Predictive Modeling of the Bus Arrival Time on the Arterial using the Real-Time BIS Data

Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. So, bus information system is in haste introduced into their bus transportation systems in the cities, and also its extension taken into consideration in some of the cities which already imported the bus information system. However, bus information data such as the present bus location, the user waiting time, the bus arrival time, and so on are not correctly provided in most of the cities putting the bus information system into operation because the proper models for predicting the bus arrival time are not suggested yet. The purpose in this study is to investigate the real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze the real-time bus traffic characteristic data such as bus travel speed, inter-arrival time, the number of vehicles, etc. in the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments such as the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for the real-time bus arrival time prediction on the bus stops of the arterial under the study. From the bus roadway and traffic characteristic analyses on the each unit segment, and the integrated model construction and verification for predicting the real-time bus arrival time on the bus stops of the urban arterial under the study, the following conclusions were drawn: ⅰ) Roadway characteristics were found to show a little difference in the width and length of the roadway, the number of the unit segments, and the figure of intersection on the arterial under the study, but traffic characteristics were found not to show a distinct difference in the number of vehicles, the travel speeds, and the inter-arrival times on the arterial under the study. ⅱ) Signal operation characteristics were found to show a considerable difference in the Green time ratios depending on the signalized intersections and time periods within the study segments, but all the arterial under the study segments were found to be put in operation by the real-time signal progressive operation system with the bus information system, except the Samsanro. ⅲ) Bus traffic characteristics were found not to show a distinct difference in the number of buses and routes passed on the arterial, but they were found to be a distinct difference in the time intervals, the travel speeds and the travel times depending on the unit segment under the study. Especially, the travel times at the node were found to show a distinct difference in Green and Red signals. ⅳ) Unit segment models were needed to be differently constructed based on the unit segments and time periods. Particularly, the WSM1 was shown to be correlated with the bus traffic characteristics during the morning and 1-day periods, the WSM2 correlated during the noon period, and the ESM2 correlated during afternoon period at the bus stop, respectively. ⅴ) ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the 1-day period, and the WSM1 correlated during the morning period at the intersection. Also, the ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the noon period and afternoon period at the node, respectively. ⅵ) ESM1 and ESM2 were shown to be correlated with the bus traffic characteristics during the 1-day and morning periods, respectively at the travel section. And the WSM1 was shown to be correlated with the bus traffic characteristics during the noon period, and the ESM1 and ESM2 correlated during the afternoon period at the travel section. ⅶ）Integrated predictive model was shown to have a high explanatory power in the coefficient of determination () of 0.945 or more, and a high significance at the F-significance level of 0.000 and the t-significance level of 0.000. Also, integrated predictive model was found to be very valid in testing between the observed and expected travel times at the 95 % level of confidence. Thus, it was concluded that the integrated predictive model would be very valid in predicting the real-time bus arrival time in the cities putting the bus information system(BIS) in operation.Abstract ⅰ Nomenclature ⅵ List of Tables ⅷ List of Figures ⅸ 제 1 장 서 론 1 1.1 연구배경 1 1.2 연구목적 및 필요성 2 1.3 연구내용 및 방법 3 1.3.1 연구내용 3 1.3.2 연구방법 3 제 2 장 문헌 연구 6 2.1 국외 문헌연구 6 2.1.1 AVL에 의한 운행자료 관측 6 2.1.2 GPS에 의한 운행자료 관측 8 2.2 국내 문헌연구 10 2.2.1 GPS에 의한 운행자료 관측 10 2.2.2 자료의 이상치 제거 11 2.2.3 모형에 의한 도착시간 예측 14 제 3 장 자료수집 및 분석 18 3.1 도로선정 및 분석 18 3.1.1 대상도로의 선정 18 3.1.2 대상도로의 분석 20 3.1.3 신호체계분석 31 3.2 자료수집 35 3.2.1 자료의 구성 체계 35 3.2.2 운행 자료수집 38 3.3 운행자료 분석 41 3.3.1 운행대수 분석 41 3.3.2 배차간격 분석 42 3.3.3 운행시간 분석 44 제 4 장 모형구축 및 검증 51 4.1 모형구축 51 4.2 구축방법 52 4.2.1 평활화기법(smoothing method, SM) 52 4.2.2 칼만 필터(Kalman Filter)기법 55 4.2.3 중 회귀기법 58 4.2.4 모형채택 59 4.3 개별모형 60 4.3.1 모형구축 60 4.3.2 버스정류장 모형 71 4.3.3 신호교차로 모형 75 4.3.4 순행구간 모형 78 4.4 통합모형 83 4.4.1 모형구축 83 4.4.2 모형검증 85 4.4.3 오차비교 91 제 5 장 결 론 9

A Study on the Transient Performance of a Diesel Engine Using a High-Frequency Engine Model and a Thermal Management Model

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 8. 민경덕.According the continuously tightening restrictions on emissions of internal combustion engines and fuel economy, numerous researches on combustion, driving strategies and the improvement of thermal efficiency have proceeded worldwide. The engine thermal management system (ETM), recognized as a measure for the enlargement of the engine efficiency, is the field which concerns the investigation of the heat transfers between engine and powertrain parts, and the improvement of fuel economy by controlling and reuse of the thermal energy. Recent researches, as parts of the Smart Cooling field, concern the technologies which improve the thermal efficiency of internal combustion engines by controlling the coolant and lubricant flows at transient operating conditions, and development of a new cooling model. The transient conditions stand for the change between engine operating conditions and the period in which engine is adequately warmed up. Particularly at the cold atmospheric condition, the engine thermal efficiency is seriously decreased due to the excessive heat losses since the coolant and lubricant are not sufficiently warmed. Even though there are numerous researches on the cold-start and transient operating conditions, there also needs a precise simulation model to describe above transient conditions. In this study, a simulation model was developed to observe the total heat losses and the temperature changes at the transient operating conditions by considering both the 1-d engine model and the engine thermal management model. The model considers the combustion heat loss, the friction heat loss and the exhaust heat loss altogether. The combustion heat loss is calculated at the 1-D engine model at each time step. The performance of the target diesel engine at the transient operating condition was measured based on the simulation model.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objective 5 Chapter 2. Simulation Model 7 2.1 High Frequency Engine Model (HFEM) 7 2.2 Engine Thermal Management Model 13 2.2.1 Thermal Mass Model 15 2.2.2 Heat Transfer Model 19 2.2.3 Coolant Model 20 2.2.4 Lubricant Model 23 2.2.5 Friction Mode 25 2.2.6 Exhaust Model 28 Chapter 3. Simulation Results 30 3.1 Simulation Setup 30 3.2 Simulation Results 31 Chapter 4. Conclusions 36 References 38 Korean Abstract 40Maste

섬유기반 연속체 모델을 이용한 삼차원 텍스타일 복합재료의 역학적 해석

학위논문 (박사)-- 서울대학교 대학원 : 재료공학부, 2016. 2. 유웅열.Fiber reinforced polymer composites are widely used in industrial field, such as military, aerospace and automobile, and in the spotlight of using structural materials. For this reason, mechanical analysis and manufacturing process composites are researched in numerus studies. Moreover, many structural analysis of composites are published for improving its properties and reinforcing its properties. Unit cell approach is major concept of mechanical analysis of composite and three dimensional (3D) modelling of composite structure is well established. This unit cell analysis has high accuracy and reflecting complicated composite structures, however it has high cost of computing time and modeling process. Moreover it cannot be considered structural effect from deformation and loading condition. For this reason, new approach of composite mechanical analysis is developed with continuum based model in this study. The aim of this method is continuum analysis with fast computing time and considering structural effect of textile composite structures with one-step 3D structural analysis. New numerical analysis model, called fiber based continuum model (FBM), is continuum based algorithm considered fiber orientation and structure of a textile composite. Fiber architecture is important factor of mechanical properties of a composite, so FBM is focused on fiber structure and its change inside of a composite. Moreover, this method is used fiber and matrix properties for numerical analysis, so structural analysis can be done with minimum parameters. Furthermore, based on layer method and modified ply discount method, Failure behavior can be predicted with Pucks failure citation. 3D textile composite fabrication and structure analysis method are developed for numerical validation and characterization, in the next chapter. FBM analysis can be used for arbitrary 3D textile composites with yarn path function, so verification works is needed with several 3D structures. In this study, 3D five axis braided and orthogonal woven composite is manufactured and tested. For this, 3D weaving method was developed in laboratory scale. Moreover, before the mechanical test of composites, structural analysis is done with micro-CT image. Finally, FBM is verified with experimental of 3D textile composites. Tensile and bending test was done for characterization of composites. The experimental results of which were compared with simulated results, demonstrating that the current numerical model can properly predict the mechanical behavior of 3D fiber-reinforced composites. Moreover, based on FBM analysis, application of 3D textile composite is investigated.Chapter 1. Introduction 1 1.1 Fiber reinforced polymer composites (FRPs) 1 1.2 Mechanical behavior prediction of FRPs 3 1.2.1 Composite stiffness prediction theories 3 1.2.2 Failure criterion of FRPs 8 1.3 Research objectives 19 Chapter 2. Fiber based continuum model (FBM) 21 2.1 Fiber based analysis mechanism 21 2.2 Numerical approach 22 2.2.1 Methodology of mechanical prediction of FRPs 22 2.2.2 Algorithm of update yarn pattern 23 2.2.3 Stiffness calculation of FRPs 24 2.2.4 Strength prediction and damage propagation 30 2.2.5 Stress update and processing increment 33 2.3 Characterization and validation of the model 34 2.3.1 Model characterization 34 2.3.2 Verification of mechanical properties prediction 40 2.3.3 3D structure calculation 42 2.4 Summary 43 Chapter 3. Fabrication and modelling of 3D textile composites 44 3.1 3D braided structures 44 3.1.1 Fabrication of 3D five-axis braided preforms 44 3.1.2 Unit cell and fiber based continuum modelling 46 3.2 3D orthogonal woven structures 50 3.2.1 Fabrication of 3D orthogonal woven preforms 50 3.2.2 Unit cell and fiber based continuum modelling 53 3.3 Forming and characterization of 3D textile composites 56 3.3.1 Vacuum assisted resin transfer molding (VARTM) process 56 3.3.2 Structure and material properties characterization 58 3.4 Summary 61 Chapter 4. Mechanical analysis of 3D textile composites 63 4.1 Experimental 64 4.1.1 Tensile test of 3D composites 64 4.1.2 Bending test of 3D composites 65 4.2 Theoretical analysis 66 4.2.1 Simulation procedure and boundary condition 67 4.2.2 Result treatment and post calculation 68 4.3 Results and discussions 70 4.3.1 Experimental results 70 4.3.2 Numerical results and comparison 76 4.3.3 Discussions and applications 85 4.4 Summary 89 Chapter 5. Concluding remarks 90 References 93 Korean Abstract 101Docto

