IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i7d10.1007_s10668-021-01829-8.html
   My bibliography  Save this article

Planning of a comprehensive transportation system in Ma’anshan based on mobile phone signaling data

Author

Listed:
  • Xiaofeng Lou

    (Southeast University)

  • Changhai Peng

    (Southeast University
    Ministry of Education)

Abstract

Integrated transportation system planning is forward-looking and oriented to the future development of transportation. It is an important issue for special planning in terms of current land and spatial planning, and all community sectors recognize its importance. The continuous development of big data technology in recent years has brought a great deal of transformative power to urban traffic planning theory and technology. Using mobile phone signaling data (MPSD) to analyze and calculate traffic data information is a new technology in wide-area dynamic traffic detection. This paper uses MPSD to extract traffic contact characteristic data, commuter contact characteristic data, and thermal population data to analyze the traffic characteristics of Ma’anshan’s metropolitan clusters, suburbs, and city areas. We identify its co-urban connection characteristics with neighboring cities and the strong southern river corridor of the metropolis, the characteristics of the connection, and the centripetal connection of the central city. According to the problems existing in Ma’anshan’s facility configuration and traffic model, we propose corresponding countermeasures: building a multilevel integrated transportation system in the same city at the metropolitan cluster regional level; forming a centrally radiating multiple-transit network to support the development of the urban spatial pattern at the suburban level; and forming public transportation leadership and road support to optimize the urban transportation network at the city level.

Suggested Citation

  • Xiaofeng Lou & Changhai Peng, 2022. "Planning of a comprehensive transportation system in Ma’anshan based on mobile phone signaling data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9380-9406, July.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:7:d:10.1007_s10668-021-01829-8
    DOI: 10.1007/s10668-021-01829-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01829-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-021-01829-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    2. Asakura, Yasuo & Iryo, Takamasa, 2007. "Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(7), pages 684-690, August.
    3. Zhenbo Lu & Zhen Long & Jingxin Xia & Chengchuan An, 2019. "A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Satomi Kimijima & Masahiko Nagai, 2017. "Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    2. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    3. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    4. Jun Shao & Xuesong Chang & Alastair M. Morrison, 2017. "How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    5. Anne Hardy & Sarah Hyslop & Kate Booth & Brady Robards & Jagannath Aryal & Ulrike Gretzel & Richard Eccleston, 2017. "Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion," Information Technology & Tourism, Springer, vol. 17(3), pages 255-274, September.
    6. Xiaoyu Cai & Yihan Zhang & Xin Zhang & Bo Peng, 2023. "Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data," Sustainability, MDPI, vol. 15(15), pages 1-28, July.
    7. Liguang, Feng & Haozhi, Zhang & Yulin, Jiang & Zhaorong, Wang, 2010. "Evaluation on the Effect of Car Use Restriction Measures in Beijing," 51st Annual Transportation Research Forum, Arlington, Virginia, March 11-13, 2010 207232, Transportation Research Forum.
    8. Chang-jun Cai & En-jian Yao & Sha-sha Liu & Yong-sheng Zhang & Jun Liu, 2015. "Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-7, February.
    9. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    10. Moiseeva, Anastasia & Timmermans, Harry, 2010. "Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning," Journal of Retailing and Consumer Services, Elsevier, vol. 17(3), pages 189-199.
    11. Rola Y. M. Mohammed, 2021. "Optimizing Temporal Business Opportunities," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(11), pages 104-104, July.
    12. Qiqi Liu & Xiaolan Tang & Ka Li, 2022. "Do Historic Landscape Images Predict Tourists’ Spatio-Temporal Behavior at Heritage Sites? A Case Study of West Lake in Hangzhou, China," Land, MDPI, vol. 11(10), pages 1-20, September.
    13. Hahnel, Ulf J.J. & Gölz, Sebastian & Spada, Hans, 2013. "How accurate are drivers’ predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 123-131.
    14. Yi Zhu, 2022. "Inference of activity patterns from urban sensing data using conditional random fields," Environment and Planning B, , vol. 49(2), pages 549-565, February.
    15. Calastri, Chiara & Hess, Stephane & Choudhury, Charisma & Daly, Andrew & Gabrielli, Lorenzo, 2019. "Mode choice with latent availability and consideration: Theory and a case study," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 374-385.
    16. Firnkorn, Jörg, 2012. "Triangulation of two methods measuring the impacts of a free-floating carsharing system in Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1654-1672.
    17. Hee Chung Chung & Namho Chung & Yoonjae Nam, 2017. "A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe," Sustainability, MDPI, vol. 9(12), pages 1-19, December.
    18. Yijing Lu & Lei Zhang, 2015. "Imputing trip purposes for long-distance travel," Transportation, Springer, vol. 42(4), pages 581-595, July.
    19. Roy, Avipsa & Fuller, Daniel & Nelson, Trisalyn & Kedron, Peter, 2022. "Assessing the role of geographic context in transportation mode detection from GPS data," Journal of Transport Geography, Elsevier, vol. 100(C).
    20. Chunla Liu & Yingjie Qin & Yufei Wang & Yue Yu & Guanghui Li, 2022. "Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan," Sustainability, MDPI, vol. 14(13), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:24:y:2022:i:7:d:10.1007_s10668-021-01829-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.