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Dynamic prediction of traffic volume through Kalman filtering theory

Author

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  • Okutani, Iwao
  • Stephanedes, Yorgos J.

Abstract

Two models employing Kalman filtering theory are proposed for predicting short-term traffic volume. Prediction parameters are improved using the most recent prediction error and better volume prediction on a link is achieved by taking into account data from a number of links. Based on data collected from a street network in Nagoya City, average prediction error is found to be less than 9% and maximum error less than 30%. The new models perform substantially (up to 80%) better than UTCS-2.

Suggested Citation

  • Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
  • Handle: RePEc:eee:transb:v:18:y:1984:i:1:p:1-11
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    Cited by:

    1. Xing, Tao & Zhou, Xuesong & Taylor, Jeffrey, 2013. "Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 66-90.
    2. Alireza Ermagun & David Levinson, 2017. "Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending," Working Papers 000166, University of Minnesota: Nexus Research Group.
    3. Xi Zou & David Levinson, 2006. "Detecting the Breakdown of Traffic," Working Papers 000034, University of Minnesota: Nexus Research Group.
    4. Dia, Hussein, 2001. "An object-oriented neural network approach to short-term traffic forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 253-261, June.
    5. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    6. Lederman, Roger & Wynter, Laura, 2011. "Real-time traffic estimation using data expansion," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1062-1079, August.
    7. Dongjoo Park & Laurence Rilett & Byron Gajewski & Clifford Spiegelman & Changho Choi, 2009. "Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting," Transportation, Springer, vol. 36(1), pages 77-95, January.
    8. Wild, Dieter, 1997. "Short-term forecasting based on a transformation and classification of traffic volume time series," International Journal of Forecasting, Elsevier, vol. 13(1), pages 63-72, March.
    9. Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    10. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    11. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.

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