A Semantics-Based Trajectory Segmentation Simplification Method

Minshi Liu,Yi Long,Guifang He

doi:10.1007/s41651-021-00088-5

Minshi Liu, Yi Long + Show 1 more

Open Access

https://doi.org/10.1007/s41651-021-00088-5

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Abstract

With the development of mobile positioning technology, a large amount of mobile trajectory data has been generated. Therefore, to store, process and mine trajectory data in a better way, trajectory data simplification is imperative. Current trajectory data simplification methods are either based on spatiotemporal features or semantic features, such as road network structure, but they do not consider semantic features of a trajectory stop. To overcome this limitation, this study presents a trajectory segmentation simplification method based on stop features. The proposed method first extracts the stop feature of a trajectory, then divides the trajectory into move segments and stop segments based on the stop features, and finally simplifies the obtained segments. The proposed method is verified by experiments on personal trajectory data and taxi trajectory data. Compared with the classic spatiotemporal simplification method, the proposed method has higher spatiotemporal and semantic accuracy under different simplification scales. The proposed method is especially suitable for trajectory data with more stop features.

Highlights

With the development of mobile positioning technologies, such as the Global Positioning System (GPS), Global System for Mobile Communications (GSM), and Radio Frequency Identification (RFID), a large number of mobile positioning devices with high positioning accuracy and low price have been proposed, including mobile phones, GPS collectors, and personal digital assistants (PDAs)
Because trajectory data are commonly collected on the road network, a trajectory simplification method constrained by the road network and a trajectory data simplification method after map matching have been proposed (Kellaris et al 2009, 2013; Popa et al 2015)
The experimental data were the data of two personal GPS trajectories in the city of Nanjing (Fig. 5), which can be downloaded from the shared database

Summary

Introduction

With the development of mobile positioning technologies, such as the Global Positioning System (GPS), Global System for Mobile Communications (GSM), and Radio Frequency Identification (RFID), a large number of mobile positioning devices with high positioning accuracy and low price have been proposed, including mobile phones, GPS collectors, and personal digital assistants (PDAs). In the T-Drive data set, there are 10,357 taxis, the sampling frequency is 5 s, and each record occupies 40 b (Yuan et al 2010), so the amount of trajectory data of all taxi trajectory in Beijing city can reach 4 GB per day Storing and indexing such massive data can cause high economic costs and low time efficiency, and it is challenging to process. Because trajectory data are commonly collected on the road network, a trajectory simplification method constrained by the road network and a trajectory data simplification method after map matching have been proposed (Kellaris et al 2009, 2013; Popa et al 2015) In this way, the trajectory reduction result is more in line with the real situation

Methods

Results

Conclusion