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Future directions in human mobility science

Nature Computational Science volume 3, pages 588–600 (2023)Cite this article

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Abstract

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

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Fig. 1: Human mobility data sources.
Fig. 2: Summary of determinant factors and cognitive structures in spatial cognition.
Fig. 3: The complex spatiotemporal nature of multimodal transport systems.
Fig. 4: Human mobility, AI and the urban environment.

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References

  1. Ravenstein, E. G. The laws of migration. J. R. Stat. Soc. 52, 241–305 (1889).

    Google Scholar 

  2. Ravenstein, E. G. The Birthplaces of the People and the Laws of Migration (Trübner, 1876).

  3. Barbosa, H. et al. Human mobility: models and applications. Phys. Rep. 734, 1–74 (2018).

    MathSciNet  MATH  Google Scholar 

  4. Wang, J., Kong, X., Xia, F. & Sun, L. Urban human mobility: data-driven modeling and prediction. SIGKDD Explor. Newsl. 21, 1–19 (2019).

    Google Scholar 

  5. O’Dea, S. Smartphone users worldwide 2021 statista. Statista www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (2022).

  6. Alessandretti, L., Aslak, U. & Lehmann, S. The scales of human mobility. Nature 587, 402–407 (2020).

    Google Scholar 

  7. Barbosa, H., de Lima-Neto, F. B., Evsukoff, A. & Menezes, R. The effect of recency to human mobility. EPJ Data Sci. 4, 1–14 (2015).

    Google Scholar 

  8. Bongiorno, C. et al. Vector-based pedestrian navigation in cities. Nat. Comput. Sci. 1, 678–685 (2021).

    Google Scholar 

  9. Cornacchia, G. & Pappalardo, L. A mechanistic data-driven approach to synthesize human mobility considering the spatial, temporal, and social dimensions together. ISPRS Int. J. GeoInf. 10, 599 (2021).

  10. Pappalardo, L. et al. Returners and explorers dichotomy in human mobility. Nat. Commun. 6, 8166 (2015).

  11. Schläpfer, M. et al. The universal visitation law of human mobility. Nature 593, 522–527 (2021).

    Google Scholar 

  12. Alessandretti, L., Sapiezynski, P., Lehmann, S. & Baronchelli, A. Multi-scale spatio-temporal analysis of human mobility. PLoS ONE 12, 0171686 (2017).

    Google Scholar 

  13. Kraemer, M. U. et al. Mapping global variation in human mobility. Nat. Hum. Behav. 4, 800–810 (2020).

    Google Scholar 

  14. Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C. A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7, 37027 (2012).

    Google Scholar 

  15. Burbey, I. & Martin, T. L. A survey on predicting personal mobility. Int. J. Pervasive Comput. Commun. 8, 5–22 (2012).

    Google Scholar 

  16. Calabrese, F., Di Lorenzo, G. & Ratti, C. Human mobility prediction based on individual and collective geographical preferences. In 13th International Conference on Intelligent Transportation Systems 312–317 (IEEE, 2010).

  17. Dai, J., Yang, B., Guo, C. & Ding, Z. Personalized route recommendation using big trajectory data. In 31st International Conference on Data Engineering 543–554 (IEEE, 2015).

  18. Luca, M., Barlacchi, G., Lepri, B. & Pappalardo, L. A survey on deep learning for human mobility. ACM Comput. Surv. 55, 1–44 (2021).

    Google Scholar 

  19. Pappalardo, L. & Simini, F. Data-driven generation of spatio-temporal routines in human mobility. Data Min. Knowl. Discov. 32, 787–829 (2018).

  20. Simini, F., Barlacchi, G., Luca, M. & Pappalardo, L. A deep gravity model for mobility flows generation. Nat. Commun. 12, 6576 (2021).

    Google Scholar 

  21. Zheng, Y. Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6, 1–41 (2015).

    Google Scholar 

  22. Zheng, Y., Capra, L., Wolfson, O. & Yang, H. Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5, 1–55 (2014).

    Google Scholar 

  23. Pappalardo, L., Ferres, L., Sacasa, M., Cattuto, C. & Bravo, L. Evaluation of home detection algorithms on mobile phone data using individual-level ground truth. EPJ Data Sci. 10, 29 (2021).

