United States Department of Transportation
i
Freight Volume Modeling on Major Highway Links
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2021-08-02
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[PDF-2.35 MB]
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Edition:Final report (08/16/2020 - 08/16/2021
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Abstract:With this research the authors would like to validate the feasibility of freight volume estimation on major highways links from accurate but sparse sensor data, by studying a restricted control area covering approximately 12 square miles around the Ports of Los Angeles and Long Beach where freight volume is most relevant. The authors researched creating a real-world dataset of real-world data in this region, however, due to the COVID 19 pandemic the authors had to limit the collection to publicly available Caltrans CCTV video footage and leveraged the authors' ADMS traffic database to generate synthetic data. The authors have implemented state-of-the-art computer vision algorithms, which were used to classify trucks and define truck categories optimized for best performance. These results show that it is feasible to use CCTV cameras to detect and classify trucks and that the process can be fully automated. In parallel, the authors created a truck simulator to generate realistic truck trajectories between predefined locations, and developed algorithms to estimate freight on links with different heuristics. The authors' approach provides the best results by relating compatible observations across sensors using travel-time information estimated on current traffic conditions. The authors' preliminary results show that freight volume estimation on major highways links is feasible.
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