PURPOSES : Traffic volume, an important basic data in the field of road traffic, is collected from traffic survey equipment installed at certain locations, which sometimes results in missing traffic volume data and abnormal detection. Therefore, this study presents various missing correction techniques using traffic characteristic analysis to obtain accurate traffic volume statistics. METHODS : The fundamental premise behind the development of a traffic volume correction and prediction model is to set the corrected data as the reference value, and the traffic volume correction and prediction process for the outliers and missing values in the raw data were performed based on the set values. RESULTS : The simulation results confirmed that the algorithm combining seasonal composition, quantile AD, and aggregation techniques showed a detection performance of more than 91% compared with actual values. CONCLUSIONS : Raw data collected due to difficulties faced by traffic survey equipment will result in missing traffic volume data and abnormal detection. If these abnormal data are used without appropriate corrections, it is difficult to accurately predict traffic demand. Therefore, it is necessary to improve the accuracy of demand prediction through characteristic analysis and the correction of missing data or outliers in the traffic data.

1. 서론
2. 선행연구
3. 자료의 수집
4. 도로교통량 자료 보정법
    4.1. 원시자료의 보정 여부 판단 기준
    4.2. 보정기준 및 보정 방법 결정
    4.3. 장기간 누락 보정 방법
5. 도로교통량 자료 특성 분석
    5.1. 연도별 특성
    5.2. 월별, 요일별, 교통량 오차율 비교
    5.3. 이상치 정보 유형과 처리
6. 교통량 데이터의 보정 전후 특성
7. 결론
감사의 글

• 한대철(정회원 · 한국건설기술연구원 도로교통연구본부 수석연구원) | Han DaeCheol 교신저자
• 문학룡(정회원 · 한국건설기술연구원 도로교통연구본부 연구위원) | Moon HakRyong
• 변상철(한국건설기술연구원 도로교통연구본부 선임연구위원) | Byun SangCheal
• 이상수(정회원 · 아주대학교 공과대학 교통시스템공학과 교수) | Lee SangSoo