摘要
有效的交通流量预测对人们出行和交管部门监管都有着重要的意义。传统的交通量预测模型主要基于交通流的时间特性,未结合交通流的时间和空间特性进行深入挖掘,因此预测效果有时不佳。提出了一种基于CNN与LightGBM结合的交通流预测模型,首先利用CNN模型挖掘出高速公路相邻路段监测点和出入口的时间和空间关联性,实现对交通流数据的时空特征提取,然后将CNN提取到的特征向量输入到LightGBM模型中进行预测。为了验证模型的有效性,实验中使用了多种预测模型进行对比,实验结果表明,所提出的考虑到时空特性的CNN-LightGBM组合的模型可以明显降低预测误差,是一种有效快速的交通流预测模型。
Effective traffic flow forecasting is of great significance to people′s travel and traffic management supervision.Traditional traffic volume prediction models are mainly based on the time characteristics of traffic flow,however,these models don′t combine the time and space characteristics of traffic flow for in-depth mining,so sometimes these models don′t perform well.This paper proposes a traffic flow prediction model based on the combination of CNN and LightGBM. The CNN model is used to excavate the temporal and spatial correlation between the monitoring points and the entrances and exits of the adjacent sections of the highway to realize the spatiotemporal feature extraction of the traffic flow data,and then the feature vector extracted by CNN is input into the LightGBM model for prediction.In order to verify the effectiveness of the model,a variety of prediction models are used in the experiment for comparison.The experimental results show that the proposed model of CNN-LightGBM considering the spatio-temporal characteristics can significantly reduce the prediction error and is an effective and fast traffic flow forecasting model.
作者
张振
曾献辉
Zhang Zhen;Zeng Xianhui(School of Information Science and Technology,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitalized Textile&Fashion Technology,Ministry of Education,Shanghai 201620,China)
出处
《信息技术与网络安全》
2020年第2期34-39,共6页
Information Technology and Network Security
