计算机与数字工程2019，Vol.47Issue(5)：1164-1169,1188,7.DOI:10.3969/j.issn.1672-9722.2019.05.029
基于高速收费大数据的短时交通流量预测方法
Short-term Traffic Flow Forecasting Method Based on Big Charging Data
摘要
Abstract
The short-term traffic flow forecast of highway is of great significance to the operation and management of highway and the improvement of highway operation efficiency. The large data of expressway charges are massive,real-time,complex and dy?namic,while the traditional time series model has the advantages of complex modeling and poor adaptability. In order to improve the accuracy of traffic flow forecasting and use the big toll data,the improved ARIMA(Autoregressive Integrated Moving Average Mod?el)is proposed to optimize the model from the aspects of model identification and parameter adjustment. Experiments based on real data show that the improved time series model effectively overcomes the shortcomings of the traditional time series model and has good adaptability to different traffic conditions,and has higher prediction accuracy both on holiday and working day.关键词
交通流量/短时预测/时间序列/ARIMAKey words
traffic flow/short-term prediction/time series/ARIMA分类
信息技术与安全科学引用本文复制引用
刘艳丽,赵卓峰,丁维龙,徐扬..基于高速收费大数据的短时交通流量预测方法[J].计算机与数字工程,2019,47(5):1164-1169,1188,7.基金项目
北京市自然科学基金(编号:4162021) （编号:4162021）
国家自然科学基金(编号:61702014)资助. （编号:61702014）
