IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v646y2024ics0378437124003819.html
   My bibliography  Save this article

Coupling graph neural networks and travel mode choice for human mobility prediction

Author

Listed:
  • Wang, Kun
  • Peng, Zhenghong
  • Cai, Meng
  • Wu, Hao
  • Liu, Lingbo
  • Sun, Zhihao

Abstract

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.

Suggested Citation

  • Wang, Kun & Peng, Zhenghong & Cai, Meng & Wu, Hao & Liu, Lingbo & Sun, Zhihao, 2024. "Coupling graph neural networks and travel mode choice for human mobility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    • Handle: RePEc:eee:phsmap:v:646:y:2024:i:c:s0378437124003819
    DOI: 10.1016/j.physa.2024.129872
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124003819
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129872?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. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    2. Habibian, Meeghat & Kermanshah, Mohammad, 2013. "Coping with congestion: Understanding the role of simultaneous transportation demand management policies on commuters," Transport Policy, Elsevier, vol. 30(C), pages 229-237.
    3. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    4. Tang, Jinjun & Zhao, Chuyun & Liu, Fang & Hao, Wei & Gao, Fan, 2022. "Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    5. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.
    6. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    7. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
    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. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    3. Toger, Marina & Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2023. "Inequality in leisure mobility: An analysis of activity space segregation spectra in the Stockholm conurbation," Journal of Transport Geography, Elsevier, vol. 111(C).
    4. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    6. Su, Duan & Wang, Yacan & Yang, Nan & Wang, Xianghong, 2020. "Promoting considerate parking behavior in dockless bike-sharing: An experimental study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 153-165.
    7. Aniket Vatsa & Ananda Shankar Hati & Vadim Bolshev & Alexander Vinogradov & Vladimir Panchenko & Prasun Chakrabarti, 2023. "Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks," Energies, MDPI, vol. 16(5), pages 1-14, March.
    8. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    9. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
    10. Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
    11. Yaxin Tian & Xiang Ren & Keke Li & Xiangqian Li, 2025. "Carbon Dioxide Emission Forecast: A Review of Existing Models and Future Challenges," Sustainability, MDPI, vol. 17(4), pages 1-29, February.
    12. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    13. Moeinaddini, Amin & Habibian, Meeghat, 2024. "Acceptability of transportation demand management policy packages considering interactions and socio-economic heterogeneity," Research in Transportation Economics, Elsevier, vol. 103(C).
    14. Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).
    15. Galit Cohen-Blankshtain & Hillel Bar-Gera & Yoram Shiftan, 2023. "Congestion pricing and positive incentives: conceptual analysis and empirical findings from Israel," Transportation, Springer, vol. 50(2), pages 607-633, April.
    16. Khalilikhah, Majid & Habibian, Meeghat & Heaslip, Kevin, 2016. "Acceptability of increasing petrol price as a TDM pricing policy: A case study in Tehran," Transport Policy, Elsevier, vol. 45(C), pages 136-144.
    17. HUO, Zhengqi & YANG, Xiaobao & LIU, Xiaobing & YAN, Xuedong, 2024. "Spatio-temporal analysis on online designated driving based on empirical data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    18. Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
    19. Banglong Pan & Hanming Yu & Hongwei Cheng & Shuhua Du & Shutong Cai & Minle Zhao & Juan Du & Fazhi Xie, 2023. "A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    20. Chen, Daqiang & Ignatius, Joshua & Sun, Danzhi & Goh, Mark & Zhan, Shalei, 2018. "Impact of congestion pricing schemes on emissions and temporal shift of freight transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 77-105.

    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:eee:phsmap:v:646:y:2024:i:c:s0378437124003819. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.