Sequence to sequence hybrid Bi-LSTM model for traffic speed prediction.

Congestion is a bane of urban life that affects a large share of the population on a daily basis. Thus, congestion gets tremendous attention from city stakeholders, residents, and researchers. The key challenge to preventing congestion is to accurately predict the traffic status (e.g., speed) of a particular road segment which is greatly affected by many factors, such as spatial, temporal, and road conditions. Although several research studies have focused on preventing congestion, most prediction-based literature came short of accurate predictions regarding precision and time efficiency regarding large-scale datasets. This paper proposes a new hybrid approach called Grizzly. This approach utilizes an improved Sequence to Sequence Bi-directional Long Short Term Memory Neural Network model that integrates data pre-processing techniques such as normalization and embeddings to improve traffic prediction accuracy. Carried out experiments on two large-scale real-world datasets, namely PEMS-BAY and METR-LA, pinpointing that the proposed approach outperformed the pioneering competitors from time-series-based and hybrid neural network-based baselines in terms of the agreed-on evaluation criteria (precision and computation time). • Bidirectional LSTM-based approach has been developed for multistep speed prediction. • Seq-to-Seq architecture is relevant to capturing long-term temporal dependencies. • The combination of Embedding and Normalization improves prediction quality and cost. • Grizzly outperforms both classical baselines and hybrid Neural Network-based models. • Grizzly was evaluated on two large real-world datasets with three prediction tasks. [ABSTRACT FROM AUTHOR]