Open AccessMapping of truck traffic in New Jersey using weigh‐in‐motion data
Author(s) -
Demiroluk Sami,
Ozbay Kaan,
Nassif Hani
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.0055
Subject(s) - truck , weigh in motion , transport engineering , covariate , ranking (information retrieval) , aggregate (composite) , bayesian probability , population , variable (mathematics) , computer science , geography , statistics , engineering , machine learning , mathematics , artificial intelligence , automotive engineering , environmental health , medicine , mathematical analysis , materials science , composite material
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.