IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p6203-d393017.html
   My bibliography  Save this article

Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration

Author

Listed:
  • Qing Yu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
    Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan)

  • Weifeng Li

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

  • Haoran Zhang

    (Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan)

  • Dongyuan Yang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

Abstract

Employment is one of the essential socioeconomic connections in urban agglomeration. However, both the demand and the supply of transjurisdictional public transport service are unevenly distributed in such area. Customized bus has a high potential of serving transjurisdictional and long-distance commuting demand. This study proposes a network-based layout design method to generate hierarchical service scopes and stations for customized bus system. First, the home and the workplace of residents are identified using mobile phone data, to construct a jobs-housing relationship network. Then, an iterative algorithm based on network community detection and density-based spatial clustering is applied to the jobs-housing relationship network to hierarchically segment the urban agglomeration area into communities. Three methods are proposed for location selection of customized bus stations. A case study is conducted using the mobile phone dataset from nine cities in the Yangtze River Urban Agglomeration in China. A four-level hierarchical customized bus system layout is generated and both the spatial properties and network properties of service scopes are analyzed. The proposed three methods of customized bus station location selection are compared based on the average travel distance and the rationality of the resulted customized bus station location.

Suggested Citation

  • Qing Yu & Weifeng Li & Haoran Zhang & Dongyuan Yang, 2020. "Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6203-:d:393017
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/6203/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/15/6203/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhiling Han & Yanyan Chen & Hui Li & Kuanshuang Zhang & Jiyang Sun, 2019. "Customized Bus Network Design Based on Individual Reservation Demands," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    2. Jing Li & Yongbo Lv & Jihui Ma & Qi Ouyang, 2018. "Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data," Energies, MDPI, vol. 11(9), pages 1-15, August.
    3. Leon Cooper, 1963. "Location-Allocation Problems," Operations Research, INFORMS, vol. 11(3), pages 331-343, June.
    4. Liu, Tao & Ceder, Avishai (Avi), 2015. "Analysis of a new public-transport-service concept: Customized bus in China," Transport Policy, Elsevier, vol. 39(C), pages 63-76.
    5. Jihui Ma & Yanqing Zhao & Yang Yang & Tao Liu & Wei Guan & Jiao Wang & Cuiying Song, 2017. "A Model for the Stop Planning and Timetables of Customized Buses," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-28, January.
    6. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    7. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    8. Zhang, Haoran & Song, Xuan & Xia, Tianqi & Yuan, Meng & Fan, Zipei & Shibasaki, Ryosuke & Liang, Yongtu, 2018. "Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response," Applied Energy, Elsevier, vol. 220(C), pages 527-535.
    9. ZhiJie Li & Rui Song & Shiwei He & Mingkai Bi, 2018. "Methodology of mixed load customized bus lines and adjustment based on time windows," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    10. Zhang, Haoran & Song, Xuan & Long, Yin & Xia, Tianqi & Fang, Kai & Zheng, Jianqin & Huang, Dou & Shibasaki, Ryosuke & Liang, Yongtu, 2019. "Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis," Applied Energy, Elsevier, vol. 242(C), pages 138-147.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weifeng Li & Jiawei He & Qing Yu & Yujiao Chang & Peng Liu, 2021. "Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    2. Peiqing Li & Longlong Jiang & Shunfeng Zhang & Xi Jiang, 2022. "Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    3. Qing Yu & Weifeng Li & Dongyuan Yang & Yingkun Xie, 2020. "Policy Zoning for Efficient Land Utilization Based on Spatio-Temporal Integration between the Bicycle-Sharing Service and the Metro Transit," Sustainability, MDPI, vol. 13(1), pages 1-14, December.
    4. Xuekai Cen & Kanghui Ren & Yiying Cai & Qun Chen, 2023. "Designing Flexible-Bus System with Ad-Hoc Service Using Travel-Demand Clustering," Mathematics, MDPI, vol. 11(4), pages 1-27, February.

    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. Zhiling Han & Yanyan Chen & Hui Li & Kuanshuang Zhang & Jiyang Sun, 2019. "Customized Bus Network Design Based on Individual Reservation Demands," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    2. Bing Zhang & Zhishan Zhong & Xun Zhou & Yongqiang Qu & Fangwei Li, 2023. "Optimization Model and Solution Algorithm for Rural Customized Bus Route Operation under Multiple Constraints," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    3. Yi, Wenjing & Yan, Jie, 2020. "Energy consumption and emission influences from shared mobility in China: A national level annual data analysis," Applied Energy, Elsevier, vol. 277(C).
    4. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    5. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    6. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    7. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    8. Liu, Jiaguo & Zhao, Huida & Li, Jian & Yue, Xiaohang, 2021. "Operational strategy of customized bus considering customers’ variety seeking behavior and service level," International Journal of Production Economics, Elsevier, vol. 231(C).
    9. Yunlin Guan & Yun Wang & Xuedong Yan & Haonan Guo & Yi Zhao, 2022. "The One E-Ticket Customized Bus Service Mode for Passengers with Multiple Trips and the Routing Problem," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    10. Jing Li & Yongbo Lv & Jihui Ma & Yuan Ren, 2019. "Factor Analysis of Customized Bus Attraction to Commuters with Different Travel Modes," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    11. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2021. "Zonal-based flexible bus service under elastic stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. Shang, Huayan & Chang, Yi & Huang, Haijun & Zhao, Fangxia, 2022. "Integration of conventional and customized bus services: An empirical study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    13. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    14. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    15. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    16. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    17. Zhang, Jie & Wang, David Z.W. & Meng, Meng, 2018. "Which service is better on a linear travel corridor: Park & ride or on-demand public bus?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 803-818.
    18. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    19. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.
    20. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.

    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:gam:jsusta:v:12:y:2020:i:15:p:6203-:d:393017. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.