Spatiotemporal Big Data Challenges for Traffic Flow Analysis

  • Dmitry PavlyukEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 36)


This paper contains a survey of spatiotemporal big data challenges in the area of urban traffic flow analysis. Existing sources and types of traffic flow data were reviewed and evidences that traffic flow data can be considered as spatiotemporal big data were provided. Current trends in spatiotemporal big data analytics and in urban traffic flow modelling and forecasting were consolidated and a list of joint emerging challenges was composed. The stated challenges cover different spatiotemporal aspects of big data and are linked to optimal time and space data resolution, spatial and temporal relationships in traffic data, computational complexity of spatiotemporal algorithms, fusion of traffic data from heterogeneous data sources into a single predictive scheme, and development of responsive streaming algorithms. The raised challenges are supported by an extensive literature review, and suggestions for future work are offered.


Spatiotemporal big data Urban traffic flows Modelling Forecasting 



This work was financially supported by the post-doctoral research aid programme of the Republic of Latvia (project no., “Spatiotemporal urban traffic modelling using big data”), funded by the European Regional Development Fund.


  1. 1.
    Vlahogianni, E.I., Park, B.B., van Lint, J.W.C.: Big data in transportation and traffic engineering. Transp. Res. Part C Emerg. Technol. 58, 161 (2015)CrossRefGoogle Scholar
  2. 2.
    Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., Zhou, X.: Spatiotemporal data mining: a computational perspective. ISPRS Int. J. Geoinf. 4, 2306–2338 (2015)CrossRefGoogle Scholar
  3. 3.
    Cressie, N.A.C., Wikle, C.K.: Statistics for Spatio-Temporal Data. Wiley, Hoboken (2011)zbMATHGoogle Scholar
  4. 4.
    Xu, J., Deng, D., Demiryurek, U., Shahabi, C., van der Schaar, M.: Mining the situation: spatiotemporal traffic prediction with big data. IEEE J. Sel. Topics Signal Process. 9, 702–715 (2015)CrossRefGoogle Scholar
  5. 5.
    Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C Emerg. Technol. 43, 3–19 (2014)CrossRefGoogle Scholar
  6. 6.
    Jiang, Z., Shekhar, S.: Spatial Big Data Science: Classification Techniques for Earth Observation Imagery. Springer, Cham (2017)CrossRefGoogle Scholar
  7. 7.
    Yatskiv, I., Grakovski, A., Yurshevich, E.: An overview of different methods available to observe traffic flows using new technologies. In: The NTTS 2013 Proceedings, p. 10, Brussel, Belgium (2013)Google Scholar
  8. 8.
    Bureau of Infrastructure, Transport and Regional Economics: GHD report: New traffic data sources. In: presented at the New Data Sources for Transport Workshop, Sydney, Australia (2014)Google Scholar
  9. 9.
    Wang, S., He, L., Stenneth, L., Yu, P.S., Li, Z., Huang, Z.: Estimating urban traffic congestions with multi-sourced data. In: presented at the 17th IEEE International Conference on Mobile Data Management (MDM) (2016)Google Scholar
  10. 10.
    Bhaskar, A., Chung, E., Dumont, A.-G.: Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks: fusing loop detector and probe vehicle data. Comput. Aided Civil Infrastruct. Eng. 26, 433–450 (2011)CrossRefGoogle Scholar
  11. 11.
    Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16, 1–9 (2014)CrossRefGoogle Scholar
  12. 12.
    Vlahogianni, E.I.: Computational intelligence and optimization for transportation big data: challenges and opportunities. In: Lagaros, N.D., Papadrakakis, M. (eds.) Engineering and Applied Sciences Optimization, pp. 107–128. Springer, Cham (2015)CrossRefGoogle Scholar
  13. 13.
    He, Z., Lv, Y., Lu, L., Guan, W.: Constructing spatiotemporal speed contour diagrams: using rectangular or non-rectangular parallelogram cells? Transportmetrica B Transp. Dyn. 1–17 (2017).
  14. 14.
    Kuhn, K., Nicholson, A.: Traffic flow forecasting and spatial data aggregation. Transp. Res. Rec. J. Transp. Res. Board 2260, 16–23 (2011)CrossRefGoogle Scholar
  15. 15.
    Loidl, M., Wallentin, G., Cyganski, R., Graser, A., Scholz, J., Haslauer, E.: GIS and transport modeling-strengthening the spatial perspective. ISPRS Int. J. GeoInf. 5, 84 (2016)CrossRefGoogle Scholar
  16. 16.
    Wong, D.: The modifiable areal unit problem (MAUP). In: Fotheringham, A.S., Rogerson, P. (eds.) The SAGE Handbook of Spatial Analysis, pp. 105–124. SAGE Publications, Los Angeles (2009)Google Scholar
