通过智能判断何时重估，提升行程剩余时间预测效率。
Towards An Efficient and Effective En Route Travel Time Estimation Framework
- 基于不确定性引导决策，只在必要时重估，减少计算开销。
- 在两个真实数据集上实现推理速度提升3倍以上，吞吐量显著增加。
- 适合高并发交通预测场景，对实时性要求高的系统尤其适用。
行程中旅行时间估计（ER-TTE）旨在预测剩余路程的通行时间。现有方法频繁进行重估，严重制约实时性能，尤其在同时处理大量用户请求时，导致延迟和响应变慢。本文提出通用高效框架U-ERTTE，结合不确定性引导决策机制（UGD）与元学习微调（FTML）。UGD量化不确定性，为全程提供置信区间，仅当实际时间超出预测区间时才触发重估，从而优化效率。FTML用于训练模型，使其学习通用驾驶模式并适应特定任务，确保置信区间准确、重估预测可靠。在两个大规模真实数据集上的大量实验表明，U-ERTTE显著提升推理速度与吞吐量，同时保持高精度。代码已开源：https://github.com/shenzekai/U-ERTTE。
原文摘要 · Abstract (English)
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
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