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Detecting Pattern Changes in Individual Travel Behavior from Vehicle GPS/GNSS Data.
1State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|April 23, 2020
Summary
Detecting changes in individual travel behavior is crucial for businesses. This study presents a new method using GPS/GNSS data and clustering to identify shifts in spatial and frequency travel patterns.
Area of Science:
- Transportation Science
- Data Science
- Human Mobility Analysis
Background:
- Individual travel behavior exhibits long-term variability, impacting service providers.
- Detecting these behavioral shifts is essential for adaptive business strategies.
- Existing methods may not adequately capture nuanced changes in travel patterns.
Purpose of the Study:
- To develop a novel methodology for detecting changes in individual travel behavior patterns.
- To utilize vehicle Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) data for pattern analysis.
- To enable timely adaptation of products and services to evolving user needs.
Main Methods:
- Defining travel behavior patterns in spatial and frequency dimensions.
- Employing a clustering algorithm to identify patterns from GPS/GNSS data.
- Introducing a pattern-matching metric to detect predefined change scenarios.
Main Results:
- The methodology successfully identified significant changes in travel behavior patterns.
- Demonstrated effectiveness using real-world GPS datasets from multiple users.
- Validated the ability to detect spatial and frequency pattern alterations.
Conclusions:
- The proposed methodology offers a robust approach for monitoring individual travel behavior dynamics.
- Enables businesses to proactively respond to long-term shifts in customer mobility.
- Highlights the utility of GPS/GNSS data for understanding and predicting human movement.
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