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송도국제도시 연결도로의 유고상황 발생에 따른 신도시 내부 영향 분석
홍기만,김태균,Hong, Ki-Man,Kim, Tea-gyun 인천대학교 도시과학연구원 2021 도시과학 Vol.10 No.1
The purpose of this study is to analysis the impact on the inside of the new city when an incidents occurs on the Songdo International City connecting road, which has a limited access. The analysis data used KTDB's O/D and network data of the Seoul metropolitan area. In addition, the scenario composition applied a method of reducing the number of lanes on the road according to the situation of incidents, targeting bridges advancing from Songdo International City to the outside in the morning peak hours. The analysis method analyzed the traffic volume, total travel time, total travel kilometer, and route change in the new city based on the results of the traffic allocation model. As a result of the analysis, the range of influence was shown to two types. First, of the seven bridges, Aam 3, Aam 2, and Aam 1 were analyzed to have an impact only in some areas of the northwestern part of the new city. On the other hand, the remaining bridges were analyzed to affect the new city as a whole. The analysis results of this study are expected to be used as basic data to establish the scope of internal road network management when similar cases occur in the future.
IPA를 이용한 스마트 교통안전 시스템의 만족도 분석 연구
홍기만,김종훈,김종훈,하정아,김광호 한국재난정보학회 2022 한국재난정보학회 논문집 Vol.18 No.4
Purpose: The purpose of this study is to derive improvements through user satisfaction analysis for the smart traffic safety system being applied to improve traffic safety. Method: A survey-based IPA analysis was used to derive system and service improvements for groups of drivers and pedestrians. Result: As a result of the analysis, both drivers and pedestrian groups showed that Quadrant 1(Keep up the Good Work) was 'Perception of risk information', and Quadrant 3(Low Priority) was 'Reliability of warning information'. On the other hand, 'AI display suitability', which was analyzed as Quadrant 1 (Keep up the Good Work) in the driver group, was found to be Quadrant 3(Low priority) in the pedestrian group. Conclusion: Satisfaction factors for smart pedestrian safety systems may vary depending on users, and it is judged that user-centered system construction and service provision are necessary. 연구목적: 본 연구는 교통안전 향상을 위해 적용중인 스마트 교통안전 시스템에 대하여 이용자의 만족도 분석을 통한 개선 사항을 도출하는데 목적이 있다. 연구방법: 설문조사 기반의 IPA 분석을 통해 운전자와 보행자 그룹의 시스템 및 서비스 개선 항목을 도출하였다. 연구결과: 분석 결과, 운전자와 보행자 그룹 모두 Quadrant 1(Keep up the Good Work)은 ‘위정정보 인지성’, Quadrant 3(Low priority)은 ‘주의정보의 정시성’으로 나타났다. 반면 운전자 그룹에서 Quadrant 1(Keep up the Good Work)로 분석된 ‘AI 디스플레이 적합성’이 보행자 그룹에서는 Quadrant 3(Low priority)으로 나타났다. 결론: 스마트 보행안전시스템은 이용자에 따라 만족 요인이 달라질 수 있으며, 이용자 중심의 시스템 구축 및 서비스 제공이 필요할 것으로 판단된다.
도로지체함수의 용량과 초기통행속도를 이용한 네트워크 정산 최적화 모형 개발 연구
홍기만,김태균,조중래,홍영석 한국도로학회 2021 한국도로학회논문집 Vol.23 No.1
PURPOSES : The purpose of this study is to build an optimization model using the capacity and initial travel speed of the volume delay functions for network calibration performed in the traffic demand analysis process. METHODS : The optimization model contains an error term between the observed traffic volume and estimated traffic volume, based on the user equilibrium principle, and was constructed as a bi-level model by applying range constraints on capacity and travel time. In addition, we searched the split section to apply the method of adjusting the section instead of adjusting the single link. The optimization model is constructed by applying the warm-start method using the bush of the origin-based model so that parameter adjustment and traffic assignment are repeatedly executed within the model and the convergence of the model configured %RSSE. RESULTS : As a result of analysis using the toy network, the optimization model is that the observed traffic volume is estimated when there are no restrictions and, when the constraint conditions were set, the error with the observed traffic volume and error rate was significantly reduced. As a result of the comparative analysis of the trial-and-error methods, KTDB optimum values, and optimization models in empirical analysis using a large-scale network, the evaluation indexes (e.g., RMSE and %RMSE) were significantly improved by applying the optimization model. CONCLUSIONS : Based on the empirical analysis, the optimization model of this study can be applied to large-scale networks and it is expected that the efficiency and reliability of road network calibration will be improved by repeatedly performing parameter adjustment and traffic assignment within the model.