Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway Environment

Sarkar, Abhijit; Papakis, Ioannis; Herbers, Eileen; Viray, Reginald

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Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway Environment

2023-12-01
By Sarkar, Abhijit ; Papakis, Ioannis ; Herbers, Eileen

File Language:

English

Details

Creators:

Sarkar, Abhijit ; Papakis, Ioannis ; Herbers, Eileen ; Viray, Reginald
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Virginia Tech Transportation Institute
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

04-121
Resource Type:

Tech Report
Geographical Coverage:

United States ; Virginia
Edition:

Final Research Report 01/2019-12/2023
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Abstract:

Automatic traffic monitoring is becoming an important investment for transportation specialists, especially as the overall volume of traffic continues to increase, as do crashes at intersections. Infrastructure cameras can be a good source of information for automatic monitoring of traffic situations at intersections. However, efficient computer vision methods that can process the video data effectively are required for this endeavor. Intersection cameras often record video data that are of low quality and low frame rate, making them challenging to use. In this project, we have demonstrated how traffic cameras can be used to automatically track roadway agents, find their kinematic behavior, and devise a safety measurement strategy, leveraging recent advancements in computer vision and deep learning. In this process, we have specifically focused on the Virginia Beach area and used publicly available traffic data to demonstrate our results.
Format:

PDF
Funding:

69A3551747115/ Project 04-121
Download URL:

https://rosap.ntl.bts.gov/view/dot/73407/dot_73407_DS1.pdf
File Type:

[PDF - 4.01 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:a399e9aa5a5c7d3b200e441010ed2f1f74ba4283122f1512879b01e6fd2ab26e5ae5ddd6f40611a6379053b4009188c6cb9dbad3379467684f1625d5ac4ab562

File Language:

English

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