Exploring a Novel Public-Private-Partnership Data Sharing Policy through a collaborative Arterial Traffic Management System

Khadka, Swastik; Li, Pengfei; Roy, Somdut; Hunter, Michael; Guin, Angshuman

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Exploring a Novel Public-Private-Partnership Data Sharing Policy through a collaborative Arterial Traffic Management System

2023-04-01
By Khadka, Swastik ; Li, Pengfei ; Roy, Somdut

File Language:

English

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Creators:

Khadka, Swastik ; Li, Pengfei ; Roy, Somdut ; Hunter, Michael ; Guin, Angshuman
Corporate Creators:

University of Texas at Arlington. Department of Civil Engineering ; Georgia Institute of Technology. School of Civil and Environmental Engineering ; Center for Transportation, Equity, Decisions & Dollars (CTEDD). University of Texas
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:

CTEDD 020-02
Resource Type:

Tech Report
Geographical Coverage:

Texas;United States
Edition:

Final Report
Corporate Publisher:

Center for Transportation, Equity, Decisions and Dollars (CTEDD) (UTC)
Abstract:

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet-of-Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to 3rd-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/72387/dot_72387_DS1.pdf
File Type:

[PDF - 3.41 MB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:21d9cf85e7bc177cc34ad0cc55785d1036de40f5aed2c838a9813cf190f4f5f1c9195442e966f84d7454246f4733792e1fc3c42ff8e7d9cb5557089c3ec0edd8

File Language:

English

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