GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset
Hossein Amiri, Richard Yang, and Andreas Zufle

TL;DR
This paper introduces GeoLife+, a large-scale simulated human mobility dataset calibrated to real GeoLife data, combining real-world statistical features with comprehensive simulation for research and application purposes.
Contribution
We develop a calibration method using genetic algorithms to generate realistic, large-scale simulated trajectory datasets based on GeoLife's statistical features.
Findings
GeoLife+ datasets include up to 100,000 users over 6 months.
Calibration achieves high similarity to real GeoLife data.
Large-scale simulated data supports diverse mobility research.
Abstract
Analyzing individual human trajectory data helps our understanding of human mobility and finds many commercial and academic applications. There are two main approaches to accessing trajectory data for research: one involves using real-world datasets like GeoLife, while the other employs simulations to synthesize data. Real-world data provides insights from real human activities, but such data is generally sparse due to voluntary participation. Conversely, simulated data can be more comprehensive but may capture unrealistic human behavior. In this Data and Resource paper, we combine the benefit of both by leveraging the statistical features of real-world data and the comprehensiveness of simulated data. Specifically, we extract features from the real-world GeoLife dataset such as the average number of individual daily trips, average radius of gyration, and maximum and minimum trip…
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Taxonomy
TopicsData Management and Algorithms · Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis
