Travel Behavior Variables Research Articles

Does travel behavior matter in defining urban form? A quantitative analysis characterizing distinct areas within a region

Research that attempts to characterize urban form is confronted with two key issues: criticism of the use of aggregate units of analysis, such as census tracts, and a general lack of consideration of variables related to elements other than the built environment, such as residents’ behavior. This methodological study explores the impact of travel behavior variables in the quantitative characterization of urban form at the census tract level for the Montreal region. Two separate factor-cluster analyses are performed: the first includes built-environment variables commonly used to typify areas within a region, and a second includes additional travel behavior variables. The results of both models are compared to satellite images to determine which analysis more accurately represents the reality on the ground. The results provide empirical evidence that travel behavior variables, in addition to built form, provide a more accurate representation of urban form at the census tract level. These variables refine the model output by moderating the effect of features that generally led to misleading results. This effect is particularly evident in areas represented by large census tracts. These results suggest that considering both built environment and behavioral characteristics in an analysis of urban form yields more precise results at the (aggregate) census tract level. The findings from this study could be helpful for engineers and planners when conducting property value studies, urban investment analysis, and policy intervention prioritization and when expanding the well-known land use classification of urban and rural categories.

Does travel behavior matter in defining urban form? A quantitative analysis characterizing distinct areas within a region

Research that attempts to characterize urban form is confronted with two key issues: criticism of the use of aggregate units of analysis, such as census tracts, and a general lack of consideration of variables related to elements other than the built environment, such as residents’ behavior. This methodological study explores the impact of travel behavior variables in the quantitative characterization of urban form at the census tract level for the Montreal region. Two separate factor-cluster analyses are performed: the first includes built-environment variables commonly used to typify areas within a region, and a second includes additional travel behavior variables. The results of both models are compared to satellite images to determine which analysis more accurately represents the reality on the ground. The results provide empirical evidence that travel behavior variables, in addition to built form, provide a more accurate representation of urban form at the census tract level. These variables refine the model output by moderating the effect of features that generally led to misleading results. This effect is particularly evident in areas represented by large census tracts. These results suggest that considering both built environment and behavioral characteristics in an analysis of urban form yields more precise results at the (aggregate) census tract level. The findings from this study could be helpful for engineers and planners when conducting property value studies, urban investment analysis, and policy intervention prioritization and when expanding the well-known land use classification of urban and rural categories.

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.

Travel by University Students in Virginia

To improve regional travel demand models, transportation engineers and planners want to represent subpopulations appropriately. A key segment of the population is university students, and their behavior is neither well understood nor well represented in travel demand models. Furthermore, universities provide a unique context for behavioral research because they are livable, are friendly to alternative travel modes, have a higher density than other contexts, and offer mixed travel modes. This study collected and analyzed data on the travel behavior of university students. With the use of an Internet-based survey instrument, the study collected data on travel behavior, sociodemographics, and context variables at four major universities in Virginia. This paper provides information about the design and implementation of the survey, the instrument structure, and a descriptive analysis of students’ personal and travel characteristics. The results indicated that the sociodemographics and travel behavior of university students were different from those of the general population. Moreover, differences in travel behavior were found between students living on campus and students living off campus and between students attending urban campuses and those attending suburban campuses. The insights gained from this study serve as a basis for further such surveys and help provide an understanding of travel behavior in and around university campuses.

An Examination of Purchase Decision-Making Criteria: A Case of Turkey as a Destination

Purchase decision-making is an important area of research in tourism. This research has been carried out to gain insights into how demographic factors and preferences affect vacation decision criteria. The study utilized a set of data from a match sample of 897 visitors that included domestic visitors from Turkey and visitors from Japan, Korea, the United Kingdom, Sweden, Belgium, Poland, Russia, France, and Germany. Specifically, the study aimed to explore the relationship between purchase decision criteria and sociodemographic characteristics and travel behavior characteristics. Specifically, demographic variables included age, gender, marital status, education level, occupation, and annual income; while travel behavior variables included tourist expenses, lodging package type, and reason for lodging package preference. The results indicated that demographic and select travel behavior characteristics affected the purchase decision in varying degrees. Understanding the complexity of the relationship between decision making and its possible determinants would be great help for destination promoters and marketing managers. The study concluded with theoretical and practical implications of the study findings and made appropriate suggestions for implications.

