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논문 기본정보

대규모 미시교통시뮬레이션모형 구축을 위한 O/D 추정 방법 성능 비교 - 중력모형과 QUEENSOD 방법을 중심으로 -

논문 개요

기관명, 저널명, ISSN, ISBN 으로 구성된 논문 개요 표입니다.
기관명 NDSL
저널명 한국도로학회논문집 = International journal of highway engineering
ISSN 1738-7159,2287-3678
ISBN

논문저자 및 소속기관 정보

저자, 소속기관, 출판인, 간행물 번호, 발행연도, 초록, 원문UR, 첨부파일 순으로 구성된 논문저자 및 소속기관 정보표입니다
저자(한글) 윤정은,이철기,이환필,김경현,박원일,윤일수
저자(영문)
소속기관
소속기관(영문)
출판인
간행물 번호
발행연도 2016-01-01
초록 PURPOSES : The aim of this study was to compare the performance of the QUEENSOD method and the gravity model in estimating Origin-Destination (O/D) tables for a large-sized microscopic traffic simulation network. METHODS : In this study, an expressway network was simulated using the microscopic traffic simulation model, VISSIM. The gravity model and QUEENSOD method were used to estimate the O/D pairs between internal and between external zones. RESULTS: After obtaining estimations of the O/D table by using both the gravity model and the QUEENSOD method, the value of the root mean square error (RMSE) for O/D pairs between internal zones were compared. For the gravity model and the QUEENSOD method, the RMSE obtained were 386.0 and 241.2, respectively. The O/D tables estimated using both methods were then entered into the VISSIM networks and calibrated with measured travel time. The resulting estimated travel times were then compared. For the gravity model and the QUEENSOD method, the estimated travel times showed 1.16% and 0.45% deviation from the surveyed travel time, respectively. CONCLUSIONS : In building a large-sized microscopic traffic simulation network, an O/D matrix is essential in order to produce reliable analysis results. When link counts from diverse ITS facilities are available, the QUEENSOD method outperforms the gravity model.
원문URL http://click.ndsl.kr/servlet/OpenAPIDetailView?keyValue=03553784&target=NART&cn=JAKO201613752757349
첨부파일

추가정보

과학기술표준분류, ICT 기술분류,DDC 분류,주제어 (키워드) 순으로 구성된 추가정보표입니다
과학기술표준분류
ICT 기술분류
DDC 분류
주제어 (키워드) gravity model,QUEENSOD method,O/D estimation,microscopic traffic simulation model,genetic algorithm