As a consequence, the analysis on travel characteristics selleck chemicals llc of the region’s residents, especially commuters, is important for the alleviation of the traffic jams. Besides, it is particularly meaningful for policy makers to develop effective traffic strategies. The commute trip, which is known as a spatial movement from home to working place, often accounts for a great proportion in

commuters’ daily trips (nearly 50%). Thus, the solution of commuters’ travel problem would be very helpful for the soothing of traffic congestions on roads. There are a considerable number of studies on the characteristics and influencing factors of commuting travel activities, but most cities they researched do not have historic sites. In China, the historic district usually

has a high population, a mixed land use pattern, and a different density of road network, and all these are quite different from those of the entire city. Besides, these factors are confirmed to be of particular importance to characteristics of commuters travel. Therefore, it is very necessary to investigate the relationships between commuters’ characteristics, activities, and travel behavior. Activity-based approach on travel behavior usually focuses on activity and decision-making, analyzing, and modeling relationships between travel behavior and activity [1–6]. In order to elaborate on the study of travel activity patens and influencing factors, these activities should be categorized at first. Some scholars suggested that they could be divided into three parts based on travel purpose: subsistence activity, involuntary activity, and voluntary activity [7]. Meanwhile, further studies proposed a more efficient classification approach, which distinguished them into four categories: subsistence activity, maintenance activity, discretionary activity,

and others [8–11]. This research adopted the latter one. Moreover, considering the activity characteristic of commuters, we distinguished them into subsistence activity and nonsubsistence activity. According to previous studies, individual and household decision-making are dominant influencing factors on commuters travel behavior [12–17]. An insight on the mechanism Brefeldin_A of commuters travel behavior, individual and household, is very meaningful for scholars to understand commuters’ travel. A lot of research has been done on the issue, and many models have been proposed (such as calculation process model, the discrete choice model, etc.). But few models can truly explain the complex relationships among them. Structural equation modeling (SEM) is a popular statistic approach in 1960s. It can test and estimate causal relations with a combination of causal assumptions. Unlike the traditional models, SEM can model two types of variables: observed variables that are directly collected or measured and latent variables that are not directly observed or measured.