Studying the relationship between activity participation,social networks,expenditures and travel behavior on leisure activities

Transportation - In the context of an increasing interest in understanding travel for non-mandatory activities, such as recreation and socializing, this work focuses on studying the relationships...     

Involvement in clubs or voluntary associations, social networks and activity generation: a path analysis

Leisure activities have received increasing attention from travel behavior researchers over the past decade. However, these activities are often treated as a single category, neglecting their differences. Whereas most leisure activities are flexible, club activities are usually scheduled longer in advance and are more fixed in time, location and company. Hence, trip-generating properties of club activities are likely to differ from those of other leisure activities. As very little is known about involvement in clubs or voluntary associations in relation to trip generation, voluntary association activities deserve further research in relation to travel. Therefore, in this paper a path analysis is conducted, analyzing the relationships between participation in clubs or voluntary associations, trip frequencies, and social network characteristics. The analyses are based on data collected in 2011 in Eindhoven in the Netherlands in a survey among 516 respondents. The results show interesting relationships between the social context and involvement in clubs. They indicate that people become club members through their social networks, and frequent club activities increase social network size. Family oriented people were found to go less often to clubs. Club membership and the frequency of going to club activities were also found to be affected by socio-demographics, such as gender, age, education, work, presence of young children in the household and owning a season ticket for public transport.     

Modeling household interactions in daily in-home and out-of-home maintenance activity participation

The activity travel patterns of individuals in a household are inter-related, and the realistic modeling of activity-travel behavior requires that these interdependencies be explicitly accommodated. This paper examines household interactions impacting weekday in-home and out-of-home maintenance activity generation in active, nuclear family, households. The in-home maintenance activity generation is modeled by examining the duration invested by the male and female household heads in household chores using a seemingly unrelated regression modeling system. The out-of-home maintenance activity generation is modeled in terms of the decision of the household to undertake shopping, allocation of the task to one or both household heads, and the duration of shopping for the person(s) allocated the responsibility. A joint mixed-logit hazard-duration model structure is developed and applied to the modeling of out-of-home maintenance activity generation. The results indicate that traditional gender roles continue to exist and, in particular, non-working women are more likely to share a large burden of the household maintenance tasks. The model for out-of-home maintenance activity generation indicates that joint activity participation in the case of shopping is motivated by resource (automobiles) constraints. Finally, women who have a higher propensity to shop are also found to be inherently more efficient shoppers.     

Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year

Using multi-day, multi-period travel diaries data of 56 days (four waves of two-week diaries) for 67 individuals in Stockholm, this study aims to examine the effects of out-of-home and in-home constraints (e.g. teleworking, studying at home, doing the laundry, cleaning and taking care of other household member[s]) on individuals’ day-to-day leisure activity participation decisions in four different seasons. This study also aims to explore the effects of various types of working schedules (fixed, shift, partial- and full-flexible) on individuals’ decisions to participate in day-to-day leisure activities. A pooled model (56 days) and wave-specific models (14 days in each wave) are estimated by using dynamic ordered Probit models. The effects of various types of working schedules are estimated by using 28 days of two waves’ data. The results show that an individual’s leisure activity participation decision is significantly influenced by out-of-home work durations but not influenced by in-home constraints, regardless of any seasons. Individuals with shift working hours engage less in day-to-day leisure activities than other workers’ types in both spring and summer seasons. The thermal indicator significantly affects individuals’ leisure activity participation decisions during the autumn season. Individuals exhibit routine behaviour characterized by repeated decisions in participating in day-to-day leisure activities that can last up to 14 days, regardless of any seasons.     

Egocentric social networks and social interactions in the Greater Tokyo Area

Transportation - This article presents the results of a survey on egocentric social networks in the Greater Tokyo Area. This is, together with our preliminary study, the first study on egocentric...     

Travel and the social environment: Evidence from Alameda County,California

The relationship between travel and the environment has been the subject of much study but the focus has mainly been on the physical and built environment. This ignores a large body of research in sociology showing that social processes are spatially embedded and affect individual behavior. This analysis asks whether the neighborhood social environment – in addition to the built environment – influences children’s decision to walk to school in Alameda County, California. The results show that social factors, particularly neighborhood cohesion, do influence the decision to walk particularly when children face trips of less than 1.6 km. These findings provide initial evidence for transportation analysts to broaden their definition of the environment to include social factors.     

The social dimension in action: A multilevel,personal networks model of social activity frequency between individuals

This paper presents a social activity-travel generation model, which explicitly incorporates the individual’s social dimension through the concept of personal networks, modeling the multilevel structure of social relations defined by these networks. The objective of the analysis is to study the relevance of the social dimension as a source of explanation of social activity-travel generation behavior between an individual and each relevant person of their social life. The paper uses a disaggregated perspective of personal networks, explicitly incorporating the characteristics of each network member as well as the characteristics of the overall social structure. Using an ordinal multilevel specification that accounts for the social network in which individuals are embedded, four dimensions are studied: personal characteristics, “with whom” activities are performed, social network composition and structure, and ICT (information and communication technology) interaction. The results show that a proper and complete understanding of social activity generation requires going beyond the individualistic paradigm, explicitly incorporating the role of the social dimension in the study of this decision-making process.     

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

Representing and estimating interactions between activities in a need-based model of activity generation

Although several activity-based models made the transition to practice in recent years, modeling dynamic activity generation and especially, the mechanisms underlying activity generation are not well incorporated in the current activity-based models. For instance, current models assume that activities are independent, but to the extent that different activities fulfill the same underlying needs and act as partial substitutes, their interactions/dependencies should be taken into account. For example, recreational, leisure, and social activities tend to be partly substitutable since they satisfy a common need of relaxation, and when undertaken together with others, social needs will be satisfied as well. This paper describes the parameter estimation of a need-based activity generation model, which includes the representation of possible interaction effects between activities. A survey was carried out to collect activity data for a typical week and a specific day among a sample of individuals. The diary data contain detailed information on activity history and future planning. Estimation of the model involves a range of shopping, social, leisure, and sports activities, as dependent variables, and socioeconomic, day preference, and interaction variables, as explanatory variables. The results show that several person, household, and dwelling attributes influence activity-episode timing decisions in a longitudinal time frame and, thus, the frequency and day choice of conducting the social, leisure, and sports activities. Furthermore, interactions were found in the sense that several activities influence the need for other activities and some activities affect the utility of conducting another activity on the same day.     

Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends

This paper formulates a model for the allocation of total weekly discretionary time of individuals between in-home and out- of-home locations and between weekdays and the weekend. The model formulation takes the form of a continuous utility-maximizing resource allocation problem. The formulation is applied to an empirical analysis using data drawn from a 1985 time-use survey conducted in the Netherlands. This survey gathered time-use information from individuals over a period of one week and also collected detailed household-personal socio-demographic data. The empirical analysis uses household socio-demographics, individual socio-demographics, and work-related characteristics as the explanatory variables. Among the explanatory variables, age of the individual and work duration during the weekdays appear to be the most important determinants of discretionary time allocation.     

Work and home location: Possible role of social networks

This research explores to what extent people’s work locations are similar to that of those who live around them. Using the Longitudinal Employer-Household Dynamics data set and the 2000 decennial census, we investigate the home and work locations of different census block residents in the Twin Cities (Minneapolis-St. Paul) metