Road Work Ahead: Using Deep Neural Networks to Estimate the Impacts of Work Zones

Authors

Abbas Rashidi, University of Utah
Ali Hassandokht Mashhadi, University of Utah

Document Type

Report

Publication Date

9-2021

Subjects

Machine learning, Road work zones -- Mobility impacts of, Transportation agencies -- Decision making, Transportation -- Forecasting

Abstract

Roadside construction - be it a detour, a closed lane, or a slow weave past workers and equipment - work zones impact traffic flow and travel times on a system-wide level. The ability to predict exactly what those impacts will be, and plan for them, would be a major help to both transportation agencies and road users. Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neural network model for analyzing the automobile traffic impacts of construction zones.

Description

This is a summary of TREC research project NITC-SS-1362, from the NITC program of TREC at Portland State University, and can be found onlineat:https://nitc.trec.pdx.edu/research/project/1362/,

Final Report NITC-SS-1362 can be found at: https://doi.org/10.15760/trec.263

Persistent Identifier

https://archives.pdx.edu/ds/psu/36421

Recommended Citation

Mashhadi, A. H., Rashidi, A., Road Work Ahead: Using Deep Neural Networks to Estimate the Impacts of Work Zones. Project Brief NITC-SS-1362, Portland, OR: Transportation Research and Education Center (TREC), 2021.