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Implementing a Community-Based Mobility Lab: improving Traffic, Protecting Data Privacy
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2024-07-31
[PDF-3.92 MB]
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Edition:Final report (8/1/2023 – 7/31/2023)
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Abstract:This study pilots a scalable methodology to conduct community-based mobility research to address critical safety, environmental, and traffic congestion issues while also addressing the data privacy issues such as transparency and accountability associated with the extensive data collections necessary for predictive traffic modeling. The study provides a blueprint for a three-phase scalable methodological approach to community-based mobility studies including the collection of video footage, the data processing for the predictive traffic modeling technology and the approach to addressing related data privacy concerns. A key focus of this study regards the establishment of an efficient and practical way of balancing between (1) gathering the extensive data required to model the intersection of study as it relates to addressing critical safety, environmental, and traffic congestion issues with (2) a corresponding need to respect residents’ concerns about smart technologies and data privacy. Using data from the video recordings, the research team used an artificial neural network to develop a predictive traffic model. To address data privacy concerns for residents living near the intersection of study during the footage collection, researchers conducted data walks – a 1.5-mile guided walk through the study area during which researchers prompted study participants to reflect on various smart technologies, including traffic cameras and the recording sessions in place.
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