ScholarWorks@Hanyang University ERICA Campus: Lee, Gunwoo

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Lee, Gunwoo (이건우)

COLLEGE OF ENGINEERING SCIENCES (DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING)

Research Area: 교통시스템공학

Tel 031-400-5156
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Korea:4||Vehicle emissions:3||MOVES:3||safety:3||Microscopic traffic simulation:3||ordered probit model:2||traffic safety:2||Speed management:2||artificial neural network:2||Deep learning:2||Baltic Dry Index:1||Battery electric vehicles:1||Bicycle speed data:1||Bicycle traffic monitoring:1||Bicycling environments:1||Big Data:1||Busan new port:1||Citi Bike:1||Competitiveness:1||Consumer intention:1||Container Terminal:1||Contingent valuation:1||Critical success factors:1||DTG (Digital Tacho Graph):1||Data Filtering:1||Decision tree:1||Deep Underground Tunnel:1||Deep learning model:1||Defined benefit:1||Defined contribution:1||Delivery motor scooters:1||Discrete choice model:1||Dispersion analysis:1||Drayage trucks:1||Endogeneity:1||Expressway:1||FCM:1||Food delivery motor scooter:1||Freeway capacity:1||Freight transportation:1||Freight truckinvolved crash:1||Fuel consumption prediction:1||GPS data:1||Geographically weighted regression:1||Health impacts:1||IMU sensor:1||Individual retirement pension:1||Intervehicle safety warning system (ISWS):1||Korean InDepth Accident Study database (KIDAS):1||Lifespan:1||Linear Regression:1||Liner shipping:1||Locally:1||Long ShortTerm memory:1||Longshortterm memory model:1||Machine learning:1||Macroscale vehicle emission estimation approach:1||Microemission factor:1||Microscale vehicle emission estimation approach:1||Microscopic simulation:1||Mobility:1||Mobility mode:1||Onoff Ramp Junction:1||Ordered probit model:1||Outlier Correction:1||PCADEA:1||Policy implications:1||Precursor:1||Priority:1||Public bicycle:1||Public bicycle usage:1||Public value:1||Random Forest:1||Random parameter ordered probit model:1||Recurrent Neural network:1||Repair and Maintenance Costs:1||Retirement pension system:1||Riding characteristics:1||Riding risks:1||Risk Analysis:1||Risky riding event:1||SDI:1||Seafarers:1||Seaport, electrical power consumption:1||Supply and demand:1||Support vector machine:1||Time of purchase:1||Traffic Control:1||Traffic Information:1||Traffic calming:1||Traffic crashes:1||Traffic violation:1||Trafficrelated air pollution exposure area:1||Trafficrelated public health:1||Transportation demand analysis:1||Travel Speed:1||Travel time saving:1||Truck platooning:1||Ttareungi:1||Turn Around Time (TAT):1||Twostage residual inclusion:1||Vehicle equipment:1||Vehicle safety:1||Vehicle trajectory:1||Vessel traffic service:1||Voyagebased approach:1||Weighted Regression(LWR):1||Willingness to pay:1||XGBoost:1||XGBoost Preliminary Feasibility Study:1||accelerometer:1||adoption:1||analytic hierarchy process:1||autonomous emergency braking system (AEBS):1||bicycles:1||bikeways:1||calibration:1||coastal ferry operator:1||complaint processing time:1||connected vehicle:1||container port:1||container shipping:1||container shipping industry:1||container ships:1||cox proportional hazard model:1||crash analysis:1||cruise tourism:1||crush extent:1||cycling stability:1||data and data science:1||data correction:1||data mining:1||deep learning:1||delivery scooter:1||delivery scooters:1||driving cycle:1||effect analysis of safety device:1||environment and health impacts:1||environmental factors:1||factor analysis:1||ferry route efficiency:1||global positioning system (GPS):1||gyro sensor:1||importanceperformance analysis:1||industrial structure:1||injury severity:1||jerk:1||kaplanmeier method:1||liner shipping:1||link emission factors:1||locally weighted regression (LWR):1||longterm recurrent convolutional network (LRCN):1||machine learning:1||marine accident:1||microlevel vehicle emission estimation:1||microscopic traffic simulation:1||motorcycle countermeasures:1||multicriteria decision making:1||multilayer perceptron:1||multiple regression analysis:1||naturalistic riding data:1||network screening:1||oceangoing container ships:1||operational efficiency:1||outlier detection:1||passenger mobility burden:1||probe vehicle data:1||random forest:1||random forest method:1||rearend collisions:1||regional comparisons:1||regression model:1||rider safety:1||riding safety monitoring:1||risky riding characteristics:1||risky riding event:1||road dangerous object:1||road problem reporting service solution service:1||safety performance:1||safety project and program implementation:1||safetyconstrained SBM:1||sample selection bias:1||seasonal variations:1||ship fuel consumption model:1||shore excursion expenditure:1||simulation:1||slackbased measure of efficiency (SBM):1||stopping distance index (SDI):1||structural equation modeling:1||supply chain integration:1||supply chain performance:1||support vector machine:1||support vector machine (SVM):1||surrogate safety measure (SSM):1||taxi driver wellness:1||traffic accidents:1||traffic simulation:1||trust:1||underground road junction:1||undesirable output:1||vehicle GPS data:1||vehicle emissions:1||공용연수:1||교통안전대체지표:1||교통정보:1||국소가중다항회귀분석:1||도로 위험 요소:1||라이더 안전:1||랜덤포레스트:1||머신러닝:1||민원 처리시간:1||배달 이륜차:1||분류기:1||서포트 벡터 머신:1||서포트벡터머신:1||수선유지비:1||안전 모니터링:1||예비타당성조사:1||위험 주행 이벤트:1||이륜차:1||이륜차 안전:1||이상치 보정:1||주행 안전성 평가:1||주행 자료:1||주행속도:1||척척해결서비스:1||카플란마이어 방법:1||콕스 비례 위험 모형:1||Acceleration noise:1||필터링:1||Accident:1||Air pollution:1||Air quality:1||Alternative marine power:1||Analytical Hierarchy Process:1||Artificial Neural Network:1||Automated vehicles:1||Autonomous vehicle concern:1||Autonomous vehicles:1||

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