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New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system
Author(s) -
Zhang Degan,
Wang Jiaxu,
Fan Hongrui,
Zhang Ting,
Gao Jinxin,
Yang Peng
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4647
Subject(s) - computer science , particle swarm optimization , simulated annealing , traffic flow (computer networking) , radial basis function , intelligent transportation system , artificial neural network , basis (linear algebra) , genetic algorithm , mathematical optimization , algorithm , artificial intelligence , machine learning , mathematics , engineering , civil engineering , geometry , computer security
Summary Traffic flow forecasting is one of the essential means to realize smart cities and smart transportation. The accurate and effective prediction will provide an important basis for decision‐making in smart transportation systems. This paper proposes a new method of traffic flow forecasting based on quantum particle swarm optimization (QPSO) strategy for intelligent transportation system (ITS). We establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing algorithm is applied to the quantum particle swarm algorithm to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network is used to obtain the required data. In addition, in order to compare the performance of the algorithms, a comparison study with other related algorithms such as QPSO radial basis function (QPSO‐RBF) is also performed. Simulation results show that compared with other algorithms, the proposed algorithm can reduce prediction errors and get better and more stable prediction results.