United States Department of Transportation
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Route based Freight Activity Metrics along the California State Highway System through a Pilot Multi Sensor Fusion System
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2024-12-31
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[PDF-1.50 MB]
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Edition:Final report (January 1, 2024 – December 31, 2024)
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Abstract:This research project investigates the integration of point detector data to enhance vehicle tracking for heavy-duty trucks in Southern California. Leveraging a combination of automated license plate readers (ALPR) and inductive signature data, we aim to enhance the accuracy of vehicle monitoring, addressing limitations of traditional telematics data, such as sampling bias and data quality issues. Our research draws upon a dataset from sites deployed in the Freight Mobility Living Laboratory (FML2) testbed, enabling a comprehensive analysis of truck activity. Employing a Bayesian Logit model and principal component analysis, we developed an innovative framework aimed at improving truck tracking accuracy across key routes by matching inductive signatures. Findings indicate that integrating these advanced data sources leads to more accurate classifications and tracking of freight vehicles, ultimately contributing to better-informed planning and environmental sustainability efforts by Caltrans, the California Air Resources Board, and local agencies. By focusing on critical highways like I-710 and SR-60, we identify significant opportunities for optimizing freight mobility and addressing the increasing pressures on transportation infrastructure in urban settings. This research highlights the importance of innovative data integration techniques in developing effective traffic management strategies that meet the evolving demands of urban freight operations.
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