IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i12p7371-d840351.html
   My bibliography  Save this article

Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand

Author

Listed:
  • Hongxia Ge

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Center, Ningbo 315211, China)

  • Siteng Li

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Center, Ningbo 315211, China)

  • Rongjun Cheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Center, Ningbo 315211, China)

  • Zhenlei Chen

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Center, Ningbo 315211, China)

Abstract

As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing demand services. A self-attention mechanism in convolutional long short-term memory (ConvLSTM) is proposed to accurately predict the online car-hailing demand. It can more effectively address the disadvantage that ConvLSTM is not good at capturing spatial correlation over a large spatial extent. Furthermore, it can generate features by aggregating pair-wise similarity scores of features at all positions of input and memory, and thus obtain the function of long-range spatiotemporal dependencies. First, the online car-hailing trajectories dataset was converted into images after geographic grid matching, and image enhancement was performed by cropping. Then, the effectiveness of the ConvLSTM embedded with a self-attention mechanism (SA-ConvLSTM) was demonstrated by comparing it to existing models. The experimental results showed that the proposed model performed better than the existing models, and including spatiotemporal information in images would perform better predictions than including spatial information in time-series pixels.

Suggested Citation

  • Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7371-:d:840351
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/12/7371/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/12/7371/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    3. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    4. Dia, Hussein, 2001. "An object-oriented neural network approach to short-term traffic forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 253-261, June.
    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. Jalalifar, Rasoul & Delavar, Mahmoud Reza & Ghaderi, Seyed Farid, 2025. "A novel approach for photovoltaic plant site selection in megacities utilizing power load forecasting and fuzzy inference system," Renewable Energy, Elsevier, vol. 243(C).

    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. Lu, Xijin & Ma, Changxi & Qiao, Yihuan, 2021. "Short-term demand forecasting for online car-hailing using ConvLSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    2. Jinlong Li & Haoran Chen & Qiuzi Lu & Xi Wang & Haifeng Song & Lunming Qin, 2025. "A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction," Mathematics, MDPI, vol. 13(14), pages 1-18, July.
    3. Alireza Ermagun & David Levinson, 2017. "Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending," Working Papers 000166, University of Minnesota: Nexus Research Group.
    4. Alireza Ermagun & David M Levinson, 2019. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions," Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
    5. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    6. Xing, Tao & Zhou, Xuesong & Taylor, Jeffrey, 2013. "Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 66-90.
    7. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    8. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    9. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    10. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    11. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    12. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    13. Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting," Environment and Planning B, , vol. 35(5), pages 762-771, October.
    14. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    15. Qing Luo & Daniel A. Griffith & Huayi Wu, 2019. "Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics," Journal of Geographical Systems, Springer, vol. 21(2), pages 237-269, June.
    16. Safikhani, Abolfazl & Kamga, Camille & Mudigonda, Sandeep & Faghih, Sabiheh Sadat & Moghimi, Bahman, 2020. "Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1138-1148.
    17. Jing Bai & Jiahui Wang & Jin Ran & Xingyuan Li & Chuang Tu, 2024. "An Improved Neural Network Algorithm for Energy Consumption Forecasting," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    18. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    19. Balaji Ganesh Rajagopal & Manish Kumar & Pijush Samui & Mosbeh R. Kaloop & Usama Elrawy Shahdah, 2022. "A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    20. Ma, Teng & Li, Ming-Jia & Xu, Jin-Liang & Cao, Feng, 2019. "Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant," Energy, Elsevier, vol. 175(C), pages 123-138.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:12:p:7371-:d:840351. 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.