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Tsinghua Science and Technology 2021, 26(4): 403-416 https://doi.org/10.26599/TST.2020.9010014
Open Access | Issue | Published: 04 January 2021

Transportation Mode Identification with GPS Trajectory Data and GIS Information

Show Author's Information Ji Li,  Xin Pei,  Xuejiao Wang,  Danya Yao( ),  Yi Zhang,  Yun Yue
Department of Automation, Tsinghua University.
National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China.
National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (NEL-PSRPC), Beijing 100041, China.
Keywords:
transportation mode, Global Positioning System (GPS), Geographic Information System (GIS), random forest
Cite this article:
Li J, Pei X, Wang X, et al. Transportation Mode Identification with GPS Trajectory Data and GIS Information. Tsinghua Science and Technology, 2021, 26(4): 403-416. https://doi.org/10.26599/TST.2020.9010014
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Abstract Full text About this article

Global Positioning System (GPS) trajectory data can be used to infer transportation modes at certain times and locations. Such data have important applications in many transportation research fields, for instance, to detect the movement mode of travelers, calculate traffic flow in an area, and predict the traffic flow at a certain time in the future. In this paper, we propose a novel method to infer transportation modes from GPS trajectory data and Geographic Information System (GIS) information. This method is based on feature extraction and machine learning classification algorithms. While using GIS information to improve inference accuracy, we ensure that the algorithm is simple and easy to use on mobile devices. Applied to GeoLife GPS trajectory dataset, our method achieves 91.1% accuracy while inferring transportation modes, such as walking, bike, bus, car, and subway, with random forest classification algorithm. GIS features in our method improved the overall accuracy by 2.5% while raising the recall of the bus and subway transportation mode categories by 3.4% and 18.5%. We believe that many algorithms used in detecting the transportation modes from GPS trajectory data that do not utilize GIS information can improve their inference accuracy by using our GIS features, with a slight increase in the consumption of data storage and computing resources.


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Transportation Mode Identification with GPS Trajectory Data and GIS Information

Show Author's information Ji Li, Xin Pei, Xuejiao Wang, Danya Yao( ), Yi Zhang, Yun Yue
Department of Automation, Tsinghua University.
National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China.
National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (NEL-PSRPC), Beijing 100041, China.

Abstract

Global Positioning System (GPS) trajectory data can be used to infer transportation modes at certain times and locations. Such data have important applications in many transportation research fields, for instance, to detect the movement mode of travelers, calculate traffic flow in an area, and predict the traffic flow at a certain time in the future. In this paper, we propose a novel method to infer transportation modes from GPS trajectory data and Geographic Information System (GIS) information. This method is based on feature extraction and machine learning classification algorithms. While using GIS information to improve inference accuracy, we ensure that the algorithm is simple and easy to use on mobile devices. Applied to GeoLife GPS trajectory dataset, our method achieves 91.1% accuracy while inferring transportation modes, such as walking, bike, bus, car, and subway, with random forest classification algorithm. GIS features in our method improved the overall accuracy by 2.5% while raising the recall of the bus and subway transportation mode categories by 3.4% and 18.5%. We believe that many algorithms used in detecting the transportation modes from GPS trajectory data that do not utilize GIS information can improve their inference accuracy by using our GIS features, with a slight increase in the consumption of data storage and computing resources.

Keywords: transportation mode, Global Positioning System (GPS), Geographic Information System (GIS), random forest

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Publication history

Received: 16 October 2019
Accepted: 06 April 2020
Published: 04 January 2021
Issue date: August 2021

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© The author(s) 2021

Acknowledgements

This research was supported in part by the National Key Basic Research and Development Program of China (No. 2017YFC0820502), the Director Foundation Project of National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC), and the National Natural Science Foundation of China (No. 61673233).

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