摘要
目前的交通流预测研究中,输入步长主要取决于人为的选择,容易受到干扰,并且缺少从理论方面选择输入步长的方法。为了能够自适应地选取输入步长,基于交通流历史时间序列的自相关分析,以机器学习中典型的最小二乘支持向量机(LSSVM)、随机森林(RF)以及长短记忆(LSTM)3种算法进行多输入步长的交通流预测,探究以自相关系数值选取最佳输入步长的方法可行性。实验结果表明,在输入步长的自相关系数为0.80~0.91时,LSSVM能获得较优的预测精度,当自相关系数为0.47~0.51时,LSTM能有较好的预测精度,而RF交通流预测的最低误差对应的输入步长自相关程度较低,自相关分析方法 可能并不适用。
In the current research of traffic flow prediction, the time step mainly depends on the artificial selection which is easy to be disturbed, and there is a lack of methods to select the time step from the theoretical aspect. In order to adaptively select the time step, three typical algorithms of least squares support vector machine(LSSVM), random forest(RF) and long short-term memory(LSTM) in machine learning are used to predict traffic flow with multiple time step based on the autocorrelation analysis of historical time series for traffic flow and explore the feasibility of selecting the best time step by the value of autocorrelation coefficient. The experimental results show that when the autocorrelation coefficient of the time step is 0.83 ~0.91, LSSVM can obtain better prediction accuracy while the autocorrelation coefficient is 0.47~0.51, LSTM can have better prediction accuracy.However, due to the low autocorrelation degree of the time step corresponding to the lowest error of traffic flow prediction, the autocorrelation analysis method may not be applicable to RF.
作者
王爽
黄海超
石宝存
陈景雅
Wang Shuang;Huang Haichao;Shi Baocun;Chen Jingya(College of Civil and Transportation Engineering,Hohai University,Nanjing 210024,China)
出处
《华东交通大学学报》
2022年第5期78-85,共8页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(52078190)
教育部人文社会科学研究规划基金项目(18YJAZH119)。
关键词
交通流预测
自相关分析
机器学习
输入步长
traffic flow prediction
autocorrelation analysis
machine learning
time step
