: cluster computing with working sets. HotCloud. 10, 95 (2010)

    Google Scholar 

  7. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)

    Article  Google Scholar 

  8. Graves, A., Graves, A.: Long short-term memory. Supervised sequence labelling with recurrent neural networks, pp. 37–45 (2012)

    Google Scholar 

  9. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)

  10. Chen, C., Liu, G.: Research on the calculation method of section passenger flow volume of urban rail transit. Technol. Econ. Areas Commun. 17, 43–46 (2015)

    Google Scholar 

  11. Bellman, R.: On a routing problem. Q. Appl. Math. 16, 87–90 (1958)

    Article  MATH  Google Scholar 

  12. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27 (2014)

    Google Scholar 

  13. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  14. Yu, H.-F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. Adv. Neural Inf. Process. Syst. 29 (2016)

    Google Scholar 

  15. Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858 (2019)

    Article  Google Scholar 

  16. Kreps, J., Narkhede, N., Rao, J., others: Kafka: a distributed messaging system for log processing. In: Proceedings of the NetDB, pp. 1–7 (2011)

    Google Scholar 

  17. Armbrust, M., et al.: Spark SQL: relational data processing in spark. In: Proceedings of the 2015 ACM Sigmod International Conference on Management of Data, pp. 1383–1394 (2015)

    Google Scholar 

  18. Armbrust, M., et al.: Structured streaming: a declarative API for real-time applications in apache spark. In: Proceedings of the 2018 International Conference on Management of Data, pp. 601–613 (2018)

    Google Scholar 

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Acknowledgements

This work is supported in part by grants of Zhejiang Xinmiao Talents Program under No. 2021R415025 and the Innovation and Entrepreneurship Training Program for Chinese College Students under No. 202111057017.

Author information

Authors and Affiliations

  1. School of Science, Zhejiang University of Science and Technology, Hangzhou, 310000, China

    Xujun Che & Shuhui Wu

  2. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310000, China

    Gang Cen, Jiaming Gu & Keying Zhu

Authors
  1. Xujun Che
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  2. Gang Cen
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  3. Shuhui Wu
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  4. Jiaming Gu
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  5. Keying Zhu
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Corresponding author

Correspondence to Shuhui Wu .

Editor information

Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Hongzhi Wang

  4. Northwestern Polytechnical University, Xi'an, China

    Bin Guo

  5. Shiga University, Shiga, Japan

    Xiaokang Zhou

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  7. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Che, X., Cen, G., Wu, S., Gu, J., Zhu, K. (2023). Real-Time Analysis and Prediction System for Rail Transit Passenger Flow Based on Deep Learning. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1880. Springer, Singapore. https://doi.org/10.1007/978-981-99-5971-6_9

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  • DOI: https://doi.org/10.1007/978-981-99-5971-6_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5970-9

  • Online ISBN: 978-981-99-5971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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