Surface transportation has evolved through technology advancements using parallel
knowledge areas such as machine learning (ML). However, the transportation industry has …

Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …

This book presents chapters that discuss contemporary issues related to the digital
economy, mainly in relation to the challenges and opportunities by artificial intelligence and …

Prediction of air pollution index may help in traffic routing and identifying serious pollutants.
Modeling of the complex relationships between these variables by sophisticated methods in …

This paper proposes a combination of hybrid models like Input Variable Selection (IVS),
Machine Learning (ML), and regression method to predict, model, and forecast the daily …

The impact of traffic pollution on the health and safety of residents that live in roadside
residential buildings has been a major concern for governments. This study investigated the …

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a
large drag on the sustainable developments of future metropolises. Here, combined with the …

Carbon dioxide emission has emerged as a major concern in the 21st century. The rising
global average temperature and its impact on climate change has a major impact on the …

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by
various factors. We developed prediction models for short-term UFP exposures using street …

The continuously growing human activity in large and densely populated cities pollutes air
and consequently puts public health in danger. This is why air quality monitoring is …