Data-Driven Transport Policy in Cities: A Literature Review and Implications for Future Developments

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 844)


The skill of analysis of big data provided by the ICT and the skill of using this information in the decision-making process become crucial elements of the increase in competitiveness and effectiveness in all sectors of economy, in particular in the transport sector, in the field of mobility management in cities. The data-driven transport policy is also one of key pillars of a smart city concept. The paper discusses the importance of data-driven decision making in the common transport policy of the European Union. The paper reviews also the carried out so far studies on the transport behaviour, utilising passive data streams. Finally, the paper discusses main challenges facing researchers and public transport authorities related to the use of passive data streams for the needs of studies in the future.


Big data Mobility Transport policy Travel behaviour Passive data stream Transport demand Automated fare collection system Smart cards Urban transport Public transport Smart city Smart governance 


  1. 1.
    The knowledge-based economy: OECD, Paris (1996).
  2. 2.
    Eurostat Database.
  3. 3.
    United Nations, Department of Economic and Social Affairs, Population Division: World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352) (2014)Google Scholar
  4. 4.
    United Nations, Department of Economic and Social Affairs, Population Division: The World’s Cities in 2016: Data booklet (2016).
  5. 5.
    European Commission: Building a European Data Economy, COM/2017/09/final, Brussels. Accessed 10 Jan 2017
  6. 6.
    European Commission: A Digital Single Market Strategy for Europe, COM/2015/0192 final, Brussels. Accessed 6 May 2015
  7. 7.
    Einav, L., Levin, J.D.: The data revolution and economic analysis. Working paper series no. 19035, National Bureau of Economic Research, Cambridge (2013)Google Scholar
  8. 8.
    Brynjolfsson, E., Hitt, L.M., Kim, H.H.: Strength in Numbers: How Does Data-Driven Decision-Making Affect Firm Performance? Accessed 22 Apr 2011
  9. 9.
    European Commission: Towards a thriving data-driven economy, COM/2014/0442/final, Brussels. Accessed 2 July 2014
  10. 10.
    European Commission: Together towards competitive and resource-efficient urban mobility, COM/2013 913 final, Brussels. Accessed 17 Dec 2013
  11. 11.
    European Commission. Action Plan for the Deployment of Intelligent Transport Systems in Europe, COM/2008/0886 final, Brussels. Accessed 16 Dec 2008
  12. 12.
    European Commission: Green paper – Towards a new culture for urban mobility. COM(2007) 551 final, Brussels. Accessed 25 Sep 2007
  13. 13.
    European Commission: Action Plan on Urban Mobility, COM (2009) 0490 final, Brussels. Accessed 30 Sep 2009
  14. 14.
    European Commission: White Paper - Roadmap to a Single European Transport Area – Towards a competitive resource efficient transport system, COM(2011) 144 final, Brussels. Accessed 28 Mar 2011
  15. 15.
    European Commission: Europe on the move – an agenda for a socially fair transition towards clean competitive and connected mobility for all, COM(2017) 283 final, Brussels. Accessed 31 May 2017
  16. 16.
  17. 17.
    Tomanek, R.: Sustainable mobility in smart metropolis. In: Brdulak, A., Brdulak, H. (eds.) Happy City - How to Plan and Create the Best Liveable Area for the People. EcoProduction (Environmental Issues in Logistics and Manufacturing), pp. 3–16. Springer, Cham (2017)CrossRefGoogle Scholar
  18. 18.
    Nam, T., Pardo, T.A.: Smart city as urban innovation: focusing on management, policy, and context. In: Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance, pp. 185–194. Tallinn, Estonia (2011)Google Scholar
  19. 19.
    Gil-Garcia, J.R., Zhang, J., Puron-Cid, G.: Conceptualizing smartness in government: an integrative and multi-dimensional view. Gov. Inf. Q. 33(3), 524–534 (2016). Scholar
  20. 20.
    Briand, A.S., Côme, E., Trépanier, M., Oukhellou, L.: Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transp. Res. Part C: Emerg. Technol. 79, 274–289 (2017). Scholar
  21. 21.
    Clifton, K.J., Handy, S.L.: Qualitative methods in travel behavior research. In: Jones, P., Stopher, P.R. (eds.) Transport Survey Quality and Innovation, pp. 283–302. Emerald, Bingley (2003)CrossRefGoogle Scholar
  22. 22.
    Trépanier, M., Tamamoto, T.: Workshop synthesis: system based passive data streams systems; smart cards, phone data, GPS. Transp. Res. Procedia 11, 340–349 (2015)CrossRefGoogle Scholar
  23. 23.