Structural Equations Model of Land Use Patterns, Location Choice, and Travel Behavior

This paper addresses the relationship between travel behavior and land use patterns through the use of a structural equations modeling framework and a more comprehensive modeling than in the past. The proposed model structure is by design heavily influenced by a model developed for Lisbon, Portugal, to allow comparisons. In a related paper about that model, the existence of significant effects of land use patterns in travel behavior was found. The travel behavior variables included in the proposed model are multidimensional and comprehend both the short term (number of trips and trip scheduling) and the long term (home location, car and pass ownership, and mobility decisions). The modeled land use variables measure the levels of urban intensity, density, and diversity, both in relation to types of uses and the mix between jobs and inhabitants and the public transport supply levels. The land use patterns are described at both the residence and the employment zones. To account for self-selection bias, the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households. The Seattle, Washington, findings are presented and then compared with the Lisbon findings. Many commonalities between the two environments were found as well as many important differences pointing to the need for policy initiatives that are local and tailored to the specific context.

Exploring Destination Satisfaction: A Case of Kizkalesi, Turkey

This study sought to determine if there were any significant differences on overall destination satisfaction in Turkish tourists with different demographic and travel behavior characteristics. In addition, we analyzed differences in the destination attributes in relation to tourists' demographic and travel behavior characteristics. Study data were collected from 233 Turkish tourists in Kizkalesi, Turkey. We performed factor analysis to reduce the number of destination attributes and to obtain correlated variable composites from the original destination attributes. ANOVA was used to determine significant differences in the overall satisfaction of tourists by demographics and travel behavior characteristics. ANOVA was also used to determine whether individuals' responses to the general destination attributes differed in relation to sociodemographic and travel behavior variables. Results indicated that a significant relationship does exist between overall destination satisfaction and travelers' sociodemographic and trip variables. In addition, we found a significant relationship between specific destination attributes and travelers' demographic and trip variables.

Travel behavior of special population groups

This annual special issue of "Transportation: Planning, Policy, Research, Practice" comprises selected papers presented at the Annual Meeting of the Transportation Research Board (TRB) in January 2008. These papers all focus on segments of the population that face particular mobility challenges. The papers develop and extend hypotheses regarding the determinants of variability in travel behavior of these special groups. The highlighted groups include university workers, immigrants, senior citizens, baby boomers, and children. It is hoped that the findings presented in this issue will increase understanding and stimulate future research in the area of travel behavior of special population groups.

Assessment of factors influencing destination image using a multiple regression model

Destination image researchers have investigated and found that several factors have influences on destination image. Some of these factors are related to perceivers (e.g. sociodemographics and behavior); some are related to the destination of concern (e.g. promotional materials); some are independent of both perceivers and the destination (e.g. news coverage and movies); and some are methodological choices made by researchers while measuring destination image (e.g. qualitative methods vs. quantitative methods). However, there has been a lack of studies measuring the relative magnitude of the influence posed by different factors. The purpose of this study is to investigate the relative impact of factors influencing destination image, by analyzing the large scale and longitudinal dataset of the Michigan Regional Travel Market Survey, applying multiple regression analysis. The nature of this secondary dataset allowed the inclusion of a set of sociodemographic and travel behavior variables as well as one variable related to the survey methodology. Only few of those selected variables (i.e. age, race and visitation) were found to be robust. Implications and future research suggestions are provided.

