메뉴 건너뛰기




Volumn 21, Issue 1-2, 2010, Pages 5-17

New travel time prediction algorithms for intelligent transportation systems

Author keywords

ATIS (advanced travelers information systems); Intelligent transportation systems; N ive bayesian classification; Rule based classification; Travel time prediction

Indexed keywords

BAYESIAN CLASSIFICATION; CLASSIFICATION TECHNIQUE; DYNAMIC ROUTE GUIDANCES; INTELLIGENT TRANSPORTATION SYSTEMS; LINEAR REGRESSION ALGORITHMS; PERFORMANCE COMPARISON; PREDICTION ALGORITHMS; PREDICTION SYSTEMS; RELATIVE ERRORS; ROAD NETWORK; RULE-BASED CLASSIFICATION; SWITCHING MODEL; TRANSPORTATION MANAGEMENT SYSTEMS; TRAVEL ROUTES; TRAVEL TIME; TRAVEL TIME PREDICTION;

EID: 76149135398     PISSN: 10641246     EISSN: 18758967     Source Type: Journal    
DOI: 10.3233/IFS-2010-0431     Document Type: Article
Times cited : (13)

References (15)
  • 1
    • 0035724797 scopus 로고    scopus 로고
    • Dynamic freeway travel time prediction using probe vehicle data: Link-based vs. path-based
    • TRB Paper No. 01-2887, Washington, D.C
    • M. Chen and S. Chien, Dynamic freeway travel time prediction using probe vehicle data: link-based vs. path-based, Journal of Transportation Research Record, TRB Paper No. 01-2887, Washington, D.C., 2001.
    • (2001) Journal of Transportation Research Record
    • Chen, M.1    Chien, S.2
  • 3
    • 33646740655 scopus 로고    scopus 로고
    • Spatio-temporal similarity analysis between trajectories on road networks
    • J.R.Hwang, H.Y. Kang and K.J. Li, Spatio-temporal similarity analysis between trajectories on road networks, In Proceedings of ER Workshops (2005), 280-289.
    • (2005) In Proceedings of ER Workshops , pp. 280-289
    • Hwang, J.R.1    Kang, H.Y.2    Li, K.J.3
  • 5
    • 0034432244 scopus 로고    scopus 로고
    • Day-to-day travel time trends and travel time prediction from loop detector data
    • No. 1717, TRB, National Research Council, Washington, D.C
    • J. Kwon, B. Coifman and P. Bickel, Day-to-day travel time trends and travel time prediction from loop detector data, Journal of Transportation Research Record, No. 1717, TRB, National Research Council, Washington, D.C., 2000, 120-129.
    • (2000) Journal of Transportation Research Record , pp. 120-129
    • Kwon, J.1    Coifman, B.2    Bickel, P.3
  • 6
    • 33646433965 scopus 로고    scopus 로고
    • A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes
    • Washington, D.C
    • J. Kwon and K. Petty, A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. 84th Transportation Research Board Annual Meeting, Washington, D.C., 2005.
    • (2005) 84th Transportation Research Board Annual Meeting
    • Kwon, J.1    Petty, K.2
  • 7
    • 0032155636 scopus 로고    scopus 로고
    • Forecasting multiple-period freeway link travel times using modular neural networks
    • D. Park and L.R. Rilett, Forecasting multiple-period freeway link travel times using modular neural networks, Journal of Transportation Research Record 1617 (1998), 163-170.
    • (1998) Journal of Transportation Research Record , vol.1617 , pp. 163-170
    • Park, D.1    Rilett, L.R.2
  • 8
    • 0032779078 scopus 로고    scopus 로고
    • Spectral basis neural networks for realtime travel tome forecasting
    • D. Park and L.R. Rilett, Spectral basis neural networks for realtime travel tome forecasting, Journal of Transport Engineering 125(6) (1999), 515-523.
    • (1999) Journal of Transport Engineering , vol.125 , Issue.6 , pp. 515-523
    • Park, D.1    Rilett, L.R.2
  • 9
    • 4544274995 scopus 로고    scopus 로고
    • A simple and effective method for predicting travel times on freeways
    • J. Rice and E. Van Zwet, A simple and effective method for predicting travel times on freeways, IEEE Transaction on Intelligent Transport Systems 5(3) (2004), 200-207.
    • (2004) IEEE Transaction on Intelligent Transport Systems , vol.5 , Issue.3 , pp. 200-207
    • Rice, J.1    Van Zwet, E.2
  • 12
    • 0030298951 scopus 로고    scopus 로고
    • Combining KOHONEN maps with ARIMA time series models to series models to forecast traffic flow
    • M. Van Der Voot, M. Dougherty and S. Watson, Combining KOHONEN maps with ARIMA time series models to series models to forecast traffic flow, Transportation Research Part C 4 (1996), 307-318.
    • (1996) Transportation Research Part C , vol.4 , pp. 307-318
    • van der Voot, M.1    Dougherty, M.2    Watson, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.