    Google Scholar 

  24. Fudolig, M. I. D., Monsivais, D., Bhattacharya, K., Jo, H.-H. & Kaski, K. Internal migration and mobile communication patterns among pairs with strong ties. EPJ Data Sci. 10, 16 (2021).

    Google Scholar 

  25. Moro, E., Calacci, D., Dong, X. & Pentland, A. Mobility patterns are associated with experienced income segregation in large US cities. Nat. Commun. 12, 4633 (2021).

    Google Scholar 

  26. Pappalardo, L. et al. An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal. 2, 75–92 (2016).

    Google Scholar 

  27. Xue, H., Voutharoja, B. P. & Salim, F. D. Leveraging language foundation models for human mobility forecasting. In Proc. 30th International Conference on Advances in Geographic Information Systems Vol. 90, 1–9 (ACM, 2022).

  28. Aleta, A. et al. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proc. Natl Acad. Sci. USA 119, 2112182119 (2022).

    Google Scholar 

  29. Lucchini, L. et al. Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures. Sci. Rep. 11, 24452 (2021).

    Google Scholar 

  30. Borst, H. C. et al. Influence of environmental street characteristics on walking route choice of elderly people. J. Environ. Psychol. 29, 477–484 (2009).

    Google Scholar 

  31. Coutrot, A. et al. Entropy of city street networks linked to future spatial navigation ability. Nature 604, 104–110 (2022).

    Google Scholar 

  32. Gauvin, L. et al. Gender gaps in urban mobility. Humanit. Soc. Sci. Commun. 7, 11 (2020).

    Google Scholar 

  33. Macedo, M., Lotero, L., Cardillo, A., Menezes, R. & Barbosa, H. Differences in the spatial landscape of urban mobility: gender and socioeconomic perspectives. PLoS ONE 17, 0260874 (2022).

    Google Scholar 

  34. Barbosa, H. et al. Uncovering the socioeconomic facets of human mobility. Sci. Rep. 11, 8616 (2021).

    Google Scholar 

  35. Gonzalez, M. C., Hidalgo, C. A. & Barabasi, A.-L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008).

    Google Scholar 

  36. Gallotti, R., Bazzani, A., Rambaldi, S. & Barthelemy, M. A stochastic model of randomly accelerated walkers for human mobility. Nat. Commun. 7, 12600 (2016).

    Google Scholar 

  37. Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D. & Giannotti, F. Understanding the patterns of car travel. Eur. Phys. J. Spec. Top. 215, 61–73 (2013).

    Google Scholar 

  38. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327, 1018–1021 (2010).

    MathSciNet  MATH  Google Scholar 

  39. Scherrer, L., Tomko, M., Ranacher, P. & Weibel, R. Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth. EPJ Data Sci. 7, 1–21 (2018).

    Google Scholar 

  40. Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S. & Baronchelli, A. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav. 2, 485–491 (2018).

    Google Scholar 

  41. Barbosa, H., de Lima-Neto, F. B., Evsukoff, A. & Menezes, R. The effect of recency to human mobility. EPJ Data Sci. 4, 1–14 (2015).

    Google Scholar 

  42. Song, C., Koren, T., Wang, P. & Barabási, A.-L. Modelling the scaling properties of human mobility. Nat. Phys. 6, 818–823 (2010).

    Google Scholar 

  43. Lu, X., Wetter, E., Bharti, N., Tatem, A. J. & Bengtsson, L. Approaching the limit of predictability in human mobility. Sci. Rep. 3, 2923 (2013).

    Google Scholar 

  44. Smith, G., Wieser, R., Goulding, J. & Barrack, D. A refined limit on the predictability of human mobility. In International Conference on Pervasive Computing and Communications 88–94 (IEEE, 2014).

  45. Cuttone, A., Lehmann, S. & González, M. C. Understanding predictability and exploration in human mobility. EPJ Data Sci. 7, 1–17 (2018).

    Google Scholar 

  46. Ikanovic, E. L. & Mollgaard, A. An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6, 1–10 (2017).

    Google Scholar 

  47. Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z. & González, M. C. Unravelling daily human mobility motifs. J. R. Soc. Interface 10, 20130246 (2013).