Shopping Travel Behaviors in an Era of Rapid Economic Transition: Evidence from Newly Built Malls in Prague, Czech Republic

The rapid transformation in both the location and type of retail provision in the Prague metropolitan area, Czech Republic, between 1997 and 2001 offers a unique opportunity for examining the relationship between land use and shopping travel behavior. The case of Prague is unique. Although the Czech capital has the compact form and transit-rich environment sought by many planners, after rates of motorization rose steadily in the early 1990s foreign developers began constructing shopping malls on the city's fringe in 1997. Although retail provision was being remade on highway-adjacent locations outside the traditional central business district, there were few changes in Prague residential and employment locations. Thus, the shift in shopping land use occurred in relative isolation. Shopping travel behavior before and after the introduction of the fringe shopping malls is examined by using research based on 1,649 surveys collected in four new malls in 2001. Mall visitors reported current demographic information and mall travel behavior as well as their travel habits for the same trip purposes before the introduction of the surveyed mall. The before-and-after data demonstrate how shopping travel behavior has changed with the changing retail land uses. Specifically, shifts in three travel behavior variables (trip frequency, shopping duration, and mode split) disaggregated by key demographic characteristics (age, gender, income, car ownership, and household size), are analyzed. Several striking shifts are apparent: people shop less often and for longer periods of time and are less likely to walk to shopping locations.

International Students’ Image of Rural Pennsylvania as a Travel Destination

This exploratory examination of the image international students have of rural tourism areas in Pennsylvania documents that they have distinct images and that their responses to them differ depending on travel behavior and sociodemographic variables.

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled.

The dynamics of household travel time expenditures and car ownership decisions

A dynamic (panel data) structural equations model is developed that links four dependent travel behavior variables at two points in time, one year apart. The four dependent variables are: car ownership, travel time per week by car, travel time by public transit, and travel time by nonmotorized modes. Exogenous variables include 13 household characteristics and variables accounting for period effects over the 1985 to 1987 time frame in the Netherlands. The model treats car ownership as ordered-response probit variables and all travel times as censored (tobit) continuous variables. The model accounts for serially-correlated errors and panel conditioning biases. Results are interpreted in terms of recommendations for forecasting procedures.

The significance and measurement of variability in travel behaviour

It is argued that an understanding of variability is central to the modelling of travel behaviour and the assessment of policy impacts, and is not the peripheral issue that it has often been considered. Drawing on recent studies in the UK and Australia, in conjunction with a review of the literature, the paper first examines the policy and analytical rationale for using multi-day data, then illustrates different ways of measuring variability, and finally discusses issues relating to the collection of suitable data for such analyses.

Intrapersonal variability and model goodness-of-fit

This paper investigates the effect of day-to-day variability in individuals' travel behavior on the goodness-of-fit of travel demand models estimated with conventional cross-sectional data sets. In particular, this paper examines the effect of day-to-day variability on the goodness-of-fit of least squares regression models of person trip generation. The analytic results show that the conventional R-square goodness-of-fit measure for least squares regression models estimated with cross-sectional data is dependent upon two factors. The first factor is the proportion of the between-person variability that is accounted for by the explanatory variables in the model. The second factor is the proportion of the total variability in the dependent variable that is due to between-person variability. One week activity diary data are used to estimate the relative magnitude of the intrapersonal and interpersonal variability components for a variety of measures of trip-making. This analysis shows that intrapersonal variability may comprise a substantial proportion of the total variability, although the relative magnitude of the intrapersonal variability component varies with trip purpose and across population segments. Generally, however, intrapersonal variability is found to have a considerable effect on the apparent goodness-of-fit of person level trip generation models estimated with cross-sectional data. The paper also illustrates that recognizing the effect of intrapersonal variability on model goodness-of-fit may be useful in interpreting and evaluating model estimation and model transferability results.

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

A Local Study of Consumer Vacation Travel Decisions

A travel agent can only fit his services to his customer's requirements when he knows their travel needs, preferences, limitations, and habits. This article describes a study conducted on Lancaster County, Nebraska, residents to determine general vacation patterns, the vacation decision process, rural versus urban decision differ ences, and the statistical relationship between consumer vacation travel behavior and socio-economic variables. This information will aid the local travel agent in designing his marketing programs.