基于QPSO__RBF神经网络的网约车需求量预测模型

Demand forecasting for online car-hailing services based on QPSO__RBF neural network

为了准确地预测乘客对网约车的需求量,指向性地提高部分地区的运力,让乘客更加容易预约到网约车,从而提升乘客的出行体验,通过对网约车需求量的变化规律和影响因素进行灰色关联度分析,选取网约车历史需求量、天气类型和道路拥堵比例作为影响因子,利用量子行为粒子群(QPSO)算法优化径向基(RBF)神经网络的网络权值、中心和基宽来构建QPSO__RBF神经网络预测模型。实际运营数据结果表明,QPSO__RBF神经网络预测模型具有可行性和有效性,其预测精度优于普通RBF神经网络模型的,且无论是改进的RBF神经网络还是普通RBF神经网络,综合考虑历史需求量、天气类型和道路拥堵比例作为影响因子的预测模型均优于只考虑历史需求量的预测模型。

Accurate demand forecasting for online car-hailing services can help companies improve traffic capacity in some areas,so that passengers can call a car easily,which improves their travel experience.Historical demand of online car-hailing services,types of weather condition and proportion of congested roads are chosen as impact factors for gray relational degree analysis,and a QPSO-RBF neural network model which uses the quantum-behaved particle swarm optimization algorithm to optimize the initial parameters of radial basis function neural network,including network weights,basis function centers and basis function widths,is established.It is found that the proposed QPSO-RBF neural network model outperformed the RBF neural network model.More importantly,the model considering historical demand of online car-hailing,types of weather condition and congested roads proportion outperforms the model only considering historical demands of online car-hailing.Thus,feasibility and effectiveness of the proposed model is verified.