Skip to main content

Advertisement

SpringerLink
Log in

ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a ‘preprocessing-encoder-decoder’ hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The commuting flow data is provided by Amap company, and they have not given their permission for researchers to share their data. Data requests can be made to Amap company (https://lbs.amap.com/). The land use data is publicly available online at http://data.ess.tsinghua.edu.cn/.

References

  1. Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, Chi G, Shi L (2015) Social sensing: A new approach to understanding our socioeconomic environments. Ann Assoc Am Geogr 105(3):512–530

    Article  Google Scholar 

  2. Walmsley DJ, Lewis GJ (2014) People and environment: Behavioural approaches in human geography. Routledge

  3. Hu Y (2017) Geospatial semantics. arXiv:1707.03550

  4. Huang W, Li S (2016) Understanding human activity patterns based on space-time-semantics. ISPRS J Photogramm Remote Sens 121:1–10

    Article  Google Scholar 

  5. Liu X, Kang C, Gong L, Liu Y (2016) Incorporating spatial interaction patterns in classifying and understanding urban land use. Int J Geogr Inf Sci 30(2):334–350

    Article  Google Scholar 

  6. Simini F, Barlacchi G, Luca M, Pappalardo L (2021) A deep gravity model for mobility flows generation. Nat Commun 12(1):1–13

    Article  Google Scholar 

  7. Tardy C, Falquet G, Moccozet L (2016) Semantic enrichment of places with vgi sources: a knowledge based approach. In: Proceedings of the 10th workshop on geographic information retrieval, pp 1–2

  8. Van Acker V, Witlox F (2011) Commuting trips within tours: how is commuting related to land use? Transportation 38(3):465–486

    Article  Google Scholar 

  9. Wu L, Yang L, Huang Z, Wang Y, Chai Y, Peng X, Liu Y (2019) Inferring demographics from human trajectories and geographical context. Comput Environ Urban Syst 77:101368

    Article  Google Scholar 

  10. Li Y, Yu R, Shahabi C, Liu Y (2018b) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: International conference on learning representations

  11. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3634–3640

  12. Zhang Y, Cheng T, Ren Y, Xie K (2020) A novel residual graph convolution deep learning model for short-term network-based traffic forecasting. Int J Geogr Inf Sci 34(5):969–995

    Article  Google Scholar 

  13. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858

    Article  Google Scholar 

  14. Barbosa H, Barthelemy M, Ghoshal G, James CR, Lenormand M, Louail T, Menezes R, Ramasco JJ, Simini F, Tomasini M (2018) Human mobility: Models and applications. Phys Rep 734:1–74

    Article  MathSciNet  MATH  Google Scholar 

  15. Lenormand M, Bassolas A, Ramasco JJ (2016) Systematic comparison of trip distribution laws and models. J Transp Geogr 51:158–169

    Article  Google Scholar 

  16. Masucci AP, Serras J, Johansson A, Batty M (2013) Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Phys Rev E 88(2):022812

    Article  Google Scholar 

  17. Zipf GK (1946) The p 1 p 2/d hypothesis: on the intercity movement of persons. Am Sociol Rev 11(6):677–686

    Article  Google Scholar 

  18. Ren Y, Ercsey-Ravasz M, Wang P, González MC, Toroczkai Z (2014) Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nat Commun 5(1):1–9

    Article  Google Scholar 

  19. Simini F, González MC, Maritan A, Barabási A-L (2012) A universal model for mobility and migration patterns. Nature 484(7392):96–100

    Article  Google Scholar 

  20. Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260

    Article  MathSciNet  MATH  Google Scholar 

  21. Morton A, Piburn J, Nagle N (2018) Need a boost? a comparison of traditional commuting models with the xgboost model for predicting commuting flows (short paper). In: 10th International Conference on Geographic Information Science (GIScience 2018). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik

  22. Mozolin M, Thill J.-C., Usery EL (2000) Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation. Transp Res B Methodol 34(1):53–73

    Article  Google Scholar 

  23. Pourebrahim N, Sultana S, Niakanlahiji A, Thill J-C (2019) Trip distribution modeling with twitter data. Comput Environ Urban Syst 77:101354

    Article  Google Scholar 

  24. Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46(sup1):234–240

    Article  Google Scholar 

  25. Xing X, Huang Z, Cheng X, Zhu D, Kang C, Zhang F, Liu Y (2020) Mapping human activity volumes through remote sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 13:5652–5668

    Article  Google Scholar 

  26. Stouffer SA (1940) Intervening opportunities: a theory relating mobility and distance. Am Sociol Rev 5(6):845–867

    Article  Google Scholar 

  27. McArthur DP, Kleppe G, Thorsen I, Ubøe J (2011) The spatial transferability of parameters in a gravity model of commuting flows. J Transp Geogr 19 (4):596–605

    Article  Google Scholar 

  28. Black WR (1995) Spatial interaction modeling using artificial neural networks. J Transp Geogr 3(3):159–166

    Article  MathSciNet  Google Scholar 

  29. Spadon G, de Carvalho AC, Rodrigues-Jr JF, Alves LG (2019) Reconstructing commuters network using machine learning and urban indicators. Sci Rep 9(1):1–13

    Article  Google Scholar 

  30. Liu Z, Miranda F, Xiong W, Yang J, Wang Q, Silva C (2020) Learning geo-contextual embeddings for commuting flow prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 808–816

  31. Yao X, Gao Y, Zhu D, Manley E, Wang J, Liu Y (2020) Spatial origin-destination flow imputation using graph convolutional networks. IEEE Trans Intell Transp Syst

  32. Ullman EL, Boyce RR (1980) Geography as spatial interaction. University of Washington Press

  33. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th international conference on neural information processing systems, pp 3844–3852

  34. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR)

  35. Wang Y, Zhu D, Yin G, Huang Z, Liu Y (2020) A unified spatial multigraph analysis for public transport performance. Sci Rep 10:9573–9581

    Article  Google Scholar 

  36. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

    Article  MathSciNet  Google Scholar 

  37. Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284–289

    Article  Google Scholar 

  38. Hirschman II, Widder DV (2012) The convolution transform. Courier Corporation

  39. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  40. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154

    Article  Google Scholar 

  41. Gong P, Chen B, Li X, Liu H, Wang J, Bai Y, Chen J, Chen X, Fang L, Feng S, et al. (2020) Mapping essential urban land use categories in China (euluc-china): Preliminary results for 2018. Sci Bull 65(3):182–187

    Article  Google Scholar 

  42. Zhu D, Zhang F, Wang S, Wang Y, Cheng X, Huang Z, Liu Y (2020) Understanding place characteristics in geographic contexts through graph convolutional neural networks. Ann Am Assoc Geogr 110(2):408–420

    Google Scholar 

  43. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  44. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794

  45. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864

  46. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  47. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710

  48. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press

  49. Li Q, Han Z, Wu X.-M. (2018a) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  50. Jenks GF (1967) The data model concept in statistical mapping. Int Yearb Cartogr 7:186–190

    Google Scholar 

Download references

Acknowledgements

We thank the editors and reviewers for their suggestions to improve the quality of this paper. This research was supported by grants from the National Natural Science Foundation of China (41830645, 41971331).

Author information

Authors and Affiliations

  1. Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, 100871, China

    Ganmin Yin, Zhou Huang, Yi Bao, Han Wang & Yi Zhang

  2. Beijing Key Lab of Spatial Information Integration & Its Applications, Peking University, Beijing, 100871, China

    Ganmin Yin, Zhou Huang, Yi Bao, Han Wang & Yi Zhang

  3. Department of Geography, California State University, Long Beach, California, USA

    Linna Li

  4. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Xiaolei Ma

Authors
  1. Ganmin Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linna Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaolei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhou Huang.

Ethics declarations

Conflict of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, G., Huang, Z., Bao, Y. et al. ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects. Geoinformatica 27, 137–157 (2023). https://doi.org/10.1007/s10707-022-00467-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-022-00467-0

Keywords

Search

Navigation