Open Access
Issue |
MATEC Web Conf.
Volume 81, 2016
2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016)
|
|
---|---|---|
Article Number | 04007 | |
Number of page(s) | 5 | |
Section | Transportation System Modeling and Forecasting | |
DOI | https://doi.org/10.1051/matecconf/20168104007 | |
Published online | 25 October 2016 |
- J.X. Liu, Y.B. Li, J. WHUT (T.S.&E), Distribution Regularity Analysis of Ship Arrival and Departure at the Tianjin Port Main Channel, 32(2), 351–353(2008) [Google Scholar]
- J. Yu, W. Zhang, J.H. Jiang, P. Liao, J.T &T.E, Probability distribution of vessel traffic flow in Xi Jiang waterway, 6(2), 88–93 (2006) [Google Scholar]
- X.L. Han, Z.Q. Lu, L.F. Xi, Eur.J.O.R, A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time, 207, 1327–1340(2010) [Google Scholar]
- Z.R. Tan, X.P. Yan, L. Liu, J.T.I&S, Examination of Vessel Traffic Flow Distribution Model in Yangtze River Bridge Area, 28(2), 70–73(2010) [Google Scholar]
- H.B. Zhang, D.H. Jiang, J.P&W.E, Distribution law of ships to port and calculation of reasonable number of berths, 9, 51–56(2014) [Google Scholar]
- L.J. Wang, Xu, N.Q. Song, T. Xu, J.DMU, Distribution function and empirical study of container liner’s arrival discipline, 39(4),107–110(2013) [Google Scholar]
- Y.H. Tian, J.B. Chen, J.S&O.E,Vessel Traffic Flow Prediction Based on BP Neural Network, 39(1), 122–125(2010) [Google Scholar]
- S. Li, L.J. Liu, M. Zhai, J. Systm, E.T&P, Prediction for short-term traffic flow based on modified PSO optimized BP neural network, 32(9), 2045–2049(2012) [Google Scholar]
- J.G. Zhai, T.F. Yan, X.P. Yan, J.SHMU, Prediction of vessel traffic flow based on BP neural network and residual analysis, 34(1), 19–22(2013) [Google Scholar]
- Z.M. Liu, Z.X. Jia, X.L. Li, J.ECJTU, Traffic Volume Forecast Based on Gray Markov Chain Model, 29(1), 30–34(2012) [Google Scholar]
- C.M. Kong, Y. Han, J.F&E, Prediction of Volatile Traffic Volume Based on the Grey-Markov Model, 31(1), 92–96(2015) [Google Scholar]
- H.J. Liu, Z. Gao, Z.X. Wang, J. Zhang, J.S&T(Algorithm), Short-term Traffic Flow Forecasting Based on Exponential Smoothing and Markov Chains, 22(12), 132–135(2013) [Google Scholar]
- Q. Yan, I. Sherif, J.T&R, Part C, A Hidden Markov Model for short term prediction of traffic conditions on freeways, 43, 95–111(2014) [Google Scholar]
- Y.M. Wang, X.J. Yu, J.X. Chang, Q. Huang, J.EofWH, Pr-ediction of runoff based on BP neural network and Markov model 41(5), 14–17(2008) [Google Scholar]
- Y.P. Jing, X. Zhang, Y. Luo, J.N.A&F.U(Nat. Sci. Ed.), Forecasting of urban water demand based on combining Grey and BP neural network with Markov chain model, 39(7), 229–234(2011) [Google Scholar]
- Y.F. Wang, S.M. Cheng, M.H. Su, J.A.soft.C, Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes, 10, 613–617(2010) [Google Scholar]
- G.G. He, Y. Li, S.F. Ma, J. Systm, E.T&P, Discussion on short-term traffic flow forecasting methods based on mathematical models, 20(12), 51–56(2000) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.