This paper provides an introduction to Structural Equation Modeling (SEM) and reviews its applications in travel behavior research. SEM is a flexible multivariate statistical modeling technique that can handle a large number of endogenous and exogenous variables, as well as latent variables. It has been used in travel behavior research since around 1980 and its use is rapidly increasing due to improved software. The paper covers the methodology of SEM, including its defining features, model specification, identification, estimation methods, and goodness-of-fit criteria. It also discusses the historical development of SEM, highlighting contributions from path analysis, latent variable modeling, and general covariance estimation methods. The paper then reviews various applications of SEM in travel behavior research, including models of vehicle ownership and usage, dynamic travel demand modeling, activity-based travel demand modeling, and the relationship between attitudes, perceptions, and hypothetical choices. The applications demonstrate the versatility of SEM in capturing complex causal relationships in travel behavior data.This paper provides an introduction to Structural Equation Modeling (SEM) and reviews its applications in travel behavior research. SEM is a flexible multivariate statistical modeling technique that can handle a large number of endogenous and exogenous variables, as well as latent variables. It has been used in travel behavior research since around 1980 and its use is rapidly increasing due to improved software. The paper covers the methodology of SEM, including its defining features, model specification, identification, estimation methods, and goodness-of-fit criteria. It also discusses the historical development of SEM, highlighting contributions from path analysis, latent variable modeling, and general covariance estimation methods. The paper then reviews various applications of SEM in travel behavior research, including models of vehicle ownership and usage, dynamic travel demand modeling, activity-based travel demand modeling, and the relationship between attitudes, perceptions, and hypothetical choices. The applications demonstrate the versatility of SEM in capturing complex causal relationships in travel behavior data.