심층신경망 모형을 활용한 대중교통 이용자의 환승시간 추정에 관한 연구
활용도 Analysis
논문 Analysis
연구자 Analysis
저자
이경재
김수재
문형택
한재윤
추상호
제어번호
106594166
학술지명
한국ITS학회논문지
권호사항
Vol.
19
No.
1
[
2020
]
발행처
한국ITS학회
자료유형
학술저널
수록면
32-43
언어
Korean
출판년도
2020
등재정보
KCI등재
소장기관
건국대학교 상허기념중앙도서관
판매처
'
심층신경망 모형을 활용한 대중교통 이용자의 환승시간 추정에 관한 연구' 의 참고문헌
대중교통 환승통행량 영향요인 분석: 대구시를 대상으로
대중교통 이용자의 환승교통수단선택 행태분석에 관한 연구
Travel Time and Transfer Analysis Using Trnasit Smart Card Data
Traffic Flow Prediction With Big Data : A Deep Learning Approach
Public Transportation Investigation 2018
Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data
Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
Inferring the Route-Use Patterns of Metro Passengers Based Only on Travel-Time Data Within a Bayesian Framework Using a Reversible-Jump Markov Chain Monte Carlo(MCMC)Simulation
Exploring the Relationship between Transfer Trips and Land Use
Development of Expressway Traffic Accident Prediction Model Using Deep Learning
Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data
Deep-Learning Architectures to Forecast Bus Ridership at the Stop and Stop-To-Stop Levels for Dense and Crowded Bus Networks
Deep learning for short-term traffic flow prediction
Deep Learning Methods and Applications
Deep Architecture for Traffic Flow Prediction
Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network
An efficient realization of deep learning for traffic data imputation
Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model
'
심층신경망 모형을 활용한 대중교통 이용자의 환승시간 추정에 관한 연구'
의 유사주제(
) 논문