Uncertainty in Cell-Phone Generated Bike and Pedestrian Volumes

Big data from mobile phones are increasingly used in transport research and planning, offering unprecedented spatial and temporal detail. However, data accuracy remains unclear. This study evaluates Replica, a dataset modelled from mobile phone GPS data, by comparing modeled volumes for motor vehicles, bicycles, and pedestrians against field counts in Santa Barbara, California. Car volumes were modeled with high accuracy (R² = 0.92), while bicycle (R² = 0.23) and pedestrian (R² = 0.05) estimates showed substantial uncertainty. When using transport data generated from mobile phone GPS, additional caution is needed for non-motorized modes.

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Findings
Published
2025-10-17
DOI
https://doi.org/10.32866/001c.145234
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
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article

Uncertainty in Cell-Phone Generated Bike and Pedestrian Volumes

Trisalyn Nelson, Lily Heidger, Dan Willet
Findings
Human Mobility and Location-Based Analysis
article

Uncertainty in Cell-Phone Generated Bike and Pedestrian Volumes

Trisalyn Nelson, Lily Heidger, Dan Willet
article en

Abstract

Big data from mobile phones are increasingly used in transport research and planning, offering unprecedented spatial and temporal detail. However, data accuracy remains unclear. This study evaluates Replica, a dataset modelled from mobile phone GPS data, by comparing modeled volumes for motor vehicles, bicycles, and pedestrians against field counts in Santa Barbara, California. Car volumes were modeled with high accuracy (R² = 0.92), while bicycle (R² = 0.23) and pedestrian (R² = 0.05) estimates showed substantial uncertainty. When using transport data generated from mobile phone GPS, additional caution is needed for non-motorized modes.

Findings
University of California, Santa Barbara (US)
Openalex Percentile: Top 70%
Human Mobility and Location-Based Analysis
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