2020
Conference article
Open Access
Learning mobility flows from urban features with spatial interaction models and neural networks
Yeghikyan G., Opolka F. L., Nanni M., Lepri B., Lio P.
Computer Science - Social and Information Networks Traffic prediction FOS: Computer and information sciences Network analysis Physics - Physics and Society Social and Information Networks (cs.SI) Mobility analytics FOS: Physical sciences Physics and Society (physics.soc-ph)
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London.
Source: SMARTCOMP 2020 - IEEE International Conference on Smart Computing, pp. 57–64, Bologna, Italy, September 14-17, 2020
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[2] A. S. Fotheringham and M. E. O'Kelly, Spatial interaction models: formulations and applications, vol. 1. Kluwer Academic Publishers Dordrecht, 1989.
[3] J. P. LeSage and R. K. Pace, “Spatial econometric modeling of origindestination flows,” Journal of Regional Science, vol. 48, no. 5, pp. 941- 967, 2008.
[4] F. Simini, M. C. Gonza´lez, A. Maritan, and A.-L. Baraba´si, “A universal model for mobility and migration patterns,” Nature, vol. 484, no. 7392, p. 96, 2012.
[5] D. Chai, L. Wang, and Q. Yang, “Bike flow prediction with multi-graph convolutional networks,” in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 397-400, ACM, 2018.
[6] F. Toque´, E. Coˆme, M. K. El Mahrsi, and L. Oukhellou, “Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1071- 1076, IEEE, 2016.
[7] D. L. Huff, “A probabilistic analysis of shopping center trade areas,” Land economics, vol. 39, no. 1, pp. 81-90, 1963.
[8] S. Erlander and N. F. Stewart, The gravity model in transportation analysis: theory and extensions, vol. 3. Vsp, 1990.
[9] J. de Dios Ortuzar and L. G. Willumsen, Modelling transport. John wiley & sons, 2011.
[10] D. P. McArthur, G. Kleppe, I. Thorsen, and J. Ubøe, “The spatial transferability of parameters in a gravity model of commuting flows,” Journal of Transport Geography, vol. 19, no. 4, pp. 596-605, 2011.
[11] R. Patuelli, A. Reggiani, S. P. Gorman, P. Nijkamp, and F.-J. Bade, “Network analysis of commuting flows: A comparative static approach to german data,” Networks and Spatial Economics, vol. 7, no. 4, pp. 315- 331, 2007.
[12] L. Zhang, J. Cheng, and C. Jin, “Spatial interaction modeling of od flow data: Comparing geographically weighted negative binomial regression (gwnbr) and ols (gwolsr),” ISPRS International Journal of Geo-Information, vol. 8, no. 5, p. 220, 2019.
[13] A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003.
[14] A. R. da Silva and T. C. V. Rodrigues, “Geographically weighted negative binomial regressionincorporating overdispersion,” Statistics and Computing, vol. 24, no. 5, pp. 769-783, 2014.
[15] P. Davis, “Spatial competition in retail markets: movie theaters,” The RAND Journal of Economics, vol. 37, no. 4, pp. 964-982, 2006.
[16] G. Bruno and G. Improta, “Using gravity models for the evaluation of new university site locations: A case study,” Computers & Operations Research, vol. 35, no. 2, pp. 436-444, 2008.
[17] N. Wan, B. Zou, and T. Sternberg, “A three-step floating catchment area method for analyzing spatial access to health services,” International Journal of Geographical Information Science, vol. 26, no. 6, pp. 1073- 1089, 2012.
[18] A. S. Fotheringham, “A new set of spatial-interaction models: the theory of competing destinations,” Environment and Planning A: Economy and Space, vol. 15, no. 1, pp. 15-36, 1983.
[19] M. De Beule, D. Van den Poel, and N. Van de Weghe, “An extended huff-model for robustly benchmarking and predicting retail network performance,” Applied Geography, vol. 46, pp. 80-89, 2014.
[20] Y. Li and L. Liu, “Assessing the impact of retail location on store performance: A comparison of wal-mart and kmart stores in cincinnati,” Applied Geography, vol. 32, no. 2, pp. 591-600, 2012.
[21] A. P. Masucci, J. Serras, A. Johansson, and M. Batty, “Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows,” Physical Review E, vol. 88, no. 2, p. 022812, 2013.
