Abstract
Recently, Korean Ministry of Construction and Transportation selects three cities as the Intelligent Transport Model City to build a test bed for research in Intelligent Transportation System (ITS). One of the most sought-after information in any ITS project is to provide arterial travel speed forecasts to travellers. To estimate the arterial travel speed, one needs to apply a mathematical model supplied with sensor data generated by roadside sensors and in-vehicle sensors. In this research effort, we develop a simple Bayesian estimator and an expanded neural network model to estimate arterial link travel speed. Input data used are from dual-loop detectors and probe vehicles with DSRC(Dedicated Short-range Communication) device. Data from one of model city, Jeonju, are used to generate test data for the simulation where the probe vehicle’s speed is random sampled from observed vehicles’ speed. Initial run shows that the neural network model developed can provide accurate estimates of arterial link speed using only probe vehicle’s speed data.
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References
Coifman, B.: Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Trans. Research 36A, 351–364 (2002)
Dailey, D.J.: A statistical algorithm for estimating speed from single loop volume and occupancy measurements. Trans. Research 33B, 313–322 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)
Kim, N., et al.: The development of free travel-time estimation and prediction models using neural networks. J. Korean Society for Transportation(JKST) 18, 47–59 (2000)
Kim, S., et al.: A new approach to estimate the link travel time by using AVL technology. JKST 17, 91–103 (1999)
Kim, Y., et al.: Estimation of link travel speed using single loop detector measurements for signalized arterial. JKST 15, 53–71 (1997)
Lee, S., Jang, H.: A study on link travel time prediction by short term simulation based on CA. JKST 21, 91–102 (2003)
Lin, W., et al.: Arterial travel time estimation for advanced traveler information systems. In: TRB 2003 (2003)
Ministry of Construction and Transportation, Jeonju System Architecture Handbook www.moct.go.kr
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Nanthwwichit, C., et al.: Application of probe vehicle data for real-time traffic state estimation and short-term travel time prediction on a free way. In: TRB 2003 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Park, T., Lee, S. (2004). A Bayesian Approach for Estimating Link Travel Time on Urban Arterial Road Network. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24707-4_114
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DOI: https://doi.org/10.1007/978-3-540-24707-4_114
Publisher Name: Springer, Berlin, Heidelberg
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