Networks and Spatial Economics

, Volume 18, Issue 2, pp 273–290 | Cite as

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

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


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.


Time-sensitive accessibility Urban transport TomTom Twitter Geographic information systems (GIS) 



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).


  1. Aziz HMA, Ukkusuri SV, Zhan X (2016) Determining the impact of personal mobility carbon allowance schemes in transportation networks. Netw Spat Econ. doi: 10.1007/s11067-016-9334-x Google Scholar
  2. Boisjoly G, El-Geneidy A (2016) Daily fluctuations in transit and job availability: a comparative assessment of time-sensitive accessibility measures. J Transp Geogr 52:73–81. doi: 10.1016/j.jtrangeo.2016.03.004 CrossRefGoogle Scholar
  3. Bruno G, Genovese A (2012) A spatial interaction model for the representation of the mobility of university students on the Italian territory. Netw Spat Econ 12:41–57. doi: 10.1007/s11067-010-9142-7 CrossRefGoogle Scholar
  4. Chen A, Yang C, Kongsomsaksakul S, Lee M (2007) Network-based acessibility measures for vulnerability analysis of degradable transportation networks. Netw Spat Econ 7:241–256. doi: 10.1007/s11067-006-9012-5 CrossRefGoogle Scholar
  5. Ciuccarelli P, Lupi G, Simeone L (2014) Visualizing the Data City, First. Springer International Publishing, Cham. Accessed 27 Apr 2017
  6. Dewulf B, Neutens T, Vanlommel M et al (2015) Examining commuting patterns using floating car data and circular statistics: exploring the use of new methods and visualizations to study travel times. J Transp Geogr 48:41–51. doi: 10.1016/j.jtrangeo.2015.08.006 CrossRefGoogle Scholar
  7. Farber S, Morang MZ, Widener MJ (2014) Temporal variability in transit-based accessibility to supermarkets. Appl Geogr 53:149–159. doi: 10.1016/j.apgeog.2014.06.012 CrossRefGoogle Scholar
  8. Fielbaum A, Jara-Diaz S, Gschwender A (2016) A parametric Description of cities for the normative analysis of transport systems. Netw Spat Econ. doi: 10.1007/s11067-016-9329-7 Google Scholar
  9. Geurs KT, van Wee B (2004) Accessibility evaluation of land-use and transport strategies: review and research directions. J Transp Geogr 12:127–140. doi: 10.1016/j.jtrangeo.2003.10.005 CrossRefGoogle Scholar
  10. Geurs KT, De Montis A, Reggiani A (2015) Recent advances and applications in accessibility modelling. Comput Environ Urban Syst 49:82–85. doi: 10.1016/j.compenvurbsys. 2014.09.003 CrossRefGoogle Scholar
  11. Geurs KT, Patuelli R, Dentinho TP (2016) Accessibility, Equity and Efficiency: Challenges for Transport and Public Services. Edward Elgar Publishing LimitedGoogle Scholar
  12. Grauwin S, Sobolevsky S, Moritz S, et al (2015) Towards a comparative science of cities: using mobile traffic records in New York, London, and Hong Kong. In: Helbich M, Jokar Arsanjani J, Leitner M (eds) Computational approaches for urban environments, First. Springer International Publishing, pp 363–387Google Scholar
  13. Jäppinen S, Toivonen T, Salonen M (2013) Modelling the potential effect of shared bicycles on public transport travel times in greater Helsinki: an open data approach. Appl Geogr 43:13–24. doi: 10.1016/j.apgeog.2013.05.010 CrossRefGoogle Scholar
  14. Jiang B, Ma D, Yin J, Sandberg M (2016) Spatial distribution of City tweets and their densities. Geogr Anal 48:337–351. doi: 10.1111/gean.12096 CrossRefGoogle Scholar
  15. Kaddoura I, Kröger L, Nagel K (2016) User-specific and dynamic internalization of road traffic noise exposures. Networks Spat Econ :1–20. doi:  10.1007/s11067-016-9321-2
  16. Lenormand M, Picornell M, Cantú-Ros OG et al (2014) Cross-checking different sources of mobility information. PLoS One 9:30–38. doi: 10.1371/journal.pone.0105184 Google Scholar
  17. Longley PA, Adnan M, Lansley G (2015) The geotemporal demographics of twitter usage. Environ Plan A 47:465–484. doi: 10.1068/a130122p CrossRefGoogle Scholar
  18. Louail T, Lenormand M, García O et al (2014) From mobile phone data to the spatial structure of cities. Sci Rep 4:5276. doi: 10.1038/srep05276 CrossRefGoogle Scholar
  19. Martin D, Wrigley H, Barnett S, Roderick P (2002) Increasing the sophistication of access measurement in a rural healthcare study. Heal Place 8:3–13. doi: 10.1016/S1353-8292(01)00031-4 CrossRefGoogle Scholar
  20. Martin D, Jordan H, Roderick P (2008) Taking the bus: incorporating public transport timetable data into health care accessibility modelling. Environ Plan A 40:2510–2525. doi: 10.1068/a4024 CrossRefGoogle Scholar
