利用GPS轨迹推断滑行状态
Inferring Taxi Status Using GPS Trajectories
摘要：
在本文中，我们推断出租车的状态，包括占用，非占用和停放，根据其GPS轨迹。状态信息可以使城市计算能够改善城市的交通系统和土地使用规划。在我们的解决方案中，我们首先识别和提取一组有效的特征，这些特征结合了单个轨迹、历史轨迹和道路网络等地理数据的知识。第二，设计停车状态检测算法以（从给定轨迹）找到停车位置，将轨迹划分成段（即，子轨迹）。第三，我们提出了一个两阶段的推理模型来学习出租车段中每个点的状态（占用或未占用）。该模型首先使用识别的特征来训练局部概率分类器，然后进行隐半马尔可夫模型（HSMM）全局考虑长期的旅行模式。我们用600辆出租车生成的大规模真实轨迹数据集评估了我们的方法，显示了我们的方法优于基线的优势。
英文摘要：
In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city's transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis, showing the advantages of our method over baselines.
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。