    Google Scholar 

  48. Yan, X.-Y., Wang, W.-X., Gao, Z.-Y. & Lai, Y.-C. Universal model of individual and population mobility on diverse spatial scales. Nat. Commun. 8, 1639 (2017).

    Google Scholar 

  49. Simini, F., González, M. C., Maritan, A. & Barabási, A.-L. A universal model for mobility and migration patterns. Nature 484, 96–100 (2012).

    Google Scholar 

  50. Tversky, B. Cognitive maps, cognitive collages, and spatial mental models. In European Conference on Spatial Information Theory 14–24 (Springer, 1993).

  51. Sadalla, E. K., Burroughs, J. W. & Staplin, L. J. Reference points in spatial cognition. J. Exp. Psychol. Hum. Learn. Mem. 6, 516–528 (1980).

    Google Scholar 

  52. Weisberg, S. M. & Newcombe, N. S. How do (some) people make a cognitive map? Routes, places, and working memory. J. Exp. Psychol. Learn. Mem. Cogn. 42, 768 (2016).

    Google Scholar 

  53. Newcombe, N. & Liben, L. S. Barrier effects in the cognitive maps of children and adults. J. Exp. Child Psychol. 34, 46–58 (1982).

    Google Scholar 

  54. Moser, E. I., Kropff, E. & Moser, M.-B. et al. Place cells, grid cells, and the brain’s spatial representation system. Annu. Rev. Neurosci. 31, 69–89 (2008).

    Google Scholar 

  55. Hirtle, S. C. & Jonides, J. Evidence of hierarchies in cognitive maps. Mem. Cogn. 13, 208–217 (1985).

    Google Scholar 

  56. Balaguer, J., Spiers, H., Hassabis, D. & Summerfield, C. Neural mechanisms of hierarchical planning in a virtual subway network. Neuron 90, 893–903 (2016).

    Google Scholar 

  57. Lynch, K. The Image of the City Vol. 1 (MIT Press, 1960).

  58. Filomena, G., Verstegen, J. A. & Manley, E. A computational approach to ‘The Image of the City’. Cities 89, 14–25 (2019).

    Google Scholar 

  59. Winter, S., Tomko, M., Elias, B. & Sester, M. Landmark hierarchies in context. Environ. Plann. B 35, 381–398 (2008).

    Google Scholar 

  60. Kuipers, B. Modeling spatial knowledge. Cogn. Sci. 2, 129–153 (1978).

    Google Scholar 

  61. Chown, E., Kaplan, S. & Kortenkamp, D. Prototypes, location, and associative networks (PLAN): towards a unified theory of cognitive mapping. Cogn. Sci. 19, 1–51 (1995).

    Google Scholar 

  62. Manley, E., Filomena, G. & Mavros, P. A spatial model of cognitive distance in cities. Int. J. Geogr. Inf. Sci. 35, 2316–2338 (2021).

    Google Scholar 

  63. Ben-Akiva, M., Bergman, M., Daly, A. J. & Ramaswamy, R. Modelling inter urban route choice behaviour. In Papers Presented During the Ninth International Symposium on Transportation and Traffic Theory 299–330 (VNU Science Press, 1984).

  64. Manley, E. J., Addison, J. D. & Cheng, T. Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London. J. Transp. Geogr. 43, 123–139 (2015).

    Google Scholar 

  65. Malleson, N. et al. The characteristics of asymmetric pedestrian behavior: a preliminary study using passive smartphone location data. Trans. GIS 22, 616–634 (2018).

    Google Scholar 

  66. Lima, A., Stanojevic, R., Papagiannaki, D., Rodriguez, P. & González, M. C. Understanding individual routing behaviour. J. R. Soc. Interface 13, 20160021 (2016).

    Google Scholar 

  67. Sevtsuk, A. & Basu, R. The role of turns in pedestrian route choice: a clarification. J. Transp. Geogr. 102, 103392 (2022).

    Google Scholar 

  68. Miranda, A. S., Fan, Z., Duarte, F. & Ratti, C. Desirable streets: using deviations in pedestrian trajectories to measure the value of the built environment. Comput. Environ. Urban Syst. 86, 101563 (2021).

