access icon free 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.

Inspec keywords: road traffic; road vehicles; geophysical image processing; cartography; data visualisation; traffic engineering computing; image motion analysis; Bayes methods

Other keywords: location ranking; location identification; interstate roadways length; spatial covariate effect visualisation; spatial variation representation; county level truck traffic mapping; roadway network elements; weigh-in-motion data; New Jersey; overweight truck counts; innovative hierarchical Bayesian model

Subjects: Geography and cartography computing; Geophysical techniques and equipment; Traffic engineering computing; Optical, image and video signal processing; Computer vision and image processing techniques; Other topics in statistics; Other topics in statistics

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