Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization

Heckendorn, Robert B; Eapen, Neeta A; Abdel-Rahim, Ahmed; Pacific Northwest Transportation Consortium (PacTrans) (UTC); University of Washington. University Transportation Center for Region 10

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Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization

2023-06-01
Datasets Available
By Heckendorn, Robert B ; Eapen, Neeta A ; Abdel-Rahim, Ahmed

[PDF-505.38 KB]

English

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Creators:

Heckendorn, Robert B ; Eapen, Neeta A ; Abdel-Rahim, Ahmed

Heckendorn, Robert B ; Eapen, Neeta A ; Abdel-Rahim, Ahmed Less -
Corporate Creators:

Pacific Northwest Transportation Consortium (PacTrans) (UTC) ; University of Washington. University Transportation Center for Region 10

Pacific Northwest Transportation Consortium (PacTrans) (UTC) ; University of Washington. University Transportation Center for Region 10 Less -
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

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

[+]

Machine Learning Mesoscopic Traffic Flow Traffic Signal Timing Traffic Simulation Travel Time
Publication/ Report Number:

2021-S-UI-3
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

Pacific Northwest Transportation Consortium (PacTrans) (UTC)
Abstract:

Traffic signal control of the future will adapt in real time to traffic, driving, and environmental conditions. The adaptations will require real-time optimization based on traffic monitoring. However, for optimization to work, it must be able to predict what traffic will do in response to changes in signal timing. Furthermore, it must do this prediction very efficiently, in a short time horizon, so that the optimization process can evaluate many alternative signal timing parameters. Instead of using a “one size fits all” legacy function for prediction of traffic flow, this project took steps to pioneer the use of machine learning techniques to learn how traffic behaves on particular segments of streets in response to traffic and driving conditions and used that learning to build a high-speed simulator to predict the arrival times of cars at intersections.
Format:

PDF
Funding:

69A355174110
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:a2d47d95ed20b1e6e72cf07edf60467c6ada718a63f2e25497266859bf3b0b16
Download URL:

https://rosap.ntl.bts.gov/view/dot/72475/dot_72475_DS1.pdf
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Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization

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