Optimal traffic data archive scheme

Buy article PDF
© The Institution of Engineering and Technology
Published 31/07/2012

Abstract

An optimal traffic data archive scheme where the maximum information of the original data can be preserved using less storage space has been described. Compared with traditional equal-width methods where compact data are obtained by aggregating source data at fixed intervals, the optimal scheme uses varying intervals to aggregate data at different levels based on the variations within the source data. The resultant scheme is optimal in terms of information conservation, that is, the errors between the source data and the optimal compact data are the smallest. Operational traffic data have been used to test three proposed optimisation schemes: single-variable, multi-variable and heuristic schemes. It was found that, compared with traditional equal-width schemes, the size of the archived data can be reduced by six times if the single-variable optimisation scheme, or by three times if the multi-variable optimisation scheme is employed. The heuristic scheme using a combination of single-variable and multi-variable optimisations can then reduce storage space by three to six times.

Inspec keywords: optimisation; traffic information systems

Other keywords: heuristic scheme; storage space; aggregate data; operational traffic data; information conservation; optimal traffic data archive scheme; optimal compact data

Subjects: Optimisation techniques; Traffic engineering computing

References

    1. 1)
      • Turner, S., Margiotta, R., Lomax, T.: Monitoring urban freeways in 2003: current conditions and trends from archived operations data, 2004 (Texas Transportation Institute)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • Chen, C., Kwon, J., Varaiya, P.: The quality of loop data and the health of California's freeway loop detectors, PeMS Development Group, 2002 (University of CaliforniaBerkeley)
    6. 6)
      • Wu, P.: `Automated data collection, analysis and archival', Final Report, September 2003, The University of Utah
    7. 7)
      • Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K., Suel, T.: `Optimal histograms with quality guarantees', Proc. of the 24th VLDB Conf., 1998, New York, USA
    8. 8)
    9. 9)
      • Jagadish, H.V., Koudas, N., Muthukrishnan, S.: `Mining deviants in a time series database', Proc. of the 25th VLDB Conf., 1999, Edinburgh, UK, p. 102–113
    10. 10)
    11. 11)
Print this page
This is a required field
Please enter a valid email address