MATEC Web Conf.
Volume 259, 20192018 6th International Conference on Traffic and Logistic Engineering (ICTLE 2018)
|Number of page(s)||7|
|Section||Intelligent Transportation and Management|
|Published online||25 January 2019|
- T.-Q. Tang, L. Caccetta, Y.-H. Wu, H.-J Huan, and X.-B. Yang. A macro model for traffic flow on road networks with varying road conditions. Journal of Advanced transportation 48, pp. 304–317 (2014). [CrossRef] [Google Scholar]
- T. Q. Tang, J. G. Li, H. J. Huang, and X. B. Yang. A car-following model with real-time road conditions and numerical tests. Measurement 48 (2014). [Google Scholar]
- G. Costesequea and J. P. Lebacque. A variational formulation for higher order macroscopic traffic flow models: Numerical investigation. Transportation Research Part B: Methodological 70 (2014). [Google Scholar]
- S. C. Calvert, T. Henk, M. Snelder, and S. P. Hoogendoorn. Application of advanced sampling for efficient probabilistic traffic modelling. Transportation Research Part C: Emerging Technologies 49 (2014). [CrossRef] [Google Scholar]
- M.-L. Huang. Intersection traffic flow forecasting based on v-gsvr with a new hybrid evolutionaryalgorithm. Neurocomputing 147, pp. 343–349 (2015). [CrossRef] [Google Scholar]
- K. Price. An introduction to differential evolution. New Ideas in Optimization, pp. 79–108 (1999). [Google Scholar]
- D. Work, O. Tossavainen, S. Blandin, A. Bayen, T. Iwuchukwu, and K. Tracton. An ensemble kalman filtering approach to highway traffic estimation using gps enabled mobile devices. In Proceedings of the 47th IEEE Conference on Decision and Control (2008). [Google Scholar]
- J. P. Lebacque. The godunov scheme and what it means for first order traffic flow models. In Proceedings of 13th International Symposium on Transportation and Traffic Theory (1996). [Google Scholar]
- P. Dubec, J. Plucar, and L. Rapant. Use of the bio-inspired algorithms to find global minimum in force directed layout algorithms. In Proceedings of the 6th International Conference on Multimedia Communications, Services and Security, Communications in Computer and Information Science 368, pp. 194–203. Springer Verlag (2013). [Google Scholar]
- L. Rapant. Traffic flow modeling based on sparse data. In Proceedings of WOFEX 2014, pp. 526–531. VSB-TUO (2014). [Google Scholar]
- S. Vucetic V. Coric, N. Djuric. Traffic state estimation from aggregated measurements with signal reconstruction techniques. Transportation Research Record: Journal of the Transportation Research Board, 2315 (2012). [Google Scholar]
- B. Jiang and Y. Fei. Traffic and vehicle speed prediction with neural network and hidden markov model in vehicular networks. In Proceedings of 2015 IEEE Intelligent Vehicles Symposium (2015). [Google Scholar]
- E. Castillo. Stochastic demand dynamic traffic models using generalized beta-gaussian bayesian networks. IEEE Transactions on Intelligent Transportation Systems 13 (2012). [Google Scholar]
- Y. Xie, Y. Zhang, and Z. Ye. Short-term traffic volume forecasting using kalman filter with discrete wavelet decomposition. Computer-Aided Civil and Infrastructure Engineering 22, pp. 326–334 (2007). [CrossRef] [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.