Traffic estimation in work zones using a custom regression model and data augmentation

Abstract

Accurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in traffic data, making accurate volume estimation difficult. Addressing this, our study utilized a custom regularized model and variational autoencoders (VAE) to generate synthetic data that improves traffic volume estimations in these challenging areas. The proposed method not only bridges the data gaps between sparse CCS sensors but also outperforms several benchmark models, as measured by mean absolute percentage error, root mean square error, and mean absolute error. Moreover, the effectiveness of VAE-augmented models in enhancing the precision and accuracy of traffic volume estimations further underscores the benefits of integrating synthetic data into traffic-modeling approaches. These findings highlight the potential of the proposed approach to enhance traffic volume estimation in construction work zones and assist transportation agencies in making informed decisions for traffic management.