연구동향 분석

시공간적 영향력을 반영한 딥러닝 기반의 통행속도 예측 모형 개발

김영찬 김준원 한여희 김종준 황제웅 2020년

논문상세정보
    • 저자 김영찬 김준원 한여희 김종준 황제웅
    • 제어번호 106594168
    • 학술지명 한국ITS학회논문지
    • 권호사항 Vol. 19 No. 1 [ 2020 ]
    • 발행처 한국ITS학회
    • 자료유형 학술저널
    • 수록면 1-16
    • 언어 Korean
    • 출판년도 2020
    • 등재정보 KCI등재
    • 소장기관 건국대학교 상허기념중앙도서관
    • 판매처
    유사주제 논문( 0)
인용/피인용

' 시공간적 영향력을 반영한 딥러닝 기반의 통행속도 예측 모형 개발' 의 참고문헌

  • Understanding the difficulty of training deep feedforward neural networks
    Glorot, X. [2010]
  • Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework
    Wang W. [2018]
  • The cell transmission model : A dynamic representation of highway traffic consistent with the hydrodynamic theory
  • The Seoul Transportation Information Center
  • The Prediction of Vehicle Speed Passing Urban Road Using Recurrent Neural Network Technique
    Choi Y. H. [2018]
  • The Perceptron : A Probabilistic Model for Information Storage and Organization in The Brain
  • The A fast learning algorithm for deep belief nets
  • Python for Data Analysis
    Mckinney W. [2013]
  • Perceptrons: An Introduction to Computational Geometry
    Minsky M. [1969]
  • Long short-term memory
  • Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data
    Ma X. [2015]
  • Learning representations by back-propagating errors
  • Forecasting short-term travel speed in a dense highway network considering both temporal and spatial relationship- Using a deep-learning architecture
    Lee M. S. [2016]
  • Deep Learning with Python by Francois Chollet
    Chollet F. [2018]
  • A simplified theory of kinematic waves. 1: general theory; II: Queuing at freeway bottle- necks; III: Multi-destination flows
    Newell G. F [1993]
  • A Deep-learning Approach to Predict Short-term Traffic Speeds Considering City-wide Spatio-temporal Correlations
    Jeon H. J. [2018]