摘要
目前,很多短时交通流预测方法仅利用某一路段历史数据的时间相关性或者道路上下游路段的时空相关性进行交通流预测,未充分考虑路网所有路段之间的时空相关性.提出了一种基于稀疏混合遗传算法优化的最小二乘支持向量回归(LSSVR)模型,并应用于路网短时交通流预测.该预测模型不仅可以自动优化LSSVR模型参数,而且可以从高维路网交通流数据中选择有助于交通流预测的变量子集.实验结果表明,与LSSVR模型相比,所提方法具有更好的预测能力;而且,少量时空变量被选择出来构建预测模型,极大减少了信息冗余,改进了模型可解释性.
Currently, many short-term traffic flow forecasting methods just take into account either temporal correlation of historical data at the target road segment or spatiotemporal correlation between the upstream/downstream segments and the target one, thus ignoring complex spatiotemporal correlation from a more global viewpoint.Aiming at above problem, Least Squares Support Vector Regression（LSSVR） model optimized by a sparse hybrid Genetic Algorithm（GA） is put forward for short term traffic flow forecasting.This model not only automatically optimizes the involved parameters, but also selects from high-dimensional traffic data a subset of spatiotemporal variables contributing to traffic flow forecasting.The experimental results show that in comparison with LSSVR model, the proposed method can improve the performance of traffic flow forecasting.Moreover, only a few of spatiotemporal variables are selected by this method, not only reducing the information redundancy but also enhancing the interpretability of the resulting model.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2017年第1期60-66,81,共8页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(51108209
61203244
61403172
61573171)
交通运输部信息化项目(2013-364-836-900)
中国博士后科学基金(2015T80511
2014M561592)
江苏大学高级人才科研启动基金(14JDG066)
福建省信息处理与智能控制重点实验室(闽江学院)(MJUKF201724)~~
关键词
智能交通
变量选择
稀疏混合遗传算法
短时交通流预测
最小二乘支持向量回归
intelligent transportation
variable selection
sparse hybrid Genetic Algorithm
short-term traffic flow forecasting
Least Squares Support Vector Regression（LSSVR）
