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Potential and pitfalls of big transport data for spatial interaction models of urban mobility

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  • Oshan, Taylor M.

Abstract

Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset - the New York City (NYC) taxi trip data - is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.

Suggested Citation

  • Oshan, Taylor M., 2020. "Potential and pitfalls of big transport data for spatial interaction models of urban mobility," OSF Preprints gwumt, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:gwumt
    DOI: 10.31219/osf.io/gwumt
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    References listed on IDEAS

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    1. Thomas, T. & Tutert, S.I.A., 2013. "An empirical model for trip distribution of commuters in The Netherlands: transferability in time and space reconsidered," Journal of Transport Geography, Elsevier, vol. 26(C), pages 158-165.
    2. Cabrera Delgado, Jorge & Bonnel, Patrick, 2016. "Level of aggregation of zoning and temporal transferability of the gravity distribution model: The case of Lyon," Journal of Transport Geography, Elsevier, vol. 51(C), pages 17-26.
    3. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi, 2013. "Modelling the spatial interactions between workplace and residential location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 110-122.
    4. Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
    5. Jaewon Lim & Jae Hong Kim, 2019. "Joint Determination of Residential Relocation and Commuting: A Forecasting Experiment for Sustainable Land Use and Transportation Planning," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    6. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    7. Xiang-Wen Wang & Xiao-Pu Han & Bing-Hong Wang, 2014. "Correlations and Scaling Laws in Human Mobility," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    8. Liang, Xiao & Zheng, Xudong & Lv, Weifeng & Zhu, Tongyu & Xu, Ke, 2012. "The scaling of human mobility by taxis is exponential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2135-2144.
    9. Daniel A. Griffith, 2009. "Spatial Autocorrelation in Spatial Interaction," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 221-237, Springer.
    10. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    11. Schirmer, Patrick & van Eggermond, Michael & Axhausen, Kay, 2014. "The role of location in residential location choice models: a review of literature," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 3-21.
    12. Taylor M Oshan, 2016. "A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)," REGION, European Regional Science Association, vol. 3, pages 11-23.
    13. Jens P Gitlesen & Inge Thorsen, 2000. "A Competing Destinations Approach to Modeling Commuting Flows: A Theoretical Interpretation and An Empirical Application of the Model," Environment and Planning A, , vol. 32(11), pages 2057-2074, November.
    14. John R. Roy, 2004. "Spatial Interaction Modelling," Advances in Spatial Science, Springer, number 978-3-540-24807-1, Fall.
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    Cited by:

    1. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    2. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.
    3. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.

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