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

Chaoyang Shi, Waner Zou, Yafei Wang (), Zhewen Zhu, Tengda Chen, Yunfei Zhang and Ni Wang
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Chaoyang Shi: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Waner Zou: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yafei Wang: School of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Zhewen Zhu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tengda Chen: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yunfei Zhang: Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, China
Ni Wang: Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou University, Chuzhou 239000, China

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: 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.

Keywords: OD-based travel time prediction; Back Propagation Neural Network; spatiotemporal features; weather and driver behavior modeling; online car-hailing trip data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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