    Google Scholar 

  69. Guo, Z. & Loo, B. P. Pedestrian environment and route choice: evidence from New York City And Hong Kong. J. Transp. Geogr. 28, 124–136 (2013).

    Google Scholar 

  70. Gilbert, N. et al. (eds) Tools and Techniques for Social Science Simulation 83–114 (Springer, 2000).

  71. Georgeff, M., Pell, B., Pollack, M., Tambe, M. & Wooldridge, M. The belief-desire-intention model of agency. In Proc. Intelligent Agents V: Agents Theories, Architectures, and Languages: 5th International Workshop 1–10 (Springer, 1999).

  72. Manley, E., Orr, S. W. & Cheng, T. A heuristic model of bounded route choice in urban areas. Transp. Res. Part C 56, 195–209 (2015).

    Google Scholar 

  73. Aboutaleb, Y. M., Danaf, M., Xie, Y. & Ben-Akiva, M. Discrete choice analysis with machine learning capabilities. Preprint at https://arxiv.org/abs/2101.10261 (2021).

  74. Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).

  75. Mirowski, P. et al. Learning to navigate in cities without a map. In Advances in Neural Information Processing Systems Vol. 31, 2424–2435 (Curran Associates, 2018).

  76. Hancock, T. O. & Choudhury, C. F. Utilising physiological data for augmenting travel choice models: methodological frameworks and directions of future research. Transp. Rev. https://doi.org/10.1080/01441647.2023.2175274 (2023).

  77. Qin, T., Dong, W. & Huang, H. Perceptions of space and time of public transport travel associated with human brain activities: a case study of bus travel in Beijing. Comput. Environ. Urban Syst. 99, 101919 (2023).

    Google Scholar 

  78. Coutrot, A. et al. Global determinants of navigation ability. Curr. Biol. 28, 2861–28664 (2018).

    Google Scholar 

  79. Montello, D. R. in Spatial and Temporal Reasoning in Geographic Information Systems 143–154 (Oxford Univ. Press, 1998).

  80. Mavros, P. et al. Collaborative wayfinding under distributed spatial knowledge. In 15th International Conference on Spatial Information Theory: Leibniz International Proceedings in Informatics Vol. 240 (eds Ishikawa, T. et al.) 25:1–25:10 (Schloss Dagstuhl, Leibniz-Zentrum für Informatik, 2022).

  81. Spiers, H. J. & Maguire, E. A. The dynamic nature of cognition during wayfinding. J. Environ. Psychol. 28, 232–249 (2008).

    Google Scholar 

  82. Ishikawa, T., Fujiwara, H., Imai, O. & Okabe, A. Wayfinding with a GPS-based mobile navigation system: a comparison with maps and direct experience. J. Environ. Psychol. 28, 74–82 (2008).

    Google Scholar 

  83. Gardony, A. L., Brunyé, T. T., Mahoney, C. R. & Taylor, H. A. How navigational aids impair spatial memory: evidence for divided attention. Spat. Cogn. Comput. 13, 319–350 (2013).

    Google Scholar 

  84. Alessandretti, L., Natera Orozco, L. G., Battiston, F., Saberi, M. & Szell, M. Multimodal urban mobility and multilayer transport networks. Environ. Plann. B. https://doi.org/10.1177/23998083221108190 (2022).

  85. Mohamed, K., Côme, E., Oukhellou, L. & Verleysen, M. Clustering smart card data for urban mobility analysis. IEEE Trans. Intell.Transp. Syst. 18, 712–728 (2016).

    Google Scholar 

  86. Zhong, C., Manley, E., Arisona, S. M., Batty, M. & Schmitt, G. Measuring variability of mobility patterns from multiday smart-card data. J. Comput. Sci. 9, 125–130 (2015).

    Google Scholar 

  87. Zhong, C. et al. Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS ONE 11, 0149222 (2016).

    Google Scholar 

  88. Nations, U. World Urbanization Prospects (United Nations, 2014)

  89. Pelaez Bueno, A. Identifying and Quantifying Mobility Hubs MSc thesis. ETH Zurich (2021).

  90. Wirtz, M. H. & Klähr, J. Smartphone based in/out ticketing systems: a new generation of ticketing in public transport and its performance testing. WIT Trans. Built Environ. 182, 351–359 (2019).

