IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2564-d756531.html
   My bibliography  Save this article

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

Author

Listed:
  • Sujae Kim

    (Department of Urban Planning, Hongik University, Seoul 04066, Korea)

  • Sangho Choo

    (Department of Urban Design & Planning, Hongik University, Seoul 04066, Korea)

  • Gyeongjae Lee

    (Department of Urban Planning, Hongik University, Seoul 04066, Korea)

  • Sanghun Kim

    (PUMP Corporation, Seoul 06147, Korea)

Abstract

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

Suggested Citation

  • Sujae Kim & Sangho Choo & Gyeongjae Lee & Sanghun Kim, 2022. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method," Sustainability, MDPI, vol. 14(5), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2564-:d:756531
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2564/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/5/2564/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.
    2. Katarzyna Turoń & Andrzej Kubik & Feng Chen, 2021. "When, What and How to Teach about Electric Mobility? An Innovative Teaching Concept for All Stages of Education: Lessons from Poland," Energies, MDPI, vol. 14(19), pages 1-16, October.
    3. Yajun Zhou & Lilei Wang & Rong Zhong & Yulong Tan, 2018. "A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, January.
    4. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    5. McKenzie, Grant, 2019. "Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C," Journal of Transport Geography, Elsevier, vol. 78(C), pages 19-28.
    6. Gabriel Dias & Elisabete Arsenio & Paulo Ribeiro, 2021. "The Role of Shared E-Scooter Systems in Urban Sustainability and Resilience during the Covid-19 Mobility Restrictions," Sustainability, MDPI, vol. 13(13), pages 1-19, June.
    7. Owain James & J I Swiderski & John Hicks & Denis Teoman & Ralph Buehler, 2019. "Pedestrians and E-Scooters: An Initial Look at E-Scooter Parking and Perceptions by Riders and Non-Riders," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    8. Tiziana Campisi & Anastasios Skoufas & Alexandros Kaltsidis & Socrates Basbas, 2021. "Gender Equality and E-Scooters: Mind the Gap! A Statistical Analysis of the Sicily Region, Italy," Social Sciences, MDPI, vol. 10(10), pages 1-24, October.
    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. Antonio A. Barreda-Luna & Juvenal Rodríguez-Reséndiz & Alejandro Flores Rangel & Omar Rodríguez-Abreo, 2022. "Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Hakan İnaç, 2023. "Micro-Mobility Sharing System Accident Case Analysis by Statistical Machine Learning Algorithms," Sustainability, MDPI, vol. 15(3), pages 1-31, January.
    3. Songhyeon Shin & Sangho Choo, 2022. "Influence of Built Environment on Micromobility–Pedestrian Accidents," Sustainability, MDPI, vol. 15(1), pages 1-11, December.

    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. Alexandra König & Laura Gebhardt & Kerstin Stark & Julia Schuppan, 2022. "A Multi-Perspective Assessment of the Introduction of E-Scooter Sharing in Germany," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    2. Georgia Ayfantopoulou & Josep Maria Salanova Grau & Zisis Maleas & Alexandros Siomos, 2022. "Micro-Mobility User Pattern Analysis and Station Location in Thessaloniki," Sustainability, MDPI, vol. 14(11), pages 1-14, May.
    3. Cao, Zhejing & Zhang, Xiaohu & Chua, Kelman & Yu, Honghai & Zhao, Jinhua, 2021. "E-scooter sharing to serve short-distance transit trips: A Singapore case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 177-196.
    4. Tim De Ceunynck & Gert Jan Wijlhuizen & Aslak Fyhri & Regine Gerike & Dagmar Köhler & Alice Ciccone & Atze Dijkstra & Emmanuelle Dupont & Mario Cools, 2021. "Assessing the Willingness to Use Personal e-Transporters (PeTs): Results from a Cross-National Survey in Nine European Cities," Sustainability, MDPI, vol. 13(7), pages 1-15, March.
    5. Tiziana Campisi & Anastasios Skoufas & Alexandros Kaltsidis & Socrates Basbas, 2021. "Gender Equality and E-Scooters: Mind the Gap! A Statistical Analysis of the Sicily Region, Italy," Social Sciences, MDPI, vol. 10(10), pages 1-24, October.
    6. Kim, Minju & Puczkowskyj, Nicholas & MacArthur, John & Dill, Jennifer, 2023. "Perspectives on e-scooters use: A multi-year cross-sectional approach to understanding e-scooter travel behavior in Portland, Oregon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    7. Samadzad, Mahdi & Nosratzadeh, Hossein & Karami, Hossein & Karami, Ali, 2023. "What are the factors affecting the adoption and use of electric scooter sharing systems from the end user's perspective?," Transport Policy, Elsevier, vol. 136(C), pages 70-82.
    8. Fitt, Helen & Curl, Angela, 2020. "The early days of shared micromobility: A social practices approach," Journal of Transport Geography, Elsevier, vol. 86(C).
    9. Samira Dibaj & Aryan Hosseinzadeh & Miloš N. Mladenović & Robert Kluger, 2021. "Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas," Sustainability, MDPI, vol. 13(21), pages 1-27, October.
    10. Alberica Domitilla Bozzi & Anne Aguilera, 2021. "Shared E-Scooters: A Review of Uses, Health and Environmental Impacts, and Policy Implications of a New Micro-Mobility Service," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    11. Stefania Boglietti & Benedetto Barabino & Giulio Maternini, 2021. "Survey on e-Powered Micro Personal Mobility Vehicles: Exploring Current Issues towards Future Developments," Sustainability, MDPI, vol. 13(7), pages 1-34, March.
    12. Ecer, Fatih & Küçükönder, Hande & Kayapınar Kaya, Sema & Faruk Görçün, Ömer, 2023. "Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    13. Bretones, Alexandra & Marquet, Oriol, 2022. "Sociopsychological factors associated with the adoption and usage of electric micromobility. A literature review," Transport Policy, Elsevier, vol. 127(C), pages 230-249.
    14. Maximilian Heumann & Tobias Kraschewski & Tim Brauner & Lukas Tilch & Michael H. Breitner, 2021. "A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    15. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    16. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    17. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    18. Shah, Nitesh R. & Ziedan, Abubakr & Brakewood, Candace & Cherry, Christopher R., 2023. "Shared e-scooter service providers with large fleet size have a competitive advantage: Findings from e-scooter demand and supply analysis of Nashville, Tennessee," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    19. Jo, Hang-Hyun & Moon, Eunyoung, 2016. "Dynamical complexity in the perception-based network formation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 282-292.
    20. Yu, Shuo & Alqahtani, Fayez & Tolba, Amr & Lee, Ivan & Jia, Tao & Xia, Feng, 2022. "Collaborative Team Recognition: A Core Plus Extension Structure," Journal of Informetrics, Elsevier, vol. 16(4).

    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:14:y:2022:i:5:p:2564-:d:756531. 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.