[22] X. Liang, J. Zhao, L. Dong, and K. Xu, “Unraveling the origin of exponential law in intra-urban human mobility,” Scientific reports, vol. 3, p. 2983, 2013.
[23] X.-Y. Yan, C. Zhao, Y. Fan, Z. Di, and W.-X. Wang, “Universal predictability of mobility patterns in cities,” Journal of The Royal Society Interface, vol. 11, no. 100, p. 20140834, 2014.
[24] G. Spadon, A. C. de Carvalho, J. F. Rodrigues-Jr, and L. G. Alves, “Reconstructing commuters network using machine learning and urban indicators,” Scientific reports, vol. 9, no. 1, pp. 1-13, 2019.
[25] M. Lorenzo and M. Matteo, “Od matrices network estimation from link counts by neural networks,” Journal of Transportation Systems Engineering and Information Technology, vol. 13, no. 4, pp. 84-92, 2013.
[26] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” Trans. Neur. Netw., vol. 20, no. 1, p. 6180, 2009.
[27] J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral networks and locally connected networks on graphs,” in International Conference on Learning Representations (ICLR2014), CBLS, April 2014, 2014.
[28] M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS16, (Red Hook, NY, USA), p. 38443852, Curran Associates Inc., 2016.
[29] T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in Proceedings of the 5th International Conference on Learning Representations, ICLR '17, 2017.
[30] X. Wang, Z. Zhou, F. Xiao, K. Xing, Z. Yang, Y. Liu, and C. Peng, “Spatio-temporal analysis and prediction of cellular traffic in metropolis,” IEEE Transactions on Mobile Computing, vol. 18, no. 9, pp. 2190- 2202, 2019.
[31] D. Zhu and Y. Liu, “Modelling spatial patterns using graph convolutional networks,” in 10th International Conference on Geographic Information Science (GIScience 2018), 2018.
[32] P. Xie, T. Li, J. Liu, D. Shengdong, Y. Xin, and J. Zhang, “Urban flows prediction from spatial-temporal data using machine learning: A survey,” 2019. arXiv preprint arXiv: 1908.10218.
[33] OpenStreetMap contributors, “Planet dump retrieved from https://planet.osm.org .” https://www.openstreetmap.org, 2017.
[34] Murray Cox, “Inside Airbnb retrieved from http://insideairbnb.com/getthe-data.html .” http://insideairbnb.com/get-the-data.html, 2019.
[35] Transport for London, “Open data retrieved from https://tfl.gov.uk/infofor/open-data-users/ .” https://tfl.gov.uk/info-for/open-data-users/, 2019.
[36] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML15, p. 448456, JMLR.org, 2015.
[37] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, p. 19291958, 2014.
[38] M. Lenormand, S. Huet, F. Gargiulo, and G. Deffuant, “A universal model of commuting networks,” PloS one, vol. 7, no. 10, 2012.
[39] M. Lenormand, A. Bassolas, and J. J. Ramasco, “Systematic comparison of trip distribution laws and models,” Journal of Transport Geography, vol. 51, pp. 158-169, 2016.
[40] G. Casiraghi, “Multiplex network regression: How do relations drive interactions?,” arXiv preprint arXiv:1702.02048, 2017.
[41] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, 2016.
[42] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (Y. Bengio and Y. LeCun, eds.), 2015.
[43] P. Velikovi, G. Cucurull, A. Casanova, A. Romero, P. Li, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations, 2018.
[44] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?,” in International Conference on Learning Representations, 2019.
[45] K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in Proceedings of the 35th International Conference on Machine Learning (J. Dy and A. Krause, eds.), vol. 80 of Proceedings of Machine Learning Research, pp. 5453-5462, 2018.
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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:447162,
title = {Learning mobility flows from urban features with spatial interaction models and neural networks},
author = {Yeghikyan G. and Opolka F. L. and Nanni M. and Lepri B. and Lio P.},
doi = {10.1109/smartcomp50058.2020.00028 and 10.48550/arxiv.2004.11924},
booktitle = {SMARTCOMP 2020 - IEEE International Conference on Smart Computing, pp. 57–64, Bologna, Italy, September 14-17, 2020},
year = {2020}
}CNR authorsLaboratories
0000-0003-3534-4332CNR ExploRA
ISTI Repository
DOI
ISTI Repository
DOI
10.1109/smartcomp50058.2020.00028
10.48550/arxiv.2004.11924
Track and Know
Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
Track and Know
Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
