Enhancing prediction of ride-hailing fares using advanced deep learning techniques
DOI: https://doi.org/10.3846/ntcs.2025.23932Abstract
Fare prediction is a critical component of online ride-hailing services, as it significantly influences consumer decision-making and enhances operational efficiency for service providers. Reliable fare prediction is especially important in dynamic pricing environments, where fares are affected by factors such as demand fluctuations, traffic conditions, and weather patterns. This study aims to enhance fare prediction in ride-hailing services by utilizing advanced deep learning models. Using a comprehensive dataset of Uber and Lyft fare data collected in Boston during the winter of 2018, we evaluated three deep learning architectures: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and BiLSTM with an attention mechanism (BiLSTM + Attention). The results showed that the BiLSTM + Attention model achieved the highest prediction accuracy, making it the most effective approach for fare prediction. However, its longer training time poses limitations for time-sensitive applications. Conversely, the LSTM model provided a strong balance between predictive accuracy and computational efficiency, making it a suitable alternative for scenarios that require faster model deployment. Additionally, our analysis identified key factors influencing fare variability – such as trip distance, time of day, and weather conditions – highlighting the importance of feature selection in enhancing model performance. By improving fare prediction accuracy, this study offers valuable insights for optimizing dynamic pricing strategies, enhancing consumer satisfaction, and helping ride-hailing platforms better manage supply–demand imbalances. These findings provide a foundation for future research exploring hybrid models and real-time data integration to further improve predictive capabilities in ride-hailing services.
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fare prediction, ride-hailing, LSTM, BiLSTM, Attention mechanismHow to Cite
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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