Structural Equation Modeling (SEM) is a flexible statistical technique used to analyze complex relationships between variables, including latent (unobserved) variables. It has been applied in travel behavior research since the 1980s, with increasing use due to improved software. SEM allows researchers to model both endogenous and exogenous variables, as well as latent variables, and to test hypotheses about causal relationships. It is a confirmatory method, requiring the modeler to define the relationships between variables. SEM can separate measurement errors from model errors and correlate error terms. It is estimated using covariance analysis, and goodness-of-fit tests are used to evaluate model accuracy. SEM is widely used in psychology, sociology, and other fields, and its application in travel behavior research has grown significantly. SEM can handle various types of data, including discrete and ordinal variables, and allows for the modeling of mediating variables and error-term relationships. It is also useful for handling non-normal data and missing values. SEM has been applied to various aspects of travel behavior, including travel demand modeling, dynamic travel demand modeling, activity-based travel demand modeling, and modeling attitudes, perceptions, and hypothetical choices. SEM has been used to analyze the effects of car ownership, travel time, mode choice, and other factors on travel behavior. It has also been used to model the relationship between attitudes and behavior, and to test the validity of stated preference data in relation to revealed preference data. SEM is a powerful tool for analyzing complex travel behavior data and has been widely adopted in transportation research.Structural Equation Modeling (SEM) is a flexible statistical technique used to analyze complex relationships between variables, including latent (unobserved) variables. It has been applied in travel behavior research since the 1980s, with increasing use due to improved software. SEM allows researchers to model both endogenous and exogenous variables, as well as latent variables, and to test hypotheses about causal relationships. It is a confirmatory method, requiring the modeler to define the relationships between variables. SEM can separate measurement errors from model errors and correlate error terms. It is estimated using covariance analysis, and goodness-of-fit tests are used to evaluate model accuracy. SEM is widely used in psychology, sociology, and other fields, and its application in travel behavior research has grown significantly. SEM can handle various types of data, including discrete and ordinal variables, and allows for the modeling of mediating variables and error-term relationships. It is also useful for handling non-normal data and missing values. SEM has been applied to various aspects of travel behavior, including travel demand modeling, dynamic travel demand modeling, activity-based travel demand modeling, and modeling attitudes, perceptions, and hypothetical choices. SEM has been used to analyze the effects of car ownership, travel time, mode choice, and other factors on travel behavior. It has also been used to model the relationship between attitudes and behavior, and to test the validity of stated preference data in relation to revealed preference data. SEM is a powerful tool for analyzing complex travel behavior data and has been widely adopted in transportation research.