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학술대회 Statistical Traffic Generation Methods for Urban Traffic Simulation
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저자
송혜원, 민옥기
발행일
201802
출처
International Conference on Advanced Communications Technology (ICACT) 2018, pp.247-250
DOI
https://dx.doi.org/10.23919/ICACT.2018.8323712
협약과제
18HS4100, 도시 교통 문제 개선을 위한 클라우드 기반 트래픽 예측 시뮬레이션 SW 기술 개발, 민옥기
초록
The urban traffic analysis is an important issue in government strategies, and there are diverse researches to address urban traffic states including congestion states. In this paper, we focus on the traffic simulation technology of various methods to analyse urban traffic states. Especially, traffic demand estimation and generation is one of key functions for simulation results to reflect real urban traffic states well. Thus, we propose the traffic demand estimation process for urban traffic simulation using trip estimation model based on L1 regularized regression model and learning the trip estimation model with trajectory data in this paper. Also, we apply the traffic demand estimation process to a case of Gangdong-gu, Seoul. Finally, we show the estimation results and simulation results by the SALT Traffic Simulator based on SUMO (Simulation of Urban MObility), so that the estimated trips are similar to real traffic patterns and the simulation results from estimated trips is within about 10% errors coverage.
키워드
Regularized Regression Models, Traffic Demand (Trip) Estimation, Urban Traffic Generation Model, Urban Traffic Simulation
KSP 제안 키워드
Generation model, Regression Model, Regularized Regression, Simulation of Urban Mobility(SUM0), Simulation technology, Traffic Generation, Traffic demand estimation, Traffic pattern, Traffic simulator, Trajectory Data, Urban traffic simulation