Public Transportation Quality of Service: Factors, Models, and Applications is the first book to help researchers better understand the contributing factors that can improve public transportation perception among users. The book compiles in one place metrics currently dispersed in journal articles, government publications and book chapters. It critically analyzes currently available modeling methodologies such as the Ordered Logit/Probit model and Models of Structural Equations, highlighting their advantages and disadvantages. The book addresses models of desired quality, including the views of users and non-users, discussing the gap between desired and perceived quality.

The book also examines data mining approaches such as decision trees and neural networks, showing how to involve the public in the decision-making process to create policies that encourage public transport demand. Measuring passenger's views on public transportation is of critical concern to promote wider transit use in cities around the world.