    Google Scholar 

  91. Baldauf, M. & Tomitsch, M. Pervasive displays for public transport: an overview of ubiquitous interactive passenger services. In Proc. 9th ACM International Symposium on Pervasive Displays 37–45 (Association for Computing Machinery, 2020).

  92. Nahmias-Biran, B.-h et al. Enriching activity-based models using smartphone-based travel surveys. Transp. Res. Rec. 2672, 280–291 (2018).

    Google Scholar 

  93. Amini, A., Kung, K., Kang, C., Sobolevsky, S. & Ratti, C. The impact of social segregation on human mobility in developing and industrialized regions. EPJ Data Sci. 3, 1–20 (2014).

    Google Scholar 

  94. Crawford, F. W. et al. Impact of close interpersonal contact on COVID-19 incidence: evidence from 1 year of mobile device data. Sci. Adv. 8, 5499 (2022).

    Google Scholar 

  95. Machado, C. A. S., de Salles Hue, N. P. M., Berssaneti, F. T. & Quintanilha, J. A. An overview of shared mobility. Sustainability 10, 4342 (2018).

    Google Scholar 

  96. McNally, M. G. in Handbook of Transport Modelling (Emerald Group, 2007).

  97. Axhausen, K. W., Horni, A. & Nagel, K. The Multi-agent Transport Simulation MATSim (Ubiquity Press, 2016).

  98. Lu, N., Cheng, N., Zhang, N., Shen, X. & Mark, J. W. Connected vehicles: solutions and challenges. IEEE Internet Things J. 1, 289–299 (2014).

    Google Scholar 

  99. Ward, J. A., Evans, A. J. & Malleson, N. S. Dynamic calibration of agent-based models using data assimilation. R. Soc. Open Sci. 3, 150703 (2016).

    MathSciNet  Google Scholar 

  100. Morandi, V. Bridging the user equilibrium and the system optimum in static traffic assignment: a review. 4OR https://doi.org/10.1007/s10288-023-00540-w (2023).

  101. Sejnowski, T. J. The Deep Learning Revolution (MIT Press, 2018).

  102. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Google Scholar 

  103. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    Google Scholar 

  104. Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).

    Google Scholar 

  105. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Google Scholar 

  106. Wu, Y. et al. Google’s neural machine translation system: bridging the gap between human and machine translation. Preprint at https://arxiv.org/abs/1609.08144 (2016).

  107. Zheng, X., Han, J. & Sun, A. A survey of location prediction on Twitter. IEEE Trans. Knowl. Data Eng. 30, 1652–1671 (2018).

    Google Scholar 

  108. Rumbert, D. E. Learning internal representations by error propagation. Parallel Distrib. Process. 1, 318–363 (1986).

    Google Scholar 

  109. Jiang, S. et al. The TimeGeo modeling framework for urban mobility without travel surveys. Proc. Natl Acad. Sci. USA 113, 5370–5378 (2016).

    Google Scholar 

  110. Toole, J. L., Herrera-Yaqüe, C., Schneider, C. M. & González, M. C. Coupling human mobility and social ties. J. R. Soc. Interface 12, 20141128 (2015).

    Google Scholar 

  111. Zipf, G. K. The P1P2/D hypothesis: on the intercity movement of persons. Am. Sociol. Rev. 11, 677–686 (1946).

    Google Scholar 

  112. Liu, E.-J. & Yan, X.-Y. A universal opportunity model for human mobility. Sci. Rep. 10, 4657 (2020).

    Google Scholar 

  113. Goodfellow, I. et al. Generative adversarial nets. In Advances in Neural Information Processing Systems 27 (NIPS, 2014)

  114. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114 (2013).

  115. Mauro, G., Luca, M., Longa, A., Lepri, B. & Pappalardo, L. Generating mobility networks with generative adversarial networks. EPJ Data Sci. 11, 58 (2022).

    Google Scholar 

  116. Bao, H., Zhou, X., Xie, Y., Zhang, Y. & Li, Y. COVID-GAN+: estimating human mobility responses to COVID-19 through spatio-temporal generative adversarial networks with enhanced features. ACM Trans. Intell. Syst. Technol. https://doi.org/10.1145/3481617 (2022).

