In the quest to optimize traffic management through machine learning (ML), this dissertation
delves into three foundational areas: Traffic state estimation (TSE), traffic pattern …

Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation
systems (ITS) operations. In the literature, TSE methods are divided into model-driven …

We present a physics-informed deep learning (PIDL) approach to tackle the challenge of
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …

Traffic state estimation (TSE), which reconstructs the traffic variables (eg, speed, flow) on
road segments using partially observed data, plays an essential role in intelligent …

Traffic state estimation (TSE) reconstructs the traffic variables (eg, density or average
velocity) on road segments using partially observed data, which is important for traffic …

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …

Accurate and prompt traffic data are necessary for the successful management of major
events. Computer vision techniques, such as convolutional neural network (CNN) applied …

Traffic flow information can be employed in an intelligent transportation system to detect and
manage traffic congestion. One of the key elements in determining the traffic flow information …

Estimating traffic states efficiently and accurately is a fundamental problem in transportation
engineering for traffic control and operation. In recent years, there is a growth of interest in …

Traffic accidents, a significant public health burden with substantial economic costs,
necessitate proactive measures for sustainable transportation. This study investigates the …