Abstract
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 Multi-Step 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.
Highlights
Recent years have been undoubtedly beneficial to the machine learning community
The conceived models were evaluated in regard to the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) error metrics
Mean RMSE and MAE values were computed for each prediction of each split of the time series cross-validator
Summary
In particular, has assumed a prominent position in many distinct fields such as computer vision (Karpathy et al, 2014; Zhang et al, 2016), speech recognition (Graves et al, 2013; Trianto et al, 2018), or natural language processing (Zhang et al, 2017; Huang et al, 2018), just to name a few This is the result of the increased robustness and ability to generalize that deep learning models have achieved as well as the appearance of new application-specific integrated circuits, such as Tensor Processing Units (TPUs) or Graphics Processing Units (GPUs), with superior capacities. Long Short-Term Memory networks (LSTMs), a specific type of RNNs, are among those that have been showed to produce valid results on time series data (Zhao et al, 2017; Ma et al, 2015)
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