Estimating temporal truck traffic patterns on rural highway systems: A spatially-aware machine learning approach

Sina Abolhoseini, Jonathan D. Regehr and Babak Mehran

Journal of Transport Geography, 2025, vol. 128, issue C

Abstract: System- or network-wide truck traffic statistics are crucial for transportation planning, infrastructure design, and managing transport networks. However, producing these statistics is more challenging than for total traffic. Current methods depend largely on engineering judgment, making them labor-intensive, susceptible to human error, and inconsistent across jurisdictions. This study aims to address these challenges by proposing a machine learning (ML) solution, utilizing Random Forest spatial classification algorithm, to automate the assignment of short-duration count stations (SCSs) to temporal truck traffic pattern groups (TTPGs) and their attribution to road segments lacking volume data. The study further focuses on identifying the key factors that influence truck traffic patterns and creating models that address data limitations. The methodology was tested using data from Manitoba's 2019 traffic monitoring program, achieving an accuracy rate of over 80 %, showcasing its potential for broader use. The proposed ML approach offers an automated, reliable, accurate, and transferable method for analyzing truck traffic patterns, reducing processing time and reliance on subjective expertise.

Keywords: Traffic pattern; Traffic volume; Truck; Machine learning; Spatial classification; Random Forest classification (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:128:y:2025:i:c:s0966692325002364

DOI: 10.1016/j.jtrangeo.2025.104345

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