  117. Feng, J. et al. Learning to simulate human mobility. In Proc. 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 3426–3433 (ACM, 2020)

  118. Huang, D. et al. A variational autoencoder based generative model of urban human mobility. In Conference on Multimedia Information Processing and Retrieval 425–430 (IEEE, 2019).

  119. Ouyang, K., Shokri, R., Rosenblum, D. S. & Yang, W. A non-parametric generative model for human trajectories. In Proc. Twenty-Seventh International Joint Conference on Artificial Intelligence Vol. 18, 3812–3817 (AAAI, 2018).

  120. Amatriain, X. Transformer models: an introduction and catalog. Preprint at https://arxiv.org/abs/2302.07730 (2023).

  121. Mizuno, T., Fujimoto, S. & Ishikawa, A. Generation of individual daily trajectories by GPT-2. Front. Phys. 10, 1118 (2022).

    Google Scholar 

  122. Ma, J. et al. Human trajectory completion with transformers. In International Conference on Communications 3346–3351 (IEEE, 2022).

  123. Guidotti, R. et al. A survey of methods for explaining black box models. ACM Comput. Surv. 51, 1–42 (2018).

    Google Scholar 

  124. Naretto, F., Pellungrini, R., Monreale, A., Nardini, F. M. & Musolesi, M. in Discovery Science. DS 2020. Lecture Notes in Computer Science Vol. 12323 (eds Appice, A. et al.) 403–418 (Springer, 2020).

  125. Huang, X. & Marques-Silva, J. The inadequacy of shapley values for explainability. Preprint at https://arxiv.org/abs/2302.08160 (2023).

  126. Kumar, I. E., Venkatasubramanian, S., Scheidegger, C. & Friedler, S. Problems with shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning 5491–5500 (PMLR. 2020).

  127. Jonietz, D. et al. Urban mobility analytics: report from Dagstuhl seminar 22162. Dagstuhl Rep. 12, 26–53 (2022).

    Google Scholar 

  128. Rolnick, D. et al. Tackling climate change with machine learning. ACM Comput. Surv. 55, 1–96 (2022).

    Google Scholar 

  129. Bai, X. et al. Six research priorities for cities and climate change. Nature 555, 23–25 (2018).

    Google Scholar 

  130. Voukelatou, V. et al. Measuring objective and subjective well-being: dimensions and data sources. Int. J. Data Sci. Anal. 11, 279–309 (2021).

    Google Scholar 

  131. Yan, A. & Howe, B. Fairness-aware demand prediction for new mobility. In Proc. AAAI Conference on Artificial Intelligence Vol. 34, 1079–1087 (AAAI, 2020).

  132. Yan, A. & Howe, B. FairST: equitable spatial and temporal demand prediction for new mobility systems. In Proc. 27th International Conference on Advances in Geographic Information Systems 552–555 (ACM, 2019).

  133. Yan, A. & Howe, B. Fairness in practice: a survey on equity in urban mobility. Q. Bull. Comput. Soc. IEEE 42, 49–63 (2019).

    Google Scholar 

  134. Ngo, N. S., Götschi, T. & Clark, B. Y. The effects of ride-hailing services on bus ridership in a medium-sized urban area using micro-level data: evidence from the Lane Transit District. Transp. Policy 105, 44–53 (2021).

    Google Scholar 

  135. Ge, Y., Knittel, C. R., MacKenzie, D. & Zoepf, S. Racial and Gender Discrimination in Transportation Network Companies Technical Report (National Bureau of Economic Research, 2016).

  136. Arora, N. et al. Quantifying the sustainability impact of Google Maps: a case study of Salt Lake City. Preprint at https://arxiv.org/abs/2111.03426 (2021).

  137. Perez-Prada, F., Monzon, A. & Valdes, C. Managing traffic flows for cleaner cities: the role of green navigation systems. Energies 10, 791 (2017).

    Google Scholar 

  138. Mehrvarz, N., Ye, Z., Barati, K. & Shen, X. Optimal Travel Routes of On-road Vehicles Considering Sustainability (IAARC Publications, 2020).