    Urbanek, A.: Big data – a challenge for urban transport managers. Commun. Sci. Lett. Univ. Zilina 19(2), 36–42 (2017)Google Scholar
  24. 24.
    Bagchi, M., White, P.R.: The potential of public transport smart card data. Transp. Policy 12(5), 464–474 (2005)CrossRefGoogle Scholar
  25. 25.
    Utsunomiya, M., Attanucci, J., Wilson, N.H.: Potential uses of transit smart card registration and transaction data to improve transit planning. Transp. Res. Rec.: J. Transp. Res. Board 1971, 119–126 (2006). Transportation Research Board of the National Academies, Washington, D.C.CrossRefGoogle Scholar
  26. 26.
    Agard, B., Morency, C., Trépanier, M.: Mining public transport user behaviour from smart card data. IFAC Proc. Vol. 39, 399–404 (2006). Scholar
  27. 27.
    Barry, J., Freiner, R., Slavin, H.: Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. Transp. Res. Rec. 2112, 53–61 (2009)CrossRefGoogle Scholar
  28. 28.
    Barry, J., Newhouser, R., Rahbee, A., Sayeda, S.: Origin and destination estimation in New York City with automated fare system data. Transp. Res. Rec.: J. Transp. Res. Board 1817, 183–187 (2002). Transportation Research Board of the National Academies, Washington, D.C.CrossRefGoogle Scholar
  29. 29.
    Liu, L., Hou, A., Biderman, A., Ratti, C., Chen, J.: Understanding individual and collective mobility patterns from smart card records: a case study in Shenzhen. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC, pp. 842–847 (2009)Google Scholar
  30. 30.
    Wang, W., Attanucci, J., Wilson, N.: Bus passenger origin-destination estimation and related analyses using automated data collection systems. J. Public Transp. 14(4), 131–150 (2011). Scholar
  31. 31.
    Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin-destination matrix from passive Smart card data from Santiago, Chile. Transp. Res. Part C 24C(12), 9–18 (2012)CrossRefGoogle Scholar
  32. 32.
    Lee, S.G., Hickman, M.: Trip purpose inference using automated fare collection data. Public Transp. 6, 1–20 (2014). Scholar
  33. 33.
    Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.J.: Understanding commuting patterns using transit smart card data. J. Transp. Geogr. 58, 135–145 (2017)CrossRefGoogle Scholar
  34. 34.
    Pelletier, M.-P., Trépanier, M., Morency, C.: Smart card data use in public transit: a literature review. Transp. Res. Part C 19(4), 557–568 (2011)CrossRefGoogle Scholar
  35. 35.
    Geschwender, A., Munizaga, M., Simonetti, C.: Using smart card and GPS data for policy and planning: the case of transantiago. Res. Transp. Econ. 59, 242–249 (2016). Scholar
  36. 36.
    González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008). Scholar
  37. 37.
    Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira Jr., J., Ratti, C.: Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp. Res. Procedia Part C 26, 301–313 (2013). Scholar
  38. 38.
    Iqbal, M.S., Choudhury, C.F., Wang, P., Gonzáles, M.C.: Development of origin–destination matrices using mobile phone call data. Transp. Res. Procedia Part C 40, 63–74 (2014). Scholar
  39. 39.
    Alexander, L., Jiang, S., Murga, M., Gonzáles, M.C.: Origin-destination trips purpose and time of day interfered from mobile phone data. Transp. Res. Procedia Part C 58, 240–250 (2015). Scholar
  40. 40.
    Geurs, K.T., Thomas, T., Bijlsma, M., Douhou, S.: Automatic trip and mode detection with move smarter: first results from the Dutch mobile mobility panel. Transp. Res. Procedia 11, 247–262 (2015). Scholar
  41. 41.
    International Transport Forum OECD: Corporate Partnership Board Report, Data-driven Transport Policy, Paris (2016)Google Scholar
  42. 42.
    International Transport Forum OECD: Corporate Partnership Board Report, Big data and Transport: Understanding and assessing options, Paris (2015)Google Scholar
  43. 43.
    Taniguchi, E.: Concepts of city logistics for sustainable and liveable cities. Procedia Soc. Behav. Sci. 151, 310–317 (2014). Scholar
  44. 44.
    Urbanek, A.: Automated fare collection systems based on check-in and check-out-premises of implementation in urban public transport. Arch. Transp. Syst. Telemat. 10(3), 40–45 (2017)Google Scholar
  45. 45.
    Steenberghen, T., Pourbaix, J., Moulin, A., Bamps, C., Keijers, S.: Study on Harmonised Collection of European Data and Statistics in the Field of Urban Mobility. MOVE/B4/196-2/2010, Final report 24 May 2013, SADL KU Leuven and UITP (2013).
  46. 46.
    Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). Official Journal of the European Union L 108, pp. 1–14, 25 April 2007.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of EconomicsUniversity of Economics in KatowiceKatowicePoland

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