Real-Time Data-Based Decision Support System for Arterial Traffic Management

Hadi, Mohammed; Elefteriadou, Lily; Mata, Hector; Amini, Ehsan; Florida International University. Department of Civil and Environmental Engineering; University of Florida. Dept. of Civil and Coastal Engineering; Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE); University of Florida. Transportation Institute

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Real-Time Data-Based Decision Support System for Arterial Traffic Management

2023-04-20
By Hadi, Mohammed ; Elefteriadou, Lily ; Mata, Hector ; ...

[PDF-1.94 MB]

English

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Details:

Creators:

Hadi, Mohammed ; Elefteriadou, Lily ; Mata, Hector ; Amini, Ehsan

Hadi, Mohammed ; Elefteriadou, Lily ; Mata, Hector ; Amini, Ehsan Less -
Corporate Creators:

Florida International University. Department of Civil and Environmental Engineering ; University of Florida. Dept. of Civil and Coastal Engineering ; Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE) ; University of Florida. Transportation Institute

Florida International University. Department of Civil and Environmental Engineering ; University of Florida. Dept. of Civil and Coastal Engineering ; Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE) ; University of Florida. Transportation Institute 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:

[+]

Arterial Highways Highway Traffic Control Machine Learning Optimization Real Time Control Strategic Planning Traffic Signal Control Systems Traffic Signal Timing
Publication/ Report Number:

STRIDE Project J2
Resource Type:

Tech Report
Geographical Coverage:

United States ; Florida

United States ; Florida Less -
Edition:

Final Report 8/15/2018 to 4/20/2023
Corporate Publisher:

Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
Abstract:

This study developed and evaluated a TRPS strategy based on supervised and unsupervised machine learning combined with signal timing optimization to address the issues with traditional TRPS. The strategy fills an important gap in providing a proactive traffic control that makes use of Automated Traffic Signal Performance Measures (ATSPM) measures-based data that are becoming available sources, including high-resolution controller data. This study also explored a methodology and evaluated multiple algorithms for the short-term prediction of the traffic state for the next half an hour. The results revealed that the ANN algorithm produced the best results in terms of accuracy and areas under the curve. Thus, this study used the ANN prediction model in the remaining task to implement and evaluate the prediction to support the activation of the signal timing plans in real-time operations. The study then assessed the performance of the predictive TRPS based on clustering and prediction by evaluating five different scenarios of signal timing plan selection. The results of the project case study showed that the predictive TRPS method can decrease the travel time by 7 percent compared to existing traffic signals, 4% compared to optimizing for a fixed signal timing plan based on a signature day for the whole database, and 17% compared to optimizing signal timing for a random day in the data. This shows that the TRPS based on traffic pattern identification and prediction has the potential to improve traffic performance compared to other assessed optimization scenarios.
Format:

PDF
Funding:

69A3551747104
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:106e698d6620f0549fed78a2e1ef9c703ff7abd026fabf6fc198595d19636b67
Download URL:

https://rosap.ntl.bts.gov/view/dot/72835/dot_72835_DS1.pdf
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Real-Time Data-Based Decision Support System for Arterial Traffic Management

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