改进PSO-LSTM算法预测高速公路交通量

Improved PSO-LSTM Algorithm for Forecasting Expressway Traffic Volume

高速公路交通政策的制定需要准确地预测交通量,基于此,选用长短期记忆网络(long short-term memory,LSTM)机器学习模型对其研究,针对LSTM模型中参数确定的问题,选用粒子群优化(particle swarm optimization,PSO)算法对其优化,同时针对PSO算法中粒子位置更新问题,以公式中各参数含义为切入点进行改进,将PSO算法公式中原来静态的惯性权重及学习权重改为会随着迭代次数及粒子位置改变而改变的动态值,从而达到搜寻精度提高的目的,据此构造改进PSO-LSTM模型,最后通过实例计算分析,分别对高速公路的工作日及休息日进行预测。结果表明改进的PSO-LSTM模型较LSTM模型在工作日及休息日交通量的预测上,其评价指标均方根误差分别提高了12.19%、10.97%,平均绝对误差分别提高了17.06%、15.17%,平方绝对百分比误差分别提高24.56%、23.88%,精度提高值明显高于PSO-LSTM模型。改进PSO-LSTM模型在交通量预测精度上具有显著提高作用,且抗干扰能力强,可以为政策的合理制定提供更可靠的依据。

The formulation of expressway traffic policy needs to accurately predict the traffic volume.Based on this,Long short-term memory(LSTM)machine learning model was selected to study it.Aiming at the problem of parameter determination in LSTM model,Particle swarm optimization(PSO)algorithm was selected to improve it.At the same time for the PSO algorithm in the particle position update problem,the meaning of each parameter in the formula for the entry point for improvement,the PSO algorithm formula for the original static inertia weights and learning weights into the iteration number and the particle position will change with the change of the dynamic value,so as to achieve the purpose of searching for the purpose of improving accuracy.Based on this,the improved PSO-LSTM model is constructed.Finally,through the calculation and analysis of an example,the working days and rest days of expressway are predicted respectively.The results show that the root mean square error of the evaluation index is increased by 12.19%and 10.97%,the average absolute error is increased by 17.06%and 15.17%,and the square absolute percentage error is increased by 24.56,respectively.The algorithm shows that the improved PSO-LSTM model plays a significant role in traffic volume forecasting,and has strong anti-interference ability.It can provide a more reliable basis for the rational formulation of policies.