  21. Mascia M, Hu S, Han K, et al (2016) Impact of traffic management on black carbon emissions: a microsimulation study. Networks Spat Econ :1–23. doi:  10.1007/s11067-016-9326-x
  22. Møller-Jensen L, Kofie RY, Allotey ANM (2012) Measuring accessibility and congestion in Accra. Nor Geogr Tidsskr - Nor J Geogr 66:52–60. doi: 10.1080/00291951.2011.644322 CrossRefGoogle Scholar
  23. Moya-Gómez B, García-Palomares JC (2015) Working with the daily variation in infrastructure performance on territorial accessibility. The cases of Madrid and Barcelona. Eur Transp Res Rev 7:1–13. doi: 10.1007/s12544-015-0168-2 CrossRefGoogle Scholar
  24. Murthy D (2013) Twitter: social communication in twitter age, first. John Wiley & Sons, CambridgeGoogle Scholar
  25. Netto VM, Pinheiro M, Meirelles JV, Leite H (2015) Digital footprints in the cityscape: finding networks of segregation through big data. In: International Conference on Location-Based Social Media Data. Athens, pp 1–15Google Scholar
  26. Ortúzar JD, Willumsen LG (2011) Modelling transport, 4th edn. John Wiley & Sons, West SussexCrossRefGoogle Scholar
  27. Owen A, Levinson DM (2015) Modeling the commute mode share of transit using continuous accessibility to jobs. Transp Res Part A Policy Pract 74:110–122. doi: 10.1016/j.tra.2015.02.002 CrossRefGoogle Scholar
  28. Páez A, Moniruzzaman M, Bourbonnais PL, Morency C (2013) Developing a web-based accessibility calculator prototype for the greater Montreal area. Transp Res Part A Policy Pract 58:103–115. doi: 10.1016/j.tra.2013.10.020 CrossRefGoogle Scholar
  29. Ratti C, Frenchman D, Pulselli RM, Williams S (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plan B Plan Des 33:727–748. doi: 10.1068/b32047 CrossRefGoogle Scholar
  30. Reades J, Calabrese F, Ratti C (2009) Eigenplaces: Analysing cities using the space - time structure of the mobile phone network. Environ Plan B Plan Des 36:824–836. doi: 10.1068/b34133t CrossRefGoogle Scholar
  31. Reggiani A, Martín JC (2011) Guest editorial: new Frontiers in accessibility modelling: an introduction. Netw Spat Econ 11:577–580. doi: 10.1007/s11067-011-9155-x CrossRefGoogle Scholar
  32. Reggiani A, Bucci P, Russo G (2011) Accessibility and network structures in the German commuting. Netw Spat Econ 11:621–641. doi: 10.1007/s11067-010-9149-0 CrossRefGoogle Scholar
  33. Salas-Olmedo MH, Rojas-Quezada C (2016) Mapping mobility patterns to public spaces in a medium-sized city using geolocated tweets. arXiv, Phys Soc 10Google Scholar
  34. Shelton T, Poorthuis A, Zook M (2015) Social media and the city: rethinking urban socio-spatial inequality using user-generated geographic information. Landsc Urban Plan 142:198–211. doi: 10.1016/j.landurbplan.2015.02.020 CrossRefGoogle Scholar
  35. Sweet MN (2014) Do firms flee traffic congestion? J Transp Geogr 35:40–49. doi: 10.1016/j.jtrangeo.2014.01.005 CrossRefGoogle Scholar
  36. Sweet MN, Harrison CJ, Kanaroglou PS (2015) Gridlock in the greater Toronto area: its geography and intensity during key periods. Appl Geogr 58:167–178. doi: 10.1016/j.apgeog.2015.01.011 CrossRefGoogle Scholar
  37. Vandenbulcke G, Steenberghen T, Thomas I (2009) Mapping accessibility in Belgium: a tool for land-use and transport planning? J Transp Geogr 17:39–53. doi: 10.1016/j.jtrangeo.2008.04.008 CrossRefGoogle Scholar
  38. van Wee B (2016) Accessible accessibility research challenges. J Transp Geogr 51:9–16. doi: 10.1016/j.jtrangeo.2015.10.018 CrossRefGoogle Scholar
  39. Wu L, Zhi Y, Sui Z, Liu Y (2014) Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS One. doi: 10.1371/journal.pone.0097010 Google Scholar
  40. Yang X, Ban XJ, Ma R (2016) Mixed equilibria with common constraints on transportation networks. Netw Spat Econ. doi: 10.1007/s11067-016-9335-9 Google Scholar
  41. Yiannakoulias N, Bland W, Svenson LW (2013) Estimating the effect of turn penalties and traffic congestion on measuring spatial accessibility to primary health care. Appl Geogr 39:172–182. doi: 10.1016/j.apgeog.2012.12.003 CrossRefGoogle Scholar
  42. Zhan X, Ukkusuri SV, Zhu F (2014) Inferring urban land use using large-scale social media check-in data. Netw Spat Econ 14:647–667. doi: 10.1007/s11067-014-9264-4 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Borja Moya-Gómez
    • 1
  • María Henar Salas-Olmedo
    • 1
  • Juan Carlos García-Palomares
    • 1
  • Javier Gutiérrez
    • 1
  1. 1.Transport, Infrastructure and Territory Research Group (t-GIS), Human Geography Department, Faculty of Geography and HistoryUniversidad Complutense de Madrid (UCM)MadridSpain

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