Review and evaluation of methods in transport mode detection based on GPS tracking data

Paria Sadeghian · Johan Håkansson · Xiaoyun Zhao

64 citations0/0 files storedJournal of Traffic and Transportation Engineering (English Edition)

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Human Mobility and Location-Based Analysis Data Management and Algorithms Traffic Prediction and Management Techniques

Abstract

Mobility data, based on global positioning system (GPS) tracking, have been widely used in many areas, such as analyzing travel patterns, investigating transport safety and efficiency, and evaluating travel impacts. Transport modes are essential factors in understanding mobility within the transport system. Therefore, in this study, a significant number of algorithms were tested for transport mode detection. However, no conclusive recommendations can be drawn regarding which method should be used. The evaluation of the performance of the algorithms was not discussed systematically either in current literature. This paper aims to provide an in-depth review of the methods applied in transport mode detection based on GPS tracking data. The performances of the reviewed methods are then compared and evaluated to provide guidance in choosing algorithms for transport mode detection based on GPS tracking data. The results indicate that the majority of current studies are based on a supervised learning method for transport mode detection. Many of the reviewed methods first require manual dataset labeling, which can produce major drawbacks, such as inefficiency and human errors. It was also found that deep learning approaches have the potential to deal with large amounts of unlabeled raw GPS datasets and increase the accuracy and efficiency of transport mode detection.

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References

2009Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement 2009Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.2009Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement 2008Understanding mobility based on GPS data 2008Learning transportation mode from raw gps data for geographic applications on the web 2003Inferring High-Level Behavior from Low-Level Sensors 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands 2010Understanding transportation modes based on GPS data for web applications

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2024Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS 2022A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data 2024Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data 2021App-based automatic collection of travel behaviour: A field study comparison with self-reported behaviour

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