Long-Term Traffic Time Prediction Using Deep Learning with Integration of Weather Effect
Traffic time prediction is a classical problem in intelligent transportation domain,which has attracted lots of attention from the research community in last three decades.The existing relevant works have been focused on how to predict the short-term traffic time for paths and roads.In fact,users may have the demand to know the future traffic time in advance as for making personal or commercial schedule.Longterm traffic time prediction is thus an emerging challenging task as there exist many complicated factors that may affect traffic situations,such as weather and congestion conditions.In this paper,we propose a novel deep learning-based framework named Deep Ensemble Stacked Long Short Term Memory(DE-SLSTM),which aims to solve the prediction bias during traffic congestion.To improve the model performance,we integrate the weather effect into the DE-SLSTM for predicting the long-term traffic time.Through a series of experiments,the proposed DE-SLSTM framework is verified to demonstrate excellent performance in terms of effectiveness.To the best of our knowledge,this is the first work on longterm traffic time prediction that considers deep learning techniques.
Long-term prediction Traffic time prediction Weather effect Ensemble model
Chih-Hsin Chou Yu Huang Chian-Yun Huang Vincent S.Tseng
Department of Computer Science,National Chiao Tung University,Hsinchu,Taiwan,Republic of China
国际会议
澳门
英文
123-135
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）