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Prediction of highway passenger transportation in Beijing based on BP neural network

Published:17 April 2024Publication History

ABSTRACT

The purpose of this paper is to explore the method and effect of using BP neural network to forecast the passenger transportation volume on highway. A prediction model based on BP neural network is constructed by analyzing and processing the historical data of highway transportation and related influencing factors in Beijing from 2000 to 2020, and its prediction results are analyzed and compared. The experimental results show that the model can effectively predict the road passenger traffic with high accuracy and generalization ability, which can provide a good reference for the prediction of road passenger traffic in other cities.

References

  1. Jin G, Feng W, Meng Q. 2022. Prediction of waterway cargo transportation volume to support maritime transportation systems based on GA-BP neural network optimization [J]. Sustainability, 14(21). Doi:10.3390/SU142113872.Google ScholarGoogle Scholar
  2. Kolidakis S, Botzoris G, Profillidis V, 2019. Road traffic forecasting—A hybrid approach combining artificial neural network with singular spectrum analysis[J]. Economic analysis and policy, 64: 159-171. Doi: 10.1016/j.eap.2019.08.002.Google ScholarGoogle Scholar
  3. Hu R, Chiu Y, Hsieh C, 2019. Mass rapid transit system passenger traffic forecast using a Re-Sample recurrent neural network[J]. Journal of Advanced Transportation, 2019. Doi: 10.1155/2019/8943291.Google ScholarGoogle ScholarCross RefCross Ref
  4. Yang F, Jia J, Liu Y 2023. Research on railroad passenger traffic prediction based on Newton interpolation and super relaxation technique[J]. Railway Transportation and Economy, 2023, 45(03):44-52. DOI: 10.16668/j.cnki.issn.1003-1421.2023.03.07.Google ScholarGoogle ScholarCross RefCross Ref
  5. Wang Y, Chen X, Han Y, 2013. Forecast of passenger and freight traffic volume based on elasticity coefficient method and grey model[J]. Procedia-Social and Behavioral Sciences, 96:136-147. DOI: 10.1016/j.sbspro.2013.08.019.Google ScholarGoogle ScholarCross RefCross Ref
  6. Jiao Z, Ming F, Shi L. 2019. Analysis of highway passenger traffic forecast under the influence of high-speed rail[J]. Automobile practical technology, (11):246-248. DOI: 10.16638/j.cnki.1671-7988.2019.11.082.Google ScholarGoogle ScholarCross RefCross Ref
  7. Tang L, Mo Y. 2017. Research on highway passenger traffic prediction based on BP neural network and multiple regression[J]. Transportation Science and Technology, (05):123-126. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=SKQB201705036&DbName=CJFQ2017.Google ScholarGoogle Scholar
  8. Glišović N, Milenković M, Bojović N, 2016. A hybrid model for forecasting the volume of passenger flows on Serbian railways[J]. Operational Research, 16: 271-285. Doi:10.1007/s12351-015-0198-5.Google ScholarGoogle ScholarCross RefCross Ref
  9. Xu S. 2019. Forecasting method of highway passenger transportation volume in Gansu Province based on BP neural network[J]. Traffic and Transportation, 35(05):28-31. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YSJT201905008&DbName=CJFQ2019.Google ScholarGoogle Scholar
  10. Xu S, Chui S. 2020. Highway passenger transportation prediction by double hidden layer BP neural network based on Softplus function[J]. Journal of University of South China (Natural Science Edition), 34(01):88-92. DOI: 10.19431/j.cnki.1673-0062.2020.01.014.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Other conferences
      EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
      October 2023
      1809 pages
      ISBN:9798400708305
      DOI:10.1145/3650400

      Copyright © 2023 ACM

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      New York, NY, United States

      Publication History

      • Published: 17 April 2024

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