Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut

Abstract

Annual average daily traffic (AADT) is one of the most inevitable elements for both transportation planning and traffic safety analysis. For state Departments of Transportations (DOTs), collecting AADT data is a critical and demanding task, normally accomplished through a combination of permanent and temporary traffic count stations, which has been proved to be extremely labor-intensive and time-consuming. Consequently, due to limited resources, it is typically performed for the state-maintained highways rather than the low-volume roadways maintained by town jurisdictions. Therefore, it is necessary to develop innovative and cost-effective approaches to generate AADT for low-volume roadways while still maintaining accuracy, including data driven analytical methodologies. To this end, this study conducted a comprehensive literature review of methodologies and relevant data used for AADT generation and estimation. Based on the data availability, data elements used for modeling AADT in Connecticut (CT) were collected. Specifically, AADT data for town-maintained highways from 2016 to 2020 was collected from the Streetlight platform and calibrated to fit the local condition in CT. Furthermore, machine learning algorithms were developed to predict future AADT beyond 2020. The model validation results indicate that the AADT estimated by this study is robust and reliable in terms of prediction accuracy, and it can serve as a valid asset in transportation planning and traffic safety analysis to practitioners and transportation agencies.