赵志远, 尹凌, 方志祥, 萧世伦, 杨喜平. 轨迹数据的时间采样间隔对停留识别和出行网络构建的影响[J]. 武汉大学学报 ( 信息科学版), 2018, 43(8): 1152-1158. DOI: 10.13203/j.whugis20160303
引用本文: 赵志远, 尹凌, 方志祥, 萧世伦, 杨喜平. 轨迹数据的时间采样间隔对停留识别和出行网络构建的影响[J]. 武汉大学学报 ( 信息科学版), 2018, 43(8): 1152-1158. DOI: 10.13203/j.whugis20160303
ZHAO Zhiyuan, YIN Ling, FANG Zhixiang, SHAW Shihlung, YANG Xiping. Impacts of Temporal Sampling Intervals on Stay Detection and Movement Network Construction in Trajectory Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1152-1158. DOI: 10.13203/j.whugis20160303
Citation: ZHAO Zhiyuan, YIN Ling, FANG Zhixiang, SHAW Shihlung, YANG Xiping. Impacts of Temporal Sampling Intervals on Stay Detection and Movement Network Construction in Trajectory Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1152-1158. DOI: 10.13203/j.whugis20160303

轨迹数据的时间采样间隔对停留识别和出行网络构建的影响

Impacts of Temporal Sampling Intervals on Stay Detection and Movement Network Construction in Trajectory Data

  • 摘要: 个体轨迹数据已经广泛用于人群活动的研究中。在静止的局部空间开展的活动是个体日常生活的基本元素,在轨迹数据中对应停留部分。因此学者常从轨迹数据中识别停留来研究个体活动信息。然而,轨迹数据的时间采样间隔会对停留识别带来影响。针对该问题,首先提出了一个框架,量化不同持续时间长度的活动在不同时间采样间隔的轨迹数据中被识别为停留的概率。其次,考虑到个体出行网络依赖于停留识别结果,基于该框架,研究分析了时间采样间隔对出行网络分析结果的影响。最后,利用该框架分别对深圳市居民出行调查数据和手机轨迹数据进行了分析。研究表明,在面向人群活动的研究和应用中,该框架能支持时间采样间隔的选择决策和面向活动类型的研究结果评价。

     

    Abstract: Trajectory data have been extensively used in human mobility studies. Activities, especially conducted on a static and local space are basic elements of people's daily life, and they are represented as stays in trajectories. Hence detecting stays from trajectories has become a base for many activity-oriented studies. The temporal sampling interval (TSI) of trajectory data can impact the result of stay detection. However such impacts have not been systematically studied yet. This study proposes a probability-based framework, which aims to quantify the probability of an activity that with a specific duration time can be detected as a stay with different TSIs. Moreover, this framework can support further analysis on the evolution of daily movement network with different TSIs. We demonstrate the impacts of TSIs on stay detection and movement networks construction by using a trip survey dataset and a mobile phone location dataset of Shenzhen, China respectively. This study provides both metho-dological and empirical guidance on the decision-making of a TSI selection as well as the estimation of the results of activity-oriented studies.

     

/

返回文章
返回