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Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Author

Listed:
  • Jiwei Li

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information Systems, Ministry Education, Wuhan University, Wuhan 430079, China)

  • Qingqing Ye

    (School of Engineering Management and Real Estate, Henan University of Economics and Law, Zhengzhou 450002, China)

  • Xuankai Deng

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information Systems, Ministry Education, Wuhan University, Wuhan 430079, China)

  • Yaolin Liu

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information Systems, Ministry Education, Wuhan University, Wuhan 430079, China)

  • Yanfang Liu

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information Systems, Ministry Education, Wuhan University, Wuhan 430079, China)

Abstract

Spring Festival travel rush is a phenomenon in China that population travel intensively surges in a short time around Chinese Spring Festival. This phenomenon, which is a special one in the urbanization process of China, brings a large traffic burden and various kinds of social problems, thereby causing widespread public concern. This study investigates the spatial-temporal characteristics of Spring Festival travel rush in 2015 through time series analysis and complex network analysis based on multisource big travel data derived from Baidu, Tencent, and Qihoo. The main results are as follows: First, big travel data of Baidu and Tencent obtained from location-based services might be more accurate and scientific than that of Qihoo. Second, two travel peaks appeared at five days before and six days after the Spring Festival, respectively, and the travel valley appeared on the Spring Festival. The Spring Festival travel network at the provincial scale did not have small-world and scale-free characteristics. Instead, the travel network showed a multicenter characteristic and a significant geographic clustering characteristic. Moreover, some travel path chains played a leading role in the network. Third, economic and social factors had more influence on the travel network than geographical location factors. The problem of Spring Festival travel rush will not be effectively improved in a short time because of the unbalanced urban-rural development and the unbalanced regional development. However, the development of the modern high-speed transport system and the modern information and communication technology can alleviate problems brought by Spring Festival travel rush. We suggest that a unified real-time traffic platform for Spring Festival travel rush should be established through the government's integration of mobile big data and the official authority data of the transportation department.

Suggested Citation

  • Jiwei Li & Qingqing Ye & Xuankai Deng & Yaolin Liu & Yanfang Liu, 2016. "Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1184-:d:83031
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    References listed on IDEAS

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    Cited by:

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    2. Yunzi Yang & Yuanyuan Ma & Hongzan Jiao, 2021. "Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example," Land, MDPI, vol. 10(9), pages 1-23, September.
    3. Yuanyuan Ma & Hongzan Jiao, 2023. "Quantitative Evaluation of Friendliness in Streets’ Pedestrian Networks Based on Complete Streets: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 15(13), pages 1-28, June.
    4. Zhen Yang & Weijun Gao & Xueyuan Zhao & Chibiao Hao & Xudong Xie, 2020. "Spatiotemporal Patterns of Population Mobility and Its Determinants in Chinese Cities Based on Travel Big Data," Sustainability, MDPI, vol. 12(10), pages 1-25, May.
    5. Li, Tao & Wang, Jiaoe & Huang, Jie & Yang, Wenyue & Chen, Zhuo, 2021. "Exploring the dynamic impacts of COVID-19 on intercity travel in China," Journal of Transport Geography, Elsevier, vol. 95(C).
    6. Wenqian Ke & Wei Chen & Zhaoyuan Yu, 2017. "Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    7. Yang, Hu & Lv, Sirui & Guo, Bao & Dai, Jianjun & Wang, Pu, 2024. "Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    8. Ruoxin Zhu & Diao Lin & Yujing Wang & Michael Jendryke & Rui Xin & Jian Yang & Jianzhong Guo & Liqiu Meng, 2020. "Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival," Sustainability, MDPI, vol. 12(8), pages 1-21, April.

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