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Statistical Classification of Highway Segments for Improving the Efficiency of Short-term Traffic Count Planning
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 Title & Authors
Statistical Classification of Highway Segments for Improving the Efficiency of Short-term Traffic Count Planning
Jung, YooSeok; Oh, JuSam;
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 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
 Keywords
short-term traffic count;interval;linear regression;coefficient of variance;Shapiro-Wilk W-test;
 Language
Korean
 Cited by
 References
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