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Research on Visualization Method of Congestion Pattern in Urban Transportation Hub

Published: 18 August 2021 Publication History

Abstract

With the deepening of the urbanization process, the number of motor vehicles is increasing day by day, plenty of cities are encountering the problem of traffic saturation. There are many interlaced road sections in urban traffic hubs, which makes the traffic jam during rush hours extremely serious. In order to clearly display the daily conditions of traffic hubs, and better assist relevant personnel to conduct the congestion research and analysis, this paper adopts the data visualization method to analyze the vehicle trajectory data of traffic hubs, which can be used to display various congestion patterns and causes, and provide references for road planning decision-making.

References

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Zhe Wang, Study on evaluation of development level of public transportation in the main urban area of Chongqing [D]. Chongqing Jiaotong University,2014.
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Pablo Samuel Castro From taxi GPS traces to social and community dynamics[J]. ACM Computing Surveys (CSUR), 2013, 46(2): 1-34.
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F. Hardisty and A. Klippel. Analyzing Spatio-temporal autocorrelation with LISTA-Viz[J]. International Journal of Geographical Information Science, 2010, 24(10): 1515-1526.
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Meng Sun, Jianmei Li, Feng Sun, XiaoWei Wu, Haotian Chen, Shuang Zhu, Traffic parameters extraction and visualization based on integrated data [A]. ITS China. Proceedings of the 15th China Intelligent Transportation Annual Conference [C] ITS China: ITS China,2020:2
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Xinyuan Cui, Duanshu. Peng. Study on Data-driven Traffic Congestion Patterns in Chengdu City[J]. American Journal of Industrial Engineering,2020,7(1).
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Zifu Huang, Jing Wang, Xinyao Liu. Congestion reconstruction analysis of Chongqing 4-km interchange to Jiangnan interchange [J]. Western Communications Technology,2018(11):139-142+208

Cited By

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  • (2024)Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modelingGeo-spatial Information Science10.1080/10095020.2024.2356238(1-25)Online publication date: 10-Jun-2024
  • (2024)Unveiling urban traffic accessibility patterns and phase diagrams of traffic direction through real-time navigation data in BeijingInformation Processing & Management10.1016/j.ipm.2024.10366061:3(103660)Online publication date: May-2024

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cover image ACM Other conferences
ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems
May 2021
2053 pages
ISBN:9781450390200
DOI:10.1145/3469213
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2021

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Author Tags

  1. Correlative Visual Analysis
  2. Road Congestion Pattern
  3. Spatiotemporal Data
  4. Taxi Trajectory Data Analysis
  5. Visualization

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Cited By

View all
  • (2024)Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modelingGeo-spatial Information Science10.1080/10095020.2024.2356238(1-25)Online publication date: 10-Jun-2024
  • (2024)Unveiling urban traffic accessibility patterns and phase diagrams of traffic direction through real-time navigation data in BeijingInformation Processing & Management10.1016/j.ipm.2024.10366061:3(103660)Online publication date: May-2024

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