基于改进在线极限学习机的短时交通流预测模型研究

A short-term traffic flow prediction model based on improved online extreme learning machine

交通流信息预测是智能交通系统进行交通疏导管理的重要基础,为城市交通管理规划提供可靠的数据支持和科学的决策依据。由于交通流量数据是实时更新的增量流数据,每次更新历史数据集时都需要重新构建预测模型,消耗了大量计算资源和运行时间,为此提出一种基于改进在线顺序极限学习机的交通流预测模型(IOS-ELM),通过构建新增数据的增强特征映射关系,生成交通流动态更新特征表示空间,实现短时交通流预测模型的动态更新。利用长沙市远大一路交通流数据评估该模型,实验结果表明,IOS-ELM模型在NRMSE和MAPE的预测性能上均超过其他基准预测模型(MLP、ELM、OS-ELM和SVR),同时模型的预测耗时较小,可以保证一定实时性,满足城市道路交通流的实时准确预测的需求。

Traffic flow information prediction is an important foundation for traffic guidance management of intelligent transportation systems,and provides reliable data support and scientific decision-making basis for urban traffic management planning.Since the traffic flow data is real-time updated incremental flow data,each time the historical data set is updated,the prediction model needs to be rebuilt,which consumes a lot of computing resources and running time.Therefore,this paper proposes an improved online sequential extreme learning machine for traffic flow prediction(IOS-ELM),which constructs the enhanced feature mapping relationship of the newly input data,generates the dynamic update feature representation space of the traffic flow,and realizes the dynamic update of the short-term traffic flow prediction model.Finally,the model is evaluated on the real-world traffic flow data of Yuanda 1 st Road in Changsha,China.The experimental results show that the IOS-ELM model exceeds other baselines prediction models(MLP,ANN,ELM,OS-ELM) in the prediction performance of NRMSE and MAPE.Meanwhile,the computation prediction of IOS-ELM is less time-consuming,which can ensure a certain real-time performance and meet the needs of real-time and accurate prediction of urban road traffic flow.