Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 24; p. 8443
• Main Authors: Casabianca, Pietro, Zhang, Yu, Martínez-García, Miguel, Wan, Jiafu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17-12-2021; MDPI
• Subjects: Algorithms; attention mechanism; Automobile drivers; Automobiles; bidirectional long short-term memory; Datasets; Deep learning; Emission standards; Forecasting; Global positioning systems; GPS; Natural Language Processing; Neural networks; Neural Networks, Computer; Teaching methods; Time series; Traffic congestion; Transportation networks; vehicle destination prediction; deep learning; vehicle destination prediction; attention mechanism; bidirectional long short-term memory
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21248443

Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle's position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, route diversions to pass traffic congestion, and optimizing fuel consumption for hybrid vehicles. Thus, reliably predicting destinations can bring benefits to the transportation industry. This paper investigates using deep learning methods for predicting a vehicle's destination based on its journey history. With this aim, Dense Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BiLSTM), and networks with and without attention mechanisms are tested. Especially, LSTM and BiLSTM models with attention mechanism are commonly used for natural language processing and text-classification-related applications. On the other hand, this paper demonstrates the viability of these techniques in the automotive and associated industrial domain, aimed at generating industrial impact. The results of using satellite navigation data show that the BiLSTM with an attention mechanism exhibits better prediction performance destination, achieving an average accuracy of 96% against the test set (4% higher than the average accuracy of the standard BiLSTM) and consistently outperforming the other models by maintaining robustness and stability during forecasting.