access icon free Method for accuracy assessment of aggregated freeway traffic data

Accurate traffic data are crucial for advanced traveller information and traffic management applications. However, it is widely acknowledged that limitations in the monitoring and communication technologies lead to significant inaccuracies in the estimates of traffic parameters such as traffic volume and travel speed. Currently deployed traffic data quality control methods do little more than identifying implausible outliers. This study presents an efficient method based on the concept of spatial consistency for assessing the accuracies of aggregated segment-wise traffic volumes in a freeway network. A novelty of the proposed approach is that unlike most other comparable solutions, it does not depend on the availability of good quality historical traffic data or well-calibrated reference stations. The method has been validated using simulated data generated by a microscopic traffic simulator. It has been found that the accuracy estimates produced by the proposed method strongly correlate with the actual accuracies of the input traffic data that have been corrupted with two types of synthetic errors.

Inspec keywords: calibration; quality control; road traffic; traffic information systems; computerised monitoring

Other keywords: communication technologies; freeway network; aggregated segment-wise traffic volumes; spatial consistency; traffic parameters; monitoring technologies; synthetic errors; traffic management applications; advanced traveller information; accuracy assessment method; aggregated freeway traffic data; traffic data quality control methods; microscopic traffic simulator

Subjects: Computerised instrumentation; Traffic engineering computing

References

    1. 1)
      • 4. Klein, L.A., Mills, M.K., Gibson, D.R.P.: ‘Traffic Detector Handbook’, (US Department of Transportation, Federal Highway Administration, 2006, Vol. 1, 3rd edn.).
    2. 2)
    3. 3)
    4. 4)
      • 3. Turner, S.: ‘Quality control procedures for archived operations traffic data: synthesis of practice and recommendations’. Final Report to the Federal Highway Administration, Prepared by Texas Transportation Institute under contract to Battelle, March 2007.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • 10. Kikuchi, S.: ‘A method to defuzzify the fuzzy number: transportation problem application’, Fuzzy Sets Syst., 2000, 116, (1), pp. 39 (doi: 10.1016/S0165-0114(99)00033-0).
    15. 15)
      • 13. Lin, D.Y., Boyles, S., Valsaraj, V., Waller, S.T.: ‘Fuzzy reliability assessment for traffic data’, J. Chin. Inst. Eng., 2012, 35, (3), pp. 114 (doi: 10.1080/02533839.2012.747250).
    16. 16)
      • 3. Turner, S.: ‘Quality control procedures for archived operations traffic data: synthesis of practice and recommendations’. Final Report to the Federal Highway Administration, Prepared by Texas Transportation Institute under contract to Battelle, March 2007.
    17. 17)
      • 18. Yang, Q., Koutsopoulos, H.N.: ‘A microscopic traffic simulator for evaluation of dynamic traffic management systems’, Transp. Res. C, 1996, 4, (3), pp. 113129 (doi: 10.1016/S0968-090X(96)00006-X).
    18. 18)
      • 9. Vanajakshi, L., Rilett, L.R.: ‘System wide data quality control of inductance loop data using nonlinear optimization’, J. Comput. Civ. Eng., 2006, 20, (3), pp. 187196 (doi: 10.1061/(ASCE)0887-3801(2006)20:3(187)).
    19. 19)
      • 7. Daganzo, C.: ‘Fundamentals of transportation and traffic operations’ (Pergamon, Oxford, UK, 1997).
    20. 20)
      • 17. Kuhne, R., Michalopoulos, P.: ‘Continuum flow modelsChapter 5 in Traffic Flow Theory, (Transportation Research Board, Washington, DC, 1997, Review edn.).
    21. 21)
      • 6. Kwon, J., Chen, C., Varaiya, P.: ‘Statistical methods for detecting spatial configuration errors in traffic surveillance sensors’, Transp. Res. Rec., 2004, 1870, (1), pp. 124132 (doi: 10.3141/1870-16).
    22. 22)
      • 14. Weijermars, W.A.M., Van Berkum, E.C.: ‘Detection of invalid loop detector data in urban areas’, Transp. Res. Rec., 2006, 1945, (1), pp. 8288 (doi: 10.3141/1945-10).
    23. 23)
      • 8. Nihan, N.: ‘Aid to determining freeway metering rates and detecting loop errors’, J. Transp. Eng., 1997, 123, (6), pp. 454458 (doi: 10.1061/(ASCE)0733-947X(1997)123:6(454)).
    24. 24)
      • 12. de Ona, J., Gomez, P., Merida-Casermeiro, E.: ‘Method to detect malfunctioning traffic count stations’, IET Intell. Transp. Syst., 2012, 6, (4), pp. 364371 (doi: 10.1049/iet-its.2011.0102).
    25. 25)
      • 4. Klein, L.A., Mills, M.K., Gibson, D.R.P.: ‘Traffic Detector Handbook’, (US Department of Transportation, Federal Highway Administration, 2006, Vol. 1, 3rd edn.).
    26. 26)
      • 16. Richards, P.I.: ‘Shock waves on the highway’, Oper. Res., 1956, 4, (1), pp. 4251 (doi: 10.1287/opre.4.1.42).
    27. 27)
      • 1. Turner, S.: ‘Defining and measuring traffic data quality’. Proc. Traffic Data Quality Workshop, 2003.
    28. 28)
      • 11. Wall, Z.R., Dailey, D.J.: ‘Algorithm for detecting and correcting errors in archived traffic data’, Transp. Res. Rec., 2003, 1855, (1), pp. 183190 (doi: 10.3141/1855-23).
    29. 29)
      • 2. Margiotta, R.: ‘State of the practice for traffic data quality’. Proc. Traffic Data Quality Workshop, 2002.
    30. 30)
      • 5. Chen, C., Kwon, J., Rice, J., Skabardonis, A., Varaiya, P.: ‘Detecting errors and imputing missing data for single-loop surveillance systems’, Transp. Res. Rec., 2003, 1855, (1), pp. 160167 (doi: 10.3141/1855-20).
    31. 31)
      • 15. Lighthill, M.J., Whitham, G.B.: ‘On kinematic waves. II. A theory of traffic flow on long crowded roads’. Proc. R. Soc. Lond. A, Math. Phys. Sci., 1955, vol. 229, no. 1178, pp. 317345.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2013.0094
Loading

Related content

content/journals/10.1049/iet-its.2013.0094
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading