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|Title:||Genetic and simulated annealing algorithms-based traffic state identification||Authors:||Yuan, Y
|Issue Date:||2018||Publisher:||Institute of Physics Publishing||Source:||IOP conference series : earth and environmental science, 2018, v. 170, no. 2, 32117, p. 1-12 How to cite?||Journal:||IOP conference series : earth and environmental science||Abstract:||Accurate and scientific traffic state identification is the basis of traffic navigation system, traffic control system and traffic organization optimization. In this paper, the dynamic traffic data collected by geomagnetic detector are firstly used to identify traffic state. We proposed SAGA-FCM clustering algorithm which is combined simulated annealing algorithm (SA) with genetic algorithm (GA) for urban expressway traffic state identification. This method can overcome the problems those the dynamic data of other detectors are not accurate and the time interval is not uniform. Meanwhile, it can overcome the instability of FCM algorithm clustering center and it is easy to fall into the local extreme value and "premature" problem of genetic algorithm. Research results show that, Compared with FCM algorithm and GA-FCM algorithm, SAGA-FCM clustering algorithm can be more effective and fast convergence, so as to improve the accuracy of urban traffic state identification.||Description:||2018 2nd International Symposium on Resource Exploration and Environmental Science, REES 2018, 28-29 April 2018||URI:||http://hdl.handle.net/10397/80018||ISSN:||1755-1307||DOI:||10.1088/1755-1315/170/3/032117||Rights:||Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (https://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Published under licence by IOP Publishing Ltd
The following publication Yuan, Y., Shao, C., Zhang, S., & Mei, L. (2018). Genetic and simulated annealing algorithms-based traffic state identification. IOP conference series : earth and environmental science, 2018, 170 (2), 32117, 1-12 is available at https://dx.doi.org/10.1088/1755-1315/170/3/032117
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