Towards An Efficient and Effective En Route Travel Time Estimation Framework

2025-04-05

AI Summary

To address the degraded real-time performance, throughput bottlenecks, and service latency caused by frequent re-predictions in en-route travel time estimation (ER-TTE), this paper proposes a synergistic framework integrating Uncertainty-Guided Decision-making (UGD) and Meta-Learning-driven Fine-Tuning (FTML). UGD dynamically determines whether re-prediction is necessary based on real-time uncertainty quantification, while FTML enables task-adaptive, low-overhead model updates. Together, they achieve on-demand re-prediction and well-calibrated confidence interval estimation in a unified manner. Evaluated on two large-scale real-world datasets, our method significantly improves inference speed (up to 3.2×) and system throughput (up to 2.8×), while preserving prediction accuracy and confidence interval calibration quality. The source code is publicly available.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

En route travel time estimation (ER-TTE) focuses on predicting the travel time of the remaining route. Existing ER-TTE methods always make re-estimation which significantly hinders real-time performance, especially when faced with the computational demands of simultaneous user requests. This results in delays and reduced responsiveness in ER-TTE services. We propose a general efficient framework U-ERTTE combining an Uncertainty-Guided Decision mechanism (UGD) and Fine-Tuning with Meta-Learning (FTML) to address these challenges. UGD quantifies the uncertainty and provides confidence intervals for the entire route. It selectively re-estimates only when the actual travel time deviates from the predicted confidence intervals, thereby optimizing the efficiency of ER-TTE. To ensure the accuracy of confidence intervals and accurate predictions that need to re-estimate, FTML is employed to train the model, enabling it to learn general driving patterns and specific features to adapt to specific tasks. Extensive experiments on two large-scale real datasets demonstrate that the U-ERTTE framework significantly enhances inference speed and throughput while maintaining high effectiveness. Our code is available at https://github.com/shenzekai/U-ERTTE

Problem

• Improving real-time en route travel time prediction efficiency
• Reducing computational delays in simultaneous user requests
• Enhancing accuracy with uncertainty-guided selective re-estimation

Innovation

• Uncertainty-Guided Decision mechanism for selective re-estimation
• Fine-Tuning with Meta-Learning for accurate predictions
• Combines UGD and FTML to enhance speed and accuracy