The digital transformation of modern cities by integrating advanced information,
communication, and computing technologies has marked the epoch of data-driven smart city …

In recent years, advances in deep learning have had a significant impact in the
transportation domain, notably through the use of generative adversarial networks (GAN). As …

The most crucial component of any smart city traffic management system is traffic flow
prediction. It can assist a driver in selecting the most efficient route to their destination. The …

Inadequate training data is a significant challenge for deep learning techniques, particularly
in applications where data is difficult to get, and publicly available datasets are uncommon …

The city's infrastructure is considered the backbone of any country's development process
and there are numerous factors that contribute to its growth. Among these factors, proper …

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 …

Artificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban
areas grow by processing data for safety enhancements, predictive analysis, and traffic …

High-quality traffic flow data is foundational to the study of traffic issues and practical
engineering applications. The development of traffic flow detection methods has greatly …

Deep learning prediction models have emerged as the most widely used for the
development of intelligent transportation systems (ITS), and their success is strongly reliant …

This study investigates the effectiveness of data augmentation techniques like noise
creation, scaling, shifting, and Grey models (GMs) for improving prediction model …