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混合Hawkes过程的人员移动同步和行程目的检测

Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes
课程网址: http://videolectures.net/kdd2017_liu_human_mobility/  
主讲教师: Guannan Liu
开课单位: 北航大学
开课时间: 2017-10-09
课程语种: 英语
中文简介:
虽然探索人类流动性可以使智能交通、城市规划和城市经济学等许多应用受益，但有两个关键问题需要回答:(i)在不同城市功能的区域之间，人类流动性的空间扩散的本质是什么?(ii)如何发现和追踪人类流动轨迹的出行目的?为了回答这些问题，我们研究了大规模和全市范围内的出租车轨迹;并进一步按照时间顺序将其组织为到达序列。我们从不同区域的到达序列中发现了一个重要的性质，即人口流动同步效应，这可以用来解释如果两个区域拥有相似的城市功能，那么它们在特定时间内具有相似的到达模式。此外，到达序列由具有不同旅行目的的到达事件混合而成，这可以通过出发地和目的地的区域环境来揭示。为此，在本文中，我们开发了一种将混合霍克斯过程(MHP)与层次主题模型集成在一起的联合模型，以捕获具有混合行程目的的到达序列。从本质上讲，人类移动同步效应在MHP中被编码为同步速率;通过引入潜在行程目的和POI主题对区域环境进行建模，生成区域的利益点(Point of interest, POI)。此外，我们提供了一种有效的参数学习推理算法。最后，我们在合成数据和真实数据上进行了密集的实验，实验结果证明了所提出模型的有效性。
课程简介: While exploring human mobility can benefit many applications such as smart transportation, city planning, and urban economics, there are two key questions that need to be answered: (i) What is the nature of the spatial diffusion of human mobility across regions with different urban functions? (ii) How to spot and trace the trip purposes of human mobility trajectories? To answer these questions, we study large-scale and city-wide taxi trajectories; and furtherly organize them as arrival sequences according to the chronological arrival time. We figure out an important property across different regions from the arrival sequences, namely human mobility synchronization effect, which can be exploited to explain the phenomenon that two regions have similar arrival patterns in particular time periods if they share similar urban functions. In addition, the arrival sequences are mixed by arrival events with distinct trip purposes, which can be revealed by the regional environment of both the origins and destinations. To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes. Essentially, the human mobility synchronization effect is encoded as a synchronization rate in the MHP; while the regional environment is modeled by introducing latent Trip Purpose and POI Topic to generate the Point of Interests (POIs) in the regions. Moreover, we provide an effective inference algorithm for parameter learning. Finally, we conduct intensive experiments on synthetic data and real-world data, and the experimental results have demonstrated the effectiveness of the proposed model.
关 键 词: 人类流动; 城市经济; 流动轨迹; 同步效应
课程来源: 视频讲座网
数据采集: 2023-03-22：chenxin01
最后编审: 2023-05-17：liyy
阅读次数: 30

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