In this study data were obtained from the household survey in the historic district of Yangzhou. A structural equation Bcl-2 protein family model (SEM) was developed to explore the relationships among commute trip-activity characteristics,

travel behavior, and individual and household attributes. A classification was done according to commuters’ working location, which is convenient for a further comparison of their respective influencing factors. The remainder of this paper is organized as follows. Section 2 presents the data source used in the research and exogenous and endogenous variables. Section 3 is mainly about descriptive statistics of the data, using the statistical analysis methods to discuss travel characteristics

of those two groups. Section 4 presents the methodology of structural equation model and the modeling framework. Then, in Section 5, we discuss the model estimation results, and in the final section, conclusions are summarized and discussed. 2. Methodology The general SEM assumes that causal relationships exist among a set of latent variables, which are specified as linear combinations of manifest variables. Through the validation of the covariance among the manifest variables, the coefficients of linear regression model can be estimated to confirm whether the assumed model is suitable for analysis. If the result is fit, the assumed relationships among the latent variables

are reasonable. There are some steps involved in SEM construction. They are as follows: establish the conceptual model, compose a path diagram, specify the variables, select the input matrix model, evaluate the sample size and its effects, and identify the methods for model (such as the approach for estimation, evaluation, and modification), as well as cross-validity. The SEM is composed of measurement equations and structural equations. Theoretically, a standard SEM has three equations and it could be expressed as below: η=Βη+Γξ+ζ, (1) y=Λyη+ε, (2) x=Λx+δ, (3) whereη is vector of latent endogenous variables;Β is the coefficient matrix of direct effects between endogenous latent variables; Γ is the matrix of regression effects for exogenous latent variables to endogenous latent variables; ξ is vector of latent exogenous variables; ζ is error vector of structural equation; y is vector of observed endogenous Cilengitide variables; Λy is the matrix of structural coefficients for latent endogenous variables to their observed indicator variables; ε is vector of measurement error terms for observed variables y; x is vector of observed exogenous variables; Λx is the matrix of structural coefficients for latent exogenous variables to their observed indicator variables; δ is vector of measurement error terms for observed variables x.