DOI QR코드

DOI QR Code

Statistical Classification of Highway Segments for Improving the Efficiency of Short-term Traffic Count Planning

효율적인 교통량 조사를 계획하기 위한 조사구간의 통계적 특성 분류 연구

Jung, YooSeok;Oh, JuSam
정유석;오주삼

  • Received : 2016.02.24
  • Accepted : 2016.05.17
  • Published : 2016.06.15

Abstract

PURPOSES : The demand for extending national highways is increasing, but traffic monitoring is hindered because of resource limitations. Hence, this study classified highway segments into 5 types to improve the efficiency of short-term traffic count planning. METHODS : The traffic volume trends of 880 highway segments were classified through R-squared and linear regression analyses; the steadiness of traffic volume trends was evaluated through coefficient of variance (COV), and the normality of the data were determined through the Shapiro-Wilk W-test. RESULTS : Of the 880 segments, 574 segments had relatively low COV and were classified as type 1 segments, and 123 and 64 segments with increasing and decreasing traffic volume trends were classified as type 2 and type 3 segments, respectively; 80 segments that failed the normality test were classified as type 4, and the remaining 39 were classified as type 5 segments. CONCLUSIONS : A theoretical basis for biennial count planning was established. Biennial count is recommended for types 1~4 because their mean absolute percentage errors (MAPEs) are approximately 10%. For type 5 (MAPE =19.26%), the conventional annual count can be continued. The results of this analysis can reduce the traffic monitoring budget.

Keywords

short-term traffic count;interval;linear regression;coefficient of variance;Shapiro-Wilk W-test

References

  1. California DOT, 2013. Annual Average Daily Truck Traffic on the California State Highway System.
  2. Ministry of Land, Infrastructure and Transport, 2013. Traffic Monitoring Guideline.
  3. Ministry of Land, Infrastructure and Transport, 2015. Yearbook of road statistics.
  4. Ministry of Land, Infrastructure and Transport, 2015. 2014 Annual Traffic Volume Report.
  5. New Jersey DOT, 2014. New Jersey Traffic Monitoring Program.
  6. New York State DOT, 2011. Traffic Data Report for New York State.
  7. Royston, P., 1992. Approximating the Shapiro-Wilk W-Test for non-normality. Stat. Comput. 2, 117-119. https://doi.org/10.1007/BF01891203
  8. Seber, G.A.F., Lee, A.J., 2012. Linear Regression Analysis. John Wiley & Sons.
  9. Thomas F. Barry, Jr., 2002. Traffic Monitoring Procedure.
  10. Vandervalk-Ostrander, A., 2009. AASHTO Guidelines for Traffic Data Programs. Aashto.
  11. Wang, C. (Ross), Tsai, Y. (James), 2013. Use of Reduction-Effectiveness Ratios to Evaluate Reduced Traffic Data Collection Plans. Transp. Res. Rec. J. Transp. Res. Board 2339, 13-18. https://doi.org/10.3141/2339-02
  12. Wiegand, K., n.d. 2013 Georgia Traffic Monitoring Program.
  13. Yoo Seok Jung, Ju Sam Oh, 2015. Determination of a Homogeneous Segment for Short-term Traffic Count Efficiency Using a Statistical Approach. International Journal of Highway Engineering :: Vol.17 No.4 pp.135-141.