Enhancing Travel Time Prediction for Intelligent Transportation Systems: A High-Resolution Origin–Destination-Based Approach with Multi-Dimensional Features

Accurate travel time prediction is essential for improving urban mobility, traffic management, and ride-hailing services. Traditional link- and path-based models face limitations due to data sparsity, segmentation errors, and computational inefficiencies. This study introduces an origin–destination (OD)-based travel time prediction framework leveraging high-resolution ride-hailing trajectory data. Unlike previous works, our approach systematically integrates spatiotemporal, quantified weather metrics and driver behavior clustering to enhance predictive accuracy. The proposed model employs a Back Propagation Neural Network (BPNN), which dynamically adjusts hyperparameters to improve generalization and mitigate overfitting. Empirical validation using ride-hailing data from Xi’an, China, demonstrates superior predictive performance, particularly for medium-range trips, achieving an RMSE of 202.89 s and a MAPE of 16.52%. Comprehensive ablation studies highlight the incremental benefits of incorporating spatiotemporal, weather, and behavioral features, showcasing their contributions to reducing prediction errors. While the model excels in moderate-speed scenarios, it exhibits limitations in short trips and low-speed cases due to data imbalance. Future research will enhance model robustness through data augmentation, real-time traffic integration, and scenario-specific adaptations. This study provides a scalable and adaptable travel time prediction framework, offering valuable insights for urban traffic management, dynamic route optimization, and sustainable mobility solutions within ITS.