Mapping of truck traffic in New Jersey using weigh-in-motion data
This study presents an innovative hierarchical Bayesian model for mapping of county level truck traffic in New Jersey. First, the model is estimated using truck counts. Then, using overweight truck counts from weigh-in-motion data as the response variable, the model is re-estimated. The goal in using the overweight trucks in the spatial model is to demonstrate the importance of representing their spatial variation due to their impact on the life of the roadway network elements. Finally, truck count maps are developed based on modelling results to visualise the effects of spatial covariates. The results of the study indicate that the most influential covariate for the truck traffic is the length of interstate roadways, followed by employment and population. The developed truck count maps can help transportation professionals on identifying and ranking the locations at an aggregate level, which requires closer attention.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
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Supplemental Notes:
- Abstract reprinted with permission of the Institution of Engineering and Technology.
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Authors:
- Demiroluk, Sami
- Ozbay, Kaan
- Nassif, Hani
- Publication Date: 2018-11
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References;
- Pagination: pp 1053-1061
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Serial:
- IET Intelligent Transport Systems
- Volume: 12
- Issue Number: 9
- Publisher: Institution of Engineering and Technology (IET)
- ISSN: 1751-956X
- EISSN: 1751-9578
- Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Bayes' theorem; Cartography; Image processing; Mapping; Traffic data; Truck traffic; Weigh in motion
- Geographic Terms: New Jersey
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01684844
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 31 2018 9:29AM