基于量子粒子群优化策略的车联网交通流量预测方法

doi:10.11896/jsjkx.191200126

Abstract

Abstract: This paper proposes a traffic flow prediction algorithm for connected vehicles based on quantum particle swarm optimization strategy.Establishing a corresponding model based on the characteristics of the traffic flow data,apply the genetic simulated annealing algorithm to the quantum particle swarm algorithm to obtain the optimized initial cluster center,and apply the optimized algorithm to the parameter optimization of the radial basis neural network prediction model.The high-dimensional mapping to the basic neural network yields the desired predicted data results.In addition,in order to compare the performance of the algorithms,a comparison study with other related algorithms such as 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.

Key words: Genetic simulated annealing, Neural network, Quantum particle swarm, Traffic flow prediction

CLC Number:

TP301

ZHANG De-gan, YANG Peng, ZHANG Jie, GAO Jin-xin, ZHANG Ting. New Method of Traffic Flow Forecasting of Connected Vehicles Based on Quantum Particle Swarm Optimization Strategy[J].Computer Science, 2020, 47(11A): 327-333.

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New Method of Traffic Flow Forecasting of Connected Vehicles Based on Quantum Particle Swarm Optimization Strategy

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