STAD：不经意走时估计的时空调整
STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation
摘要：
行程时间估计是现代交通应用中的重要组成部分。用于行程时间估计的现有技术使用GPS轨迹来学习道路网络的权重，通常建模为有向图，然后应用Dijkstra算法来找到最短路径。为了实现时间依赖性，现有系统计算多个加权图，对应于不同的时间窗口。这些图通常在部署到生产路由引擎之前进行离线优化，导致严重的工程开销。在本文中，我们介绍了STAD，这是一个系统，可以动态调整以出发地、目的地和出发时间形式表示的任何旅行请求的旅行时间估计。STAD使用机器学习和稀疏行程数据来学习任何基本路由引擎的缺陷，然后将其转变为一个成熟的时间依赖系统，能够根据城市的实际交通状况调整行程时间。STAD通过将空间特征（例如出发地和目的地地理区域）与时间特征（例如出发时间和日期）相结合来利用交通的时空属性，以显著改善基本路由引擎的旅行时间估计。在多哈、纽约和波尔图的真实旅行数据集上进行的实验表明，前两个城市的中位绝对误差减少了14%，后两个城市减少了29%。我们还表明，STAD在所有三个城市中的表现优于不同的商业和研究基线。
英文摘要：
Travel time estimation is an important component in modern transportation applications. The state of the art techniques for travel time estimation use GPS traces to learn the weights of a road network, often modeled as a directed graph, then apply Dijkstra-like algorithms to find shortest paths. Travel time is then computed as the sum of edge weights on the returned path. In order to enable time-dependency, existing systems compute multiple weighted graphs corresponding to different time windows. These graphs are often optimized offline before they are deployed into production routing engines, causing a serious engineering overhead. In this paper, we present STAD, a system that adjusts - on the fly - travel time estimates for any trip request expressed in the form of origin, destination, and departure time. STAD uses machine learning and sparse trips data to learn the imperfections of any basic routing engine, before it turns it into a full-fledged time-dependent system capable of adjusting travel times to real traffic conditions in a city. STAD leverages the spatio-temporal properties of traffic by combining spatial features such as departing and destination geographic zones with temporal features such as departing time and day to significantly improve the travel time estimates of the basic routing engine. Experiments on real trip datasets from Doha, New York City, and Porto show a reduction in median absolute errors of 14% in the first two cities and 29% in the latter. We also show that STAD performs better than different commercial and research baselines in all three cities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
