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Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

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Abstract

Accessibility is essentially a dynamic concept. However, most studies on urban accessibility take a static approach, overlooking the fact that accessibility conditions change dramatically throughout the day. Due to their high spatial and temporal resolution, the new data sources (Big Data) offer new possibilities for the study of accessibility. The aim of this paper is to analyse urban accessibility considering its two components –the performance of the transport network and the attractiveness of the destinations– using a dynamic approach using data from TomTom and Twitter respectively. This allows us to obtain profiles that highlight the daily variations in accessibility in the city of Madrid, and identify the influence of congestion and the changes in location of the population. These profiles reveal significant variations according to transport zones. Each transport zone has its own accessibility profile, and thus its own specific problems, which require solutions that are also specific.

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Notes

  1. Transport zones typically consider the following criteria: compatibility with other administrative divisions, homogeneity in their land use and/or population composition, correspondence with the natural catchment’s area of transport, etc. (Ortúzar and Willumsen 2011). Of them, the internal homogeneity within each zone is of especial interest for our study. Tweeters leave a particular temporal footprint on each land use, thus reflecting the daily variation of the use of each land use. For example, university campuses are very active in the morning, while retail and leisure areas are more active in the evening. The advantage of transport zones over other zones is that they are big enough to contain a representative number of tweeters every 15 min while preserving the above-mentioned land use homogeneity, which would become blurred in other zones (i.e. grid cells would contain a land use mixture).

  2. https://goo.gl/IT2LTq

  3. Longley et al. (2015) report that these three days of the week have a very similar tweet profile, and represent the average working day, while Monday and Friday have specific profiles influenced by the proximity of Sunday and Saturday respectively.

  4. This study uses Madrid official transport zones delimitation, which follows the criteria explained in previous footnote 1. There are 1171 transport zones in the study area.

  5. https://goo.gl/tFIPMw

  6. https://goo.gl/rDmS25

  7. https://goo.gl/8lge7M

  8. https://goo.gl/d67WcP

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Acknowledgements

The authors gratefully acknowledge funding from the ICT Theme of the European Union’s Seventh Framework Programme (INSIGHT project - Innovative Policy Modelling and Governance Tools for Sustainable Post-Crisis Urban Development, GA 611307), the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (TRA2015-65283-R and FPDI 2013/17001), and the Madrid Regional Government (SOCIALBIGDATA-CM, S2015/HUM-3427).

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Authors and Affiliations

  1. Transport, Infrastructure and Territory Research Group (t-GIS), Human Geography Department, Faculty of Geography and History, Universidad Complutense de Madrid (UCM), c/Profesor Aranguren S/N, 28040, Madrid, Spain

    Borja Moya-Gómez, María Henar Salas-Olmedo, Juan Carlos García-Palomares & Javier Gutiérrez

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  1. Borja Moya-Gómez
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  2. María Henar Salas-Olmedo
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  3. Juan Carlos García-Palomares
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Correspondence to Borja Moya-Gómez.

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Moya-Gómez, B., Salas-Olmedo, M.H., García-Palomares, J.C. et al. Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness. Netw Spat Econ 18, 273–290 (2018). https://doi.org/10.1007/s11067-017-9348-z

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