Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty requires generating the most likely path and assessing travel time uncertainty along the path. This involves two main challenges: 1) Predicting a path that aligns with the ground truth, and 2) modeling the impact of travel time in each segment on overall uncertainty under varying conditions. We propose DutyTTE to address these challenges. For the first challenge, we introduce a deep reinforcement learning method to improve alignment between the predicted path and the ground truth, providing more accurate travel time information from road segments to improve TTE. For the second challenge, we propose a mixture of experts guided uncertainty quantification mechanism to better capture travel time uncertainty for each segment under varying contexts. Additionally, we calibrate our results using Hoeffding's upper-confidence bound to provide statistical guarantees for the estimated confidence intervals. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed method.

本研究针对旅行时间估计中的不确定性量化问题，提出了DutyTTE方法来改善预测路径与真实情况的对齐，增进旅行时间信息的准确性。同时，采用专家混合指导的不确定性量化机制，捕捉不同条件下每个区间的旅行时间不确定性。实验证明该方法在实际数据集上具有优越性。

DutyTTE：解读出发地-目的地旅行时间估计中的不确定性