摘要
考虑交通流的时空因素进行短时交通流预测,能够提高预测的精度.为此,引入径向基核函数,将复杂的预测问题转化为高维空间的回归问题;然后,基于支持向量回归机并考虑时空因素影响作用建立在线的短时交通量预测模型,通过网格搜索的方法对模型参数进行优化;最后,构造时间—空间状态向量,通过不同的状态向量对时间和空间维度的影响进行了分析.利用高速公路检测器数据,对比不同模型的精度,对在线短时交通量预测模型的有效性和可行性进行了验证.结果表明:在线模型精度优于传统的支持向量回归模型,考虑时空因素影响后交通量预测模型具有更高的精度和稳定性.
Considering the impact of temporal-spatial features of traffic flow can improve the prediction accuracy. Therefore, this paper introduces a radial kernel function to convert the complex predictive problem into a regression algorithm in high-dimensional space. Then, based on support vector regression, an online short-term traffic flow prediction model considering temporal-spatial features is built. Grid search method is used to optimize the parameters. Finally, state vector is built to analyze the influence of temporal-spatial features. Based on the dataset of detectors in highway, different models are compared and the validity and feasibility of the prediction model are verified. The results indicate that online model is superior to traditional support vector. If considering the influence of temporal-spatial features the prediction model is more accuracy and steady.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2016年第5期165-171,共7页
Journal of Transportation Systems Engineering and Information Technology
基金
国家重点基础研究发展计划(973计划)(2012CB725405)
交通运输部科技示范工程(2015364X16030
2014364223150)~~
关键词
智能交通
短时交通量预测
支持向量回归
时空因素
状态向量
intelligent transportation
short-term traffic flow forecasting
support vector regression
temporal-spatial features
state vector
