Open Access
Issue |
MATEC Web Conf.
Volume 81, 2016
2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016)
|
|
---|---|---|
Article Number | 03006 | |
Number of page(s) | 7 | |
Section | Traffic Control | |
DOI | https://doi.org/10.1051/matecconf/20168103006 | |
Published online | 25 October 2016 |
- M. Birdsall, Google and ITE: The road ahead for self-driving cars, ITE Journal 84, 5, pp. 36–39 (2014) [Google Scholar]
- N. Navet, Y. Song, F. Simonot-Lion, C. Wilwert, Trends in automotive communication systems, Proc. of the IEEE 93, no. 6, pp. 1204–1223 (2005) [CrossRef] [Google Scholar]
- M. Selinger, L. Schmidt, Adaptive traffic control systems in the U.S.: Updated summary and comparison, HDR Engineering, Tech. Rep. (2010) [Google Scholar]
- H. Prothmann, Organic Traffic Control. KIT Scientific Publishing (2011) [Google Scholar]
- A. Stevanovic, National Research Council (U.S.), Adaptive traffic control systems: domestic and foreign state of practice, Synthesis of highway practice (2010) [CrossRef] [Google Scholar]
- N. H. Gartner, OPAC Strategy for demand-responsive decentralized traffic signal control, in Control, Comp., Communic. in Transp. (1989) [Google Scholar]
- A. G. Sims, K. W. Dobinson, The Sydney coordinated adaptive traffic (SCAT) system – Philosophy and benefits, IEEE Trans. Veh. Techn. 29, 130–137 (1980) [CrossRef] [Google Scholar]
- D. I. Robertson, R. D. Bretherton, Optimizing networks of traffic signals in real time – the SCOOT method, IEEE Trans. Veh. Technol. 40, 11–15 (1991) [CrossRef] [Google Scholar]
- R. Chrobok, O. Kaumann, J. Wahle, M. Schreckenberg, Different methods of traffic forecast based on real data, European Journal of Operational Research 155, 3, pp. 558–568 (2004) [Google Scholar]
- S. Garside, K. Lindveld, and J. Whittaker, Tracking and predicting a network traffic process. Intern. Journal of Forecasting 13, pp. 51–61 (1997) [CrossRef] [Google Scholar]
- M. S. Dougherty, M. R. Cobbett, Short-term inter-urban traffic forecasts using neural networks, Intern. Journal of Forecasting 13, 1, pp. 21–31 (1997) [CrossRef] [Google Scholar]
- R. Adhikari, R. K. Agrawal, Performance evaluation of weights selection schemes for linear combination of multiple forecasts, Artif. Intell. Rev., 529–548 (2014) [CrossRef] [Google Scholar]
- M. Sommer, S. Tomforde, J. Hähner, D. Auer, Learning a Dynamic Re-combination Strategy of Forecast Techniques at Runtime, Proc. of IEEE Int. Conf. Autonomic Computing, pp. 261–266 (2015) [Google Scholar]
- M. C. Bell, R. D. Bretherton, Ageing of fixed-time traffic signal plans, 2nd Int. Conf. On Road Traffic Control, pp. 77–80 (1986) [Google Scholar]
- J. Barcelo, J. Casas, Dynamic network simulation with AIMSUN, Int. Symp. on Transp. Simulation. Kluwer, pp. 1–25 (2002) [Google Scholar]
- R. J. Hyndman, Y. Khandakar, Automatic time series forecasting: the forecast package for R, J. Statistical Software 26, 3, pp. 1–22 (2008) [Google Scholar]
- L. I. Panis, S. Broekx, R. Liu, Modelling instantaneous traffic emission and the influence of traffic speed limits, Science of The Total Environment 371, pp. 270–285 (2006) [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.