  139. Cornacchia, G. et al. How routing strategies impact urban emissions. In Proc. 30th International Conference on Advances in Geographic Information Systems https://doi.org/10.1145/3557915.3560977 (ACM, 2022).

  140. Lai, S. et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature 585, 410–413 (2020).

    Google Scholar 

  141. Haushofer, J. & Metcalf, C. J. E. Which interventions work best in a pandemic? Science 368, 1063–1065 (2020).

    Google Scholar 

  142. Gao, S. et al. Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic. Preprint at https://arxiv.org/abs/2004.11430 (2020).

  143. Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368, 395–400 (2020).

    Google Scholar 

  144. Gatto, M. et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc. Natl Acad. Sci. USA 117, 10484–10491 (2020).

    Google Scholar 

  145. Jia, J. S. et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582, 389–394 (2020).

  146. Tian, H. et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368, 638–642 (2020).

    Google Scholar 

  147. Ross, S., Breckenridge, G., Zhuang, M. & Manley, E. Household visitation during the COVID-19 pandemic. Sci. Rep. 11, 22871 (2021).

    Google Scholar 

  148. Pappalardo, L., Cornacchia, G., Navarro, V., Bravo, L. & Ferres, L. A dataset to assess mobility changes in Chile following local quarantines. Sci. Data 10, 6 (2023).

    Google Scholar 

  149. Ritchie, H. Sector by sector: where do global greenhouse gas emissions come from? Our World in Data https://ourworldindata.org/ghg-emissions-by-sector (2020).

  150. Transforming Our World: the 2030 Agenda for Sustainable Development Technical Report (United Nations General Assembly, 2015).

  151. Böhm, M., Nanni, M. & Pappalardo, L. Gross polluters and vehicle emissions reduction. Nat. Sustain. https://doi.org/10.1038/s41893-022-00903-x (2022).

  152. Nyhan, M. et al. Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model. Atmos. Environ. 140, 352–363 (2016).

    Google Scholar 

  153. Macfarlane, J. When apps rule the road: the proliferation of navigation apps is causing traffic chaos. It’s time to restore order. IEEE Spectrum 56, 22–27 (2019).

    Google Scholar 

  154. Lu, Y., Nakicenovic, N., Visbeck, M. & Stevance, A.-S. Policy: Five priorities for the unsustainable development goals. Nature 520, 432–433 (2015).

    Google Scholar 

  155. Glaeser, E. L., Resseger, M. & Tobio, K. Inequality in cities. J. Reg. Sci. 49, 617–646 (2009).

    Google Scholar 

  156. Sanchez, T. W., Stolz, R. & Ma, J. S. Inequitable effects of transportation policies on minorities. Transp. Res. Rec. 1885, 104–110 (2004).

    Google Scholar 

  157. Smith, D. A. & Barros, J. in Urban Form and Accessibility (Mulley, C. & Nelson, J. D.) 27–44 (Elsevier, 2021).

  158. Carpio-Pinedo, J. Multimodal transport and potential encounters with social difference: a novel approach based on network analysis. J. Urban Aff. 43, 93–116 (2021).

    Google Scholar 

  159. Gillies, S. et al. Shapely: manipulation and analysis of geometric objects. GitHub https://github.com/Toblerity/Shapely (2007).

  160. Sekara, V. et al. in Guide to Mobile Data Analytics in Refugee Scenarios 53–66 (2019).

  161. Schlosser, F., Sekara, V., Brockmann, D. & Garcia-Herranz, M. Biases in human mobility data impact epidemic modeling. Preprint at https://arxiv.org/abs/2112.12521 (2021).

  162. Zhao, Z. et al. Understanding the bias of call detail records in human mobility research. Int. J. Geogr. Inf. Sci. 30, 1738–1762 (2016).

    Google Scholar 

  163. Blumenstock, J. & Eagle, N. Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda. In Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development 1–10 (ACM/IEEE, 2010)

  164. Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W. & Buckee, C. O. Heterogeneous mobile phone ownership and usage patterns in kenya. PLoS ONE 7, 35319 (2012).

