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Transportation research. Part C, Emerging technologies, v.19 no.6, 2011년, pp.1157 - 1170
Yu, B. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China) , Lam, W.H.K. , Tam, M.L.
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Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple r...
Transportation Research Part C Cathey 11 3 241 2003 10.1016/S0968-090X(03)00023-8 A prescription for transit arrival/departure prediction using automatic vehicle location data
Transportation Research Record Chan 2121 102 2009 10.3141/2121-11 Real-time estimation of arterial travel times with spatial travel time covariance relationships
Transportmetrica Chang 6 1 19 2010 10.1080/18128600902929591 Dynamic multi-interval bus travel time prediction using bus transit data
Computer-Aided Civil and Infrastructure Engineering Chen 19 5 364 2004 10.1111/j.1467-8667.2004.00363.x A dynamic bus arrival time prediction model based on APC data
Journal of Transportation Engineering Chien 129 6 608 2003 10.1061/(ASCE)0733-947X(2003)129:6(608) Dynamic travel time prediction with real-time and historic data
Journal of Transportation Engineering Chien 128 5 429 2002 10.1061/(ASCE)0733-947X(2002)128:5(429) Dynamic bus arrival time prediction with artificial neural networks
Cristianini 2000 An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Ding, Y., Chien, S., 2000. The prediction of bus arrival times with link-based artificial neural networks. In: Proceedings of the Fifth Joint Conference on Information Sciences, Atlantic City, NJ.
Huang, S.H., Ran, B., 2003. An Application of Neural Network on Traffic Speed Prediction under Adverse Weather Condition. The 82nd Annual Meeting of the Transportation Research Board, Washington, DC.
Jeong, R., Rilett, L.R., 2004. Bus Arrival Time Prediction Using Artificial Neural Network Model. In: 7th International IEEE Conference on Intelligent Transportation Systems: (ITSC 2004), Washington DC.
Neural Networks Moller 6 4 523 1993 10.1016/S0893-6080(05)80056-5 A scaled conjugate gradient algorithm for fast supervised learning
Journal of the Eastern Asia Society for Transportation Studies Park 7 1859 2007 Transit travel time forecasts for location-based queries: implementation and evaluation
Journal of Public Transportation Shalaby 7 1 41 2004 10.5038/2375-0901.7.1.3 Prediction models of bus arrival and departure times using AVL and APC data
Transportation Research Part C Smith 10 4 303 2002 10.1016/S0968-090X(02)00009-8 Comparison of parametric and nonparametric models for traffic flow forecasting
Transportmetrica Tam 4 3 179 2008 10.1080/18128600808685688 Using automatic vehicle identification data for travel time estimation in Hong Kong
Tam, M.L., Lam, W.H.K., 2009. Short-term Travel Time Prediction for Congested Urban Road Networks. The 88th Annual Meeting of the Transportation Research Board, Washington, DC.
10.1016/j.trc.2009.04.007 van Hinsbergen, C.P.I.J., van Lint, J.W.C., van Zuylen, H.J., 2009. Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C 17(5), 498-509.
10.1016/j.trc.2005.03.001 van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J., 2005. Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research Part C 13(5-6), 347-369.
IEEE Transactions on Neural Networks Vapnik 10 5 988 1999 10.1109/72.788640 An overview of statistical learning theory
Vapnik 2000 The Nature of Statistical Learning Theory
Transportation Research Part C Vlahogianni 13 3 211 2005 10.1016/j.trc.2005.04.007 Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach
Transportation Research Part C Vlahogianni 14 5 351 2006 10.1016/j.trc.2006.09.002 Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume
Wall, Z., Dailey, D.J., 1999. An algorithm for predicting the arrival time of mass transit vehicle using Automatic Vehicle Location data. The 78th Annual Meeting of the Transportation Research Board, Washington, DC.
Transportation Research Part C You 8 1-6 231 2000 10.1016/S0968-090X(00)00012-7 Development and evaluation of a hybrid travel time forecasting model
Journal of Intelligent Transportation Systems Yu 10 4 151 2006 10.1080/15472450600981009 Bus arrival time prediction using support vector machines
Journal of Advanced Transportation Yu 44 3 193 2010 10.1002/atr.136 A hybrid model for bus arrival time prediction
Proceedings of the Institution of Civil Engineers - Transport Yu 163 1 3 2010 10.1680/tran.2010.163.1.3 Bus travel-time prediction based on bus speed
Transportation Research Part C Zhang 11 3-4 187 2003 10.1016/S0968-090X(03)00026-3 Short-term travel time prediction
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