基于梯度提升回归树的短时交通流预测模型

短时交通流预测是交通流建模的一个重要组成部分,在城市道路交通的 管理和控制中起着重要的作用。然而,常见的时间序列模型(如ARIMA)、随机森林(RF)模型在交通流预测方面由于被构建模型产生的残差和输入变量所影响,其预测精度受到限制。针对该问题,提出了一种基于梯度提升回归树的短时交通预测模型来预测交通速度。首先,模型引入Huber损失函数作为模型残差的处理方法;其次, 在输入变量中考虑预测断面受到毗邻空间因素和时间因素相关性的影响。模型在训练过程中通过不断调整弱学习器的权重来纠正模型的残差,从而提高模型预测的精度。利用某城市快速路的交通速度数据进行实验,并使用MSE和MAPE等指标将本文模型与ARIMA模型和随机森林模型进行对比,结果表明,文中所提模型的预测精度最好,从而验证了模型在短时交通流预测方面的有效性。

Short-term Traffic Flow Prediction Model Based on Gradient Boosting Regression Tree

Short-term traffic flow prediction is an important part of traffic flow modeling,and it also plays an important role in urban road traffic management and control.However,the common time series model (e.g.,ARIMA) and random forest model (RF) are limited in the prediction accuracy due to the residuals generated by the model and the input variables.Aiming at this problem,a short-term traffic flow prediction model based on gradient boosting regression tree(GBRT) was proposed to predict the travel speed.The model (GBRT) first introduces the Huber loss function to deal with residuals.Secondly,the spatial-temporal correlations are also considered in the input variables.The model adjusts the weight of the weak learners in the training process,and corrects the residuals of the model to improve the prediction accuracy. Experiment was conducted by using traffic speed data of a city expressway,and ARIMA model and random forest modle were compared with the proposed model by using MSE,MAPE and other indicators.Results show that the proposed model has the best prediction accuracy,and the validity of the model in short-term traffic flow prediction is verified.