Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches
Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches
复制标题
DOI：
10.15388/20-infor431
复制
发表时间：
2020
期刊：
影响因子：
2.9
通讯作者：
Bruno Fernandes;Fábio Silva;H. Alaiz-Moretón;P. Novais;J. Neves;Cesar Analide
中科院分区：
文献类型：
--
作者：
Bruno Fernandes;Fábio Silva;H. Alaiz-Moretón;P. Novais;J. Neves;Cesar Analide
Traffic flow forecasting is an acknowledged time series problem whose solutions have been essentially grounded on statistical-based models. Recent times came, however, with promising results regarding the use of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory networks (LSTMs), to accurately address time series problems. Literature is, however, evasive in regard to several aspects of the conceived models and often exhibits misconceptions that may lead to important pitfalls. This study aims to conceive and find the best possible LSTM model for traffic flow forecasting while addressing several important aspects of such models such as the multitude of input features, the time frames used by the model and the employed approach for multi-step forecasting. To overcome the spatial problem of open source datasets, this study presents and describes a new dataset collected by the authors of this work. After several weeks of model fitting, Recursive MultiStep Multi-Variate models were the ones showing better performance, strengthening the perception that LSTMs can be used to accurately forecast the traffic flow for several future timesteps.
