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Mode Inference using enhanced Segmentation and Pre-processing on raw Global Positioning System data
Measurement and Control ( IF 1.3 ) Pub Date : 2020-05-27 , DOI: 10.1177/0020294020918324
Asif Nawaz 1 , Huang Zhiqiu 1, 2, 3 , Wang Senzhang 1 , Yasir Hussain 1 , Amara Naseer 1 , Muhammad Izhar 1 , Zaheer Khan 1
Affiliation  

Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.

中文翻译:

使用增强的分割和预处理原始全球定位系统数据的模式推断

许多应用程序使用全球定位系统数据,为多种目的提供丰富的上下文信息。更容易获得和访问全球定位系统数据可以促进各种移动应用程序,此类应用程序之一是推断用户的移动性。大多数现有的用于推断用户交通方式的工作需要结合全球定位系统数据和其他类型的数据,例如加速度计和全球移动通信系统。然而,如果对等数据源不可用,应用程序依赖于使用除全球定位系统之外的数据源使得应用程序的使用变得困难。在本文中,我们介绍了一种仅使用全球定位系统数据来推断交通方式的新通用框架。我们的贡献是三方面的。首先,我们提出了一种使用网格概率分布函数进行全球定位系统轨迹数据预处理的新方法。其次,我们引入了一种基于变化点的轨迹分割算法,以更有效地从全球定位系统轨迹中识别单模段。第三,我们引入了新的基于统计的地形特征,这些特征对交通模式检测更具辨别力。通过对大型轨迹数据 GeoLife 的广泛评估,我们的方法在精度方面比最先进的基线模型有显着的性能提升。从全球定位系统轨迹中更有效地识别单模段。第三,我们引入了新的基于统计的地形特征,这些特征对交通模式检测更具辨别力。通过对大型轨迹数据 GeoLife 的广泛评估,我们的方法在精度方面比最先进的基线模型有显着的性能提升。从全球定位系统轨迹中更有效地识别单模段。第三,我们引入了新的基于统计的地形特征,这些特征对交通模式检测更具辨别力。通过对大型轨迹数据 GeoLife 的广泛评估,我们的方法在精度方面比最先进的基线模型有显着的性能提升。
更新日期:2020-05-27
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