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

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


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.


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


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