  17. 17.
    Qiao, F., Yu, L., Wang, X.: Double-sided determination of aggregation level for intelligent transportation system data. Transp. Res. Rec. J. Transp. Res. Board 1879, 80–88 (2004)CrossRefGoogle Scholar
  18. 18.
    Oh, C., Ritchie, S., Oh, J.-S.: Exploring the relationship between data aggregation and predictability to provide better predictive traffic information. Transp. Res. Rec. J. Transp. Res. Board 1935, 28–36 (2005)CrossRefGoogle Scholar
  19. 19.
    Vlahogianni, E., Karlaftis, M.: Temporal aggregation in traffic data: implications for statistical characteristics and model choice. Transp. Lett. 3, 37–49 (2011)CrossRefGoogle Scholar
  20. 20.
    Kamarianakis, Y., Prastacos, P.: Space-time modeling of traffic flow. Comput. Geosci. 31, 119–133 (2005)CrossRefGoogle Scholar
  21. 21.
    Min, X., Hu, J., Chen, Q., Zhang, T., Zhang, Y.: Short-term traffic flow forecasting of urban network based on dynamic STARIMA model. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6 (2009)Google Scholar
  22. 22.
    Salamanis, A., Kehagias, D.D., Filelis-Papadopoulos, C.K., Tzovaras, D., Gravvanis, G.A.: Managing spatial graph dependencies in large volumes of traffic data for travel-time prediction. IEEE Trans. Intell. Transp. Syst. 17, 1678–1687 (2016)CrossRefGoogle Scholar
  23. 23.
    Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C Emerg. Technol. 11, 121–135 (2003)CrossRefGoogle Scholar
  24. 24.
    Cheng, T., Haworth, J., Wang, J.: Spatio-temporal autocorrelation of road network data. J. Geogr. Syst. 14, 389–413 (2012)CrossRefGoogle Scholar
  25. 25.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C Emerg. Technol. 19, 606–616 (2011)CrossRefGoogle Scholar
  26. 26.
    Schimbinschi, F., Moreira-Matias, L., Nguyen, V.X., Bailey, J.: Topology-regularized universal vector autoregression for traffic forecasting in large urban areas. Expert Syst. Appl. 82, 301–316 (2017)CrossRefGoogle Scholar
  27. 27.
    Kamarianakis, Y., Shen, W., Wynter, L.: Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO. Appl. Stoch. Models Bus. Ind. 28, 297–315 (2012)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Haworth, J., Cheng, T.: Graphical LASSO for local spatio-temporal neighbourhood selection. In: Proceedings the GIS Research UK 22nd Annual Conference, presented at the GISRUK, pp. 425–433 (2014)Google Scholar
  29. 29.
    Li, L., Su, X., Wang, Y., Lin, Y., Li, Z., Li, Y.: Robust causal dependence mining in big data network and its application to traffic flow predictions. Transp. Res. Part C Emerg. Technol. 58, 292–307 (2015)CrossRefGoogle Scholar
  30. 30.
    Ermagun, A., Levinson, D.M.: Spatiotemporal traffic forecasting: review and proposed directions. In: presented at the 96th Annual Transportation Research Board Meeting, USA (2016)Google Scholar
  31. 31.
    Kazar, B.M., Celik, M.: Spatial AutoRegression (SAR) Model. Springer, New York (2012)CrossRefzbMATHGoogle Scholar
  32. 32.
    Lee, J.-G., Kang, M.: Geospatial big data: challenges and opportunities. Big Data Res. 2, 74–81 (2015)CrossRefGoogle Scholar
  33. 33.
    Eldawy, A., Mokbel, M.F.: The era of big spatial data: a survey. Inf. Media Technol. 10, 305–316 (2015)Google Scholar
  34. 34.
    Alkathiri, M., Abdul, J., Potdar, M.B.: Geo-spatial big data mining techniques. Int. J. Comput. Appl. 135, 28–36 (2016)Google Scholar
  35. 35.
    Eldawy, A., Mokbel, M.F.: SpatialHadoop: a MapReduce framework for spatial data. In: presented at the 2015 IEEE 31st International Conference on Data Engineering (ICDE) (2015)Google Scholar
  36. 36.
    Aggarwal, C.C. (ed.): Data Streams. Springer, Boston (2007)zbMATHGoogle Scholar
  37. 37.
    Herring, R.J.: Real-time traffic modeling and estimation with streaming probe data using machine learning (2010)Google Scholar
  38. 38.
    Tan, M.-C., Wong, S.C., Xu, J.-M., Guan, Z.-R., Zhang, P.: An aggregation approach to short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 10, 60–69 (2009)CrossRefGoogle Scholar
  39. 39.
    Stathopoulos, A., Karlaftis, M., Dimitriou, L.: Fuzzy rule-based system approach to combining traffic count forecasts. Transp. Res. Rec. J. Transp. Res. Board 2183, 120–128 (2010)CrossRefGoogle Scholar
  40. 40.
    Faouzi, N.-E.E., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems: progress and challenges – a survey. Inf. Fusion 12, 4–10 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Transport and Telecommunication InstituteRigaLatvia

Personalised recommendations