A recurrent neural network for urban long-term traffic flow forecasting

作者：Asma Belhadi, Youcef Djenouri, Djamel Djenouri, Jerry Chun‐Wei Lin · 发表于：Applied Intelligence · 年份：2020 · DOI：10.1007/s10489-020-01716-1 · 被引用次数：97 · 研究领域：Traffic Prediction and Management Techniques、Transportation Planning and Optimization、Air Quality Monitoring and Forecasting

Abstract This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data.