Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.

翻译：给定一个起点（O）、一个终点（D）和一个出发时间（T），起终点（OD）行程时间预测器（ODT-Oracle）能够估计从O到D在T时刻出发所需的行程时间。ODT-Oracle在地图服务中具有重要作用。为了构建此类预测器，我们提出了一种基于历史轨迹的行程时间估计（TTE）解决方案，用于估计OD对的时变行程时间。该问题的复杂性在于：连接同一OD对的多条历史轨迹可能具有不同的行程时间，且轨迹之间可能存在差异。解决该问题的关键在于，在为未来查询进行行程时间估计时剔除异常轨迹。我们提出了一种新颖的两阶段框架，称为基于扩散的起终点行程时间估计（DOT）。首先，DOT采用条件化像素化轨迹（PiT）去噪器，通过学习OD对与历史轨迹之间的相关性，构建基于扩散的PiT推理过程。具体而言，给定一个OD对和出发时间，我们旨在推断出一个PiT。其次，DOT包含一个掩码视觉变换器（MViT），能够基于推断出的PiT高效且准确地估计行程时间。我们在两个真实世界数据集上进行了大量实验，结果表明DOT在准确性、可扩展性和可解释性方面均优于基线方法。