An Analysis of the Intraurban Trip Distance Using the Time Geography Framework; Influenced by Individual Constraints or Spatial Opportunities

Trip distance, as a key variable of travel behavior, represents the level of sustainable transportation, quality of life, individuals’ accessibility to spatial opportunities, and spatial balance among urban areas. Research

Despite numerous research relating to travel behavior, few scant studies have investigated the role of different factors in explaining trip distance for non-work purposes. So, the main purpose of this paper is to investigate the role of both individual and physical-spatial factors (at trip origin and destination) on trip distance using the theoretical framework of time geography. Research

The research method is descriptive-analytic based on logical reasoning and empirical observations. In this study, based on the g time geography framework, 9 factors at the individual level and 9 factors at the scale of the neighborhood are categorized into three sets of constraints including capacity constraints, coupling constraints, and authority constraints. In order to test the theoretical framework, 30 study districts in the metropolitan of Isfahan, Iran are selected and required data were collected using 1312 questionnaires. For analysis of the abovementioned factors, the potential impacts of the factors have been firstly explained and then, using the collected data and the linear regression technique, the expected relationships have been experimentally tested.

The results and the findings of the research show that the variables related to all three types of constraints affect trip distance; and the role of physical-spatial characteristics in explaining trip distance is stronger than individual factors. Distance from the city center is the most important factor affecting trip distance. Also, although it was theoretically expected that some factors such as gender, household size, commercial density, and land use diversity affect travel length, they did not significantly appear in the empirical analysis model.