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      통계적인 기법을 활용한 동질성구간에 따른 교통량 수시조사 효율화 연구

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      https://www.riss.kr/link?id=A101760572

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      PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget.
      METHODS: Based on the statistical approach, a homogeneous segment in the same road section is determined. Statistical analysis using t-test, mean difference, and correlation coefficient are carried out for 10-year-long (2004-2013) short-term count traffic data and the MAPE of fresh data (2014) are evaluated. The correlation coefficient represents a trend in traffic count, while the mean difference and t-score represent an average traffic count.
      RESULTS : The statistical analysis suggests that the number of target segments varies with the criteria. The correlation coefficient of more than 30% of the adjacent segment is higher than 0.8. A mean difference of 36.2% and t-score of 19.5% for adjacent segments are below 20% and 2.8, respectively. According to the effectiveness analysis, the integration criteria of the mean difference have a higher effect as compared to the t-score criteria. Thus, the mean difference represents a traffic volume similarity.
      CONCLUSIONS : The integration of 47 road segments from 882 adjacent road segments indicate 8.87% of MAPE, which is within an acceptable range. It can reduce the traffic monitoring budget and increase the count to improve an accuracy of traffic volume estimation.
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      PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget. METHODS: Based on the statistical approa...

      PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget.
      METHODS: Based on the statistical approach, a homogeneous segment in the same road section is determined. Statistical analysis using t-test, mean difference, and correlation coefficient are carried out for 10-year-long (2004-2013) short-term count traffic data and the MAPE of fresh data (2014) are evaluated. The correlation coefficient represents a trend in traffic count, while the mean difference and t-score represent an average traffic count.
      RESULTS : The statistical analysis suggests that the number of target segments varies with the criteria. The correlation coefficient of more than 30% of the adjacent segment is higher than 0.8. A mean difference of 36.2% and t-score of 19.5% for adjacent segments are below 20% and 2.8, respectively. According to the effectiveness analysis, the integration criteria of the mean difference have a higher effect as compared to the t-score criteria. Thus, the mean difference represents a traffic volume similarity.
      CONCLUSIONS : The integration of 47 road segments from 882 adjacent road segments indicate 8.87% of MAPE, which is within an acceptable range. It can reduce the traffic monitoring budget and increase the count to improve an accuracy of traffic volume estimation.

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