Analysis and Prediction of Urban Traffic Congestion Based on Big Data

Zhenhua Wang; Yangsen Yu; Dangchen Ju

doi:doi:10.11648/j.ijdst.20180403.14

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Analysis and Prediction of Urban Traffic Congestion Based on Big Data

Zhenhua Wang, Yangsen Yu, Dangchen Ju

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.

DOI	10.11648/j.ijdst.20180403.14
Published in	International Journal on Data Science and Technology (Volume 4, Issue 3, September 2018)
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), 2024. Published by Science Publishing Group

Keywords

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

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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

Zhenhua Wang

School of Information Engineering, China University of Geosciences, Beijing, China
Yangsen Yu

Geological Survey Institute, China University of Geosciences, Beijing, China
Dangchen Ju

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

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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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download