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Volumn 25, Issue 3, 2006, Pages 173-192

Comparison of two non-parametric models for daily traffic forecasting in Hong Kong

Author keywords

Annual average daily traffic (AADT); Gaussian maximum likelihood (GML); Non parametric regression (NPR); Short term daily traffic forecasting

Indexed keywords

DATA ACQUISITION; FORECASTING; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; REGRESSION ANALYSIS;

EID: 33646421626     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.984     Document Type: Article
Times cited : (42)

References (13)
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    • Huang, K.Y.1    Mausal, P.M.2
  • 5
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    • A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow
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    • Lin WH. 2001. A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow. In Proceedings of 2001 IEEE Intelligent Transportation System Conference, Oakland (CA); 152-157.
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    • Lin, W.H.1
  • 8
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    • Comparison of parametric and nonparametric models for traffic flow forecasting
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    • Smith, B.L.1    Williams, B.M.2    Oswald, R.K.3
  • 9
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    • Comparison of four modeling techniques for short-term AADT forecasting in Hong Kong
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    • Tang, Y.F.1    Lam, W.H.K.2    Ng, P.L.P.3
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  • 13
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    • Nearest-neighbour methods for time series analysis
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.