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Analysis and Prediction of Urban Traffic Congestion Based on Big Data

Received: 28 September 2018     Accepted: 10 October 2018     Published: 30 October 2018
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Abstract

With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.

Published in International Journal on Data Science and Technology (Volume 4, Issue 3)
DOI 10.11648/j.ijdst.20180403.14
Page(s) 100-105
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Traffic Congestion, Big Data, Intelligent Transportation System, Road Capacity

References
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Cite This Article
  • APA Style

    Zhenhua Wang, Yangsen Yu, Dangchen Ju. (2018). Analysis and Prediction of Urban Traffic Congestion Based on Big Data. International Journal on Data Science and Technology, 4(3), 100-105. https://doi.org/10.11648/j.ijdst.20180403.14

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    ACS Style

    Zhenhua Wang; Yangsen Yu; Dangchen Ju. Analysis and Prediction of Urban Traffic Congestion Based on Big Data. Int. J. Data Sci. Technol. 2018, 4(3), 100-105. doi: 10.11648/j.ijdst.20180403.14

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    AMA Style

    Zhenhua Wang, Yangsen Yu, Dangchen Ju. Analysis and Prediction of Urban Traffic Congestion Based on Big Data. Int J Data Sci Technol. 2018;4(3):100-105. doi: 10.11648/j.ijdst.20180403.14

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  • @article{10.11648/j.ijdst.20180403.14,
      author = {Zhenhua Wang and Yangsen Yu and Dangchen Ju},
      title = {Analysis and Prediction of Urban Traffic Congestion Based on Big Data},
      journal = {International Journal on Data Science and Technology},
      volume = {4},
      number = {3},
      pages = {100-105},
      doi = {10.11648/j.ijdst.20180403.14},
      url = {https://doi.org/10.11648/j.ijdst.20180403.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180403.14},
      abstract = {With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Analysis and Prediction of Urban Traffic Congestion Based on Big Data
    AU  - Zhenhua Wang
    AU  - Yangsen Yu
    AU  - Dangchen Ju
    Y1  - 2018/10/30
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijdst.20180403.14
    DO  - 10.11648/j.ijdst.20180403.14
    T2  - International Journal on Data Science and Technology
    JF  - International Journal on Data Science and Technology
    JO  - International Journal on Data Science and Technology
    SP  - 100
    EP  - 105
    PB  - Science Publishing Group
    SN  - 2472-2235
    UR  - https://doi.org/10.11648/j.ijdst.20180403.14
    AB  - With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.
    VL  - 4
    IS  - 3
    ER  - 

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Author Information
  • School of Information Engineering, China University of Geosciences, Beijing, China

  • Geological Survey Institute, China University of Geosciences, Beijing, China

  • School of Information Engineering, China University of Geosciences, Beijing, China

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