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Research: Freight Volume
Modeling on Major Highway Links
Recent METRANS funded research studied the feasibility of freight traffic modeling from precise and localized but sparse freight data and built machine learning models that can predict freight volumes on highway segments.
One of the biggest challenges in freight transportation planning is the lack of data on truck movements. The findings of this report offer a privacy-preserving, cost-effective approach for estimating truck traffic using existing roadside infrastructure such as CCTV cameras and weigh-in-motion sensors.
By enabling accurate freight movement analysis without relying on private GPS data, agencies can establish more informed transportation planning, infrastructure investment, and freight policy decisions.
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