租稅回避의 否認에 관한 租稅訴訟의 構造

학위논문(석사) --서울대학교 대학원 :법학과,2008.2Maste

일부 지방 중소병원 응급실 내원환자의 특성 연구

학위논문(석사)--서울대학교 보건대학원 :보건학과 역학전공,1997.Maste

Three-Dimensional Braided Composites for Regenerating Articular Cartilage

학위논문(석사) --서울대학교 대학원 :재료공학부,2010.2.Maste

Intelligent Credit Rating Model for Korean Companies using Multiclass Support Vector Machines

신용등급의 투자자나 채권자 등 다양한 이해관계자들이 특정 기업이나 그 기업에서 발행된 채권에 대한 위험을 평가하는 지표로서, 정교한 등급평가는 개인의 투자위험 뿐만 아니라 금융시장 전체에 영향을 미칠 수 있는 중요한 요소 중 하나이다. 이러한 이유로 지금까지 기업 신용등급평가에 대한 다양한 연구가 진행되어 왔으며, 최근에는 특히 복잡한 재무데이터의 특성을 모형에 보다 잘 반영할 수 있는 것으로 알려진 인공지능기법, 특히 인공신경망의 우수한 예측능력을 활용한 연구가 활발하게 진행되고 있다. 그러나, 인공신경망 기법은 입력자료 분포를 추정하기 위해 다양의 학습데이터가 필요하고, 과도적합문제(ovefitting)로 인해 일반화의 어려움이 있을 뿐만 아니라, 지역 최소값(local minima)을 피하기 위한 초기화 작업이 경험에 의존해야 하고, 기본적으로 암상자 모형이라서 각 변수의 중요도 등 모형을 해석하기 어렵다는 점 등이 한계점으로 지적되어 왔다. 특히, 기업채원의 등급평가와 같이 다분류 문제의 경우에는 각 등급별 데이터가 회소하여 인공신경망처럼 다량의 학습데이터를 필요로 하는 모형은 구축이 불가능한 경우가 발생할 수 있다. 본 연구에서는 이에 대한 해결방안으로 최근 각광 받고 있는 다분류 support vector machine (SVM)을 채권등급평가에 적용하고자 한다. SVM은 명백한 이론적 근거에 기반하므로 결과 해석이 용이하고, 실제 응용에 있어서 인공신경망 수준의 높은 성과를 내며, 적은 학습자료만으로 신속하게 분류학습을 수행할 수 있다는 장점을 갖고 있다. 또한 기존은 학습 알고리즘은 경험적 위험 최소화 원칙(empirical risk minimization)을 구현하는 것인데 비해, SVM은 구조적 위험 최소화 원칙(structural risk minimization)에 기반하므로 과도적합문제를 어느 정도 피할 수 있다는 장점도 갖고 있다. 본 연구에서는 이 같은 가능성을 확인해 보기 위해, 다분류 SVM을 한국기업의 채권평가 사례에 적용해 보았다. 타 비교모형에 대한 우월성을 검증해 보기 위해, 인공신경망 및 다중판별분석과 그 성과를 비교하였으며, 분석 결과 다분류 SVM이 다른 비교대상에 비해 통계적으로 유의하게 우수한 성과차이를 보임을 확인할 수 있었다