Exploring traffic flow databases using space-time plots and data cubes | Transportation Skip to main content
Springer Nature Link
Log in

Exploring traffic flow databases using space-time plots and data cubes

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

Local departments of transportation and metropolitan planning organizations have been collecting traffic data for many decades. However, these data are rarely exploited to their full potential. In this paper, we describe an exploratory visualization toolkit for large traffic flow databases. The visualization toolkit is based on the concept of the traffic cube: an extension of the data cube in data mining. The traffic cube organizes traffic flow data across different spatial and temporal dimensions and with respect to user-specified aggregation levels. The toolkit allows the user to perform data cube operations to select, summarize and cross-tabulate the traffic data prior to visualization as two-dimensional space-time plots. We demonstrate a prototype system using MATLAB, ArcGIS and MS Access database software. Example visualizations of a large database of hourly traffic flows along major highways in the state of Utah (USA) over a 10-year period illustrate the potential for the toolkit to reveal patterns about traffic flows and trends hidden in the database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Harlow, UK (1996)

    Google Scholar 

  • Cho, H.-J., Jou, Y.-J., Lan, C.-L.: Time dependent origin-destination estimation from traffic count without prior information. Networks Spatial Econ. 9, 145–170 (2009)

    Article  Google Scholar 

  • Daganzo, C.F.: Fundamentals of Transportation and Traffic Operations. Elsevier Science, Oxford (1997)

    Google Scholar 

  • Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–30. AAAI Press, Menlo Park, CA (1996)

    Google Scholar 

  • Fayyad, U., Grinstein, G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann, San Matel, CA (2001)

    Google Scholar 

  • FHWA: Traffic detector handbook: third edition, federal highway administration. U.S. Department of Transportation, Publication Number: FHWA-HRT-06-139, October 2006. http://www.fhwa.dot.gov/publications/research/operations/its/06108/06108.pdf (2006). Accessed 18 April 2011

  • Gahegan, M.: The case for inductive and visual techniques in the analysis of spatial data. J. Geogr. Syst. 2, 77–83 (2000)

    Article  Google Scholar 

  • Gahegan, M.: Visual exploration and explanation in geography: analysis with light. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edn, pp. 291–324. Taylor and Francis, London (2009)

    Chapter  Google Scholar 

  • Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Min. Knowl. Disc. 1, 29–53 (1997)

    Article  Google Scholar 

  • Guo, D.: Multivariate spatial clustering and visualization. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edition edn, pp. 325–345. Taylor and Francis, London (2009)

    Chapter  Google Scholar 

  • Han, J.W., Kamber, J.: Data mining: concept and techniques, 2nd edn, pp. xxi, 1–40, 105–157, and 600–614. Elsevier Inc. (2006)

  • Harinarayan, V., Rajaramna, A., Ullman, J.D.: Implementing data cubes efficiently. SIGMOD Record 25, 205–216 (1996)

    Article  Google Scholar 

  • Keim, D.A., Kriegel, H.-P.: Using visualization to support data mining of large existing databases. In: Lee, J.P., Grinstein, G.G. (eds.) Database Issues for Data Visualization, Lecture Notes in Computer Science, vol. 871, pp. 210–229 (1994).

  • Lu, C.-T., Boedihardjo, A.P., Shekhar, S.: Analysis of spatial data with map cubes: highway traffic data. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edition edn, pp. 69–97. Taylor and Francis, London (2009)

    Google Scholar 

  • McCormick, S., Thomas, J.: The Fast Adaptive Composite Grid (FAC) method for elliptic equations. Math. Comput. 46–174, 439–456 (1986)

    Google Scholar 

  • Meinhardt, H.: Models of Biological Pattern Formation. Academic Press, London (1982)

    Google Scholar 

  • Miller, H.J., Han, J.: Geographic data mining and knowledge discovery: an overview. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edition edn, pp. 1–26. Taylor & Francis, London (2009)

    Chapter  Google Scholar 

  • Mohania, M., et al.: Advances and research directions in data-warehousing technology. Aust. J. Inform. Syst. 7–1, 41–59 (1999)

    Google Scholar 

  • Nagel, K., Wolf, D.E., Wagner, P., Simon, P.: Two-lane traffic rules for cellular automata: a systematic approach. Phys. Rev. E 58, 1425–1437 (1998)

    Article  Google Scholar 

  • Nagel, K., Wagner, P., Woesler, R.: Still flowing: old and new approaches for traffic flow modeling. Oper. Res. 51, 681–710 (2003)

    Article  Google Scholar 

  • Nicolai, T., Carr, D., Weiland, S.K., Duhme, H., von Ehrenstein, O., Wagner, C., von Mutius, E.: Urban traffic and pollutant exposure related to respiratory outcomes and atopy in a large sample of children. Eur. Respir. J. 21, 956–963 (2003)

    Article  Google Scholar 

  • OLAP Council: OLAP and OLAP server definitions (1995)

  • Prasher, S., Zhou, X.: Multiresolution amalgamation: dynamic spatial data cube generation. In: Proceedings of 15th Australasian Database Conference (ADC 2004), Dunedin, New Zealand, pp. 103–111 (2004)

  • Rao, F., Zhang, L., Yu, X.L., Li, Y., Chen, Y.: Spatial hierarchy and OLAP-favored search in spatial data warehouse. In: Proceedings of the 6th ACM international workshop on data warehousing and OLAP, pp. 48–55 (2003)

  • Shekhar, S., Lu, C.T., Liu, R., Zhou, C.: CubeView: a system for traffic data visualization. intelligent transportation systems. In: Proceedings of the Fifth IEEE International Conference on Intelligent Transportation Systems, pp. 674–679 (2002)

  • Shekhar, S., Lu, C.T., Tan, X., Chawla, S., Vatsavai, R.R.: Map cube: a visualization tool for spatial data warehouses. In: Miller, H., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, pp. 74–109. Taylor & Francis (2001)

  • Skupin, A., Fabrikant, S.: Spatialization. In: Wilson, J., Fotheringham, S. (eds.) The Handbook of Geographical Information Science, pp. 61–79. Blackwell Publishing, London (2008)

    Google Scholar 

  • Stefanovic, N., Han, J., Koperski, K.: Object-based selective materialization for efficient implementation of spatial data cube. IEEE Trans. Knowl. Data Eng. 12–6, 938–958 (2000)

    Article  Google Scholar 

  • Transportation Research Board (2009) Special Report 260: Strategic Highway Research: Saving Lives, Reducing Congestion, Improving Quality of Life

  • Treiber, M., Helbing, D.: Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooperative Transportation Dynamics, 1, 3.1–3.24 (online journal; www.TrafficForum.org) (2002)

  • Zhuang, J., Ogata, Y., Vere-Jones, D.: Stochastic declustering of space-time earthquake occurrences. J. Am. Stat. Assoc. 97, 369–380 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Utah, 260 S. Central Campus Dr. Room 270, Salt Lake City, UT, 84112-9155, USA

    Ying Song & Harvey J. Miller

Authors
  1. Ying Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harvey J. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harvey J. Miller.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, Y., Miller, H.J. Exploring traffic flow databases using space-time plots and data cubes. Transportation 39, 215–234 (2012). https://doi.org/10.1007/s11116-011-9343-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-011-9343-z

Keywords

Search

Navigation