TY - JOUR
T1 - Mapping of truck traffic in New Jersey using weigh-in-motion data
AU - Demiroluk, Sami
AU - Ozbay, Kaan
AU - Nassif, Hani
N1 - Funding Information:
This study was partially supported by the NJDOT and Tier 1 University Transportation Center, C2SMART funded by USDOT's UTC program. The help and support of Paul Truban and Andrew Ludasi of the NJDOT are greatly appreciated. The contents of this paper reflect views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents of this paper do not necessarily reflect the official views or policies of the agencies.
Publisher Copyright:
© The Institution of Engineering and Technology 2018
PY - 2018/11/1
Y1 - 2018/11/1
N2 - 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.
AB - 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.
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U2 - 10.1049/iet-its.2018.0055
DO - 10.1049/iet-its.2018.0055
M3 - Article
AN - SCOPUS:85054962593
SN - 1751-956X
VL - 12
SP - 1053
EP - 1061
JO - IET Intelligent Transport Systems
JF - IET Intelligent Transport Systems
IS - 9
ER -