    Google Scholar 

  165. Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W. & Buckee, C. O. The impact of biases in mobile phone ownership on estimates of human mobility. J. R. Soc. Interface 10, 20120986 (2013).

    Google Scholar 

  166. I.T.U.: World Telecommunication/ICT Indicators Database (ITU, 2022).

  167. Leo, Y., Fleury, E., Alvarez-Hamelin, J. I., Sarraute, C. & Karsai, M. Socioeconomic correlations and stratification in social-communication networks. J. R. Soc. Interface 13, 20160598 (2016).

    Google Scholar 

  168. Ricciato, F. et al. Estimating Population Density Distribution from Network-based Mobile Phone Data (Publications Office of the European Union, 2015).

  169. Acosta, R. J., Kishore, N., Irizarry, R. A. & Buckee, C. O. Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico. Proc. Natl Acad. Sci. USA 117, 32772–32778 (2020).

    Google Scholar 

  170. Salganik, M. J. Bit by Bit: Social Research in the Digital Age (Princeton Univ. Press, 2019).

  171. Tizzoni, M. et al. On the use of human mobility proxies for modeling epidemics. PLoS Comput. Biol. 10, 1003716 (2014).

    Google Scholar 

  172. Pestre, G., Letouzé, E. & Zagheni, E. The ABCDE of big data: assessing biases in call-detail records for development estimates. World Bank Econ. Rev. 34, 89–97 (2020).

    Google Scholar 

  173. Blondel, V. D., Decuyper, A. & Krings, G. A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 10 (2015).

    Google Scholar 

  174. De Montjoye, Y.-A. et al. On the privacy-conscientious use of mobile phone data. Sci. Data 5, 180286 (2018).

    Google Scholar 

  175. Rzeszewski, M. & Luczys, P. Care, indifference and anxiety-attitudes toward location data in everyday life. ISPRS Int. J. GeoInf. 7, 383 (2018).

    Google Scholar 

  176. Gerber, N., Gerber, P. & Volkamer, M. Explaining the privacy paradox: a systematic review of literature investigating privacy attitude and behavior. Comput. Secur. 77, 226–261 (2018).

    Google Scholar 

  177. Taylor, L. No place to hide? The ethics and analytics of tracking mobility using mobile phone data. Environ. Plann. D 34, 319–336 (2016).

    Google Scholar 

  178. Maxmen, A. Surveillance science. Nature 569, 614–617 (2019).

    Google Scholar 

  179. Stadler, J. et al. Cognitive mapping: using local knowledge for planning health research. BMC Med. Res. Methodol. 13, 96 (2013).

    Google Scholar 

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Acknowledgements

L.P. thanks G. Cornacchia and G. Mauro for their valuable suggestions and D. Fadda for his precious support in visualizations. L.P. has been supported by EU projects: (1) H2020 HumaneAI-net G.A. 952026; (2) H2020 SoBigData++ G.A. #871042; (3) NextGenerationEU - National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR), project ‘SoBigData.it - Strengthening the Italian RI for Social Mining and Big Data Analytics’, prot. IR0000013, avviso n. 3264 on 28/12/2021. V.S. is supported by Digital Research Center Denmark (DIREC) grant P25—Understanding Biases and Diversity of Big Data used for Mobility Analysis. E.M. is supported by UK Research and Innovation (UKRI) through the Consumer Data Research Centre (ES/L011891/1) and Responsible Automation for Inclusive Mobility (ES/T012587/1) projects.

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Authors and Affiliations

  1. Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy

    Luca Pappalardo

  2. School of Geography, University of Leeds, Leeds, UK

    Ed Manley

  3. Leeds Institute for Data Analytics, University of Leeds, Leeds, UK

    Ed Manley

  4. IT University of Copenhagen, Copenhagen, Denmark

    Vedran Sekara

  5. DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark

    Laura Alessandretti

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L.A. and L.P. conceived the overall structure of the paper. All authors wrote the paper, reviewed the literature, contributed to figures and approved the final version of the paper. L.A. coordinated the work.

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Pappalardo, L., Manley, E., Sekara, V. et al. Future directions in human mobility science. Nat Comput Sci 3, 588–600 (2023). https://doi.org/10.1038/s43588-023-00469-4

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