Estimating Construction Work Zones Capacity Using Deep Neural Network
Construction work zones are a major cause of traffic disruptions and delays on roadways. Thus, accurate estimation of traffic states within work zones would be beneficial to both road users and transportation agencies. To this end, a deep neural network has been calibrated based on the hourly data points collected from 80 projects completed in Utah from 2013 to 2020. Reported results show that the proposed model outperforms its counterparts from the literature while achieving the R score of 0.97, RMSE of 185, and MAE of 108. Comparing the study results with the Highway Capacity Manual 2016 (HCM) shows that the proposed model is a good alternative for work zone capacity estimation. Future studies could leverage the probe vehicle data to improve the model’s performance by decreasing the RMSE and MAE values.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483961
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Supplemental Notes:
- © 2022 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Mashhadi, Ali Hassandokht
- Markovic, Nikola
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0000-0003-0883-2701
- Rashidi, Abbas
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0000-0002-4342-0588
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Conference:
- Construction Research Congress 2022
- Location: Arlington Virginia, United States
- Date: 2022-3-9 to 2022-3-12
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Web
- Pagination: pp 98-107
- Monograph Title: Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics
Subject/Index Terms
- TRT Terms: Construction; Highway traffic control; Traffic delays; Work zones
- Geographic Terms: Utah
- Subject Areas: Construction; Highways;
Filing Info
- Accession Number: 01842225
- Record Type: Publication
- ISBN: 9780784483961
- Files: TRIS, ASCE
- Created Date: Apr 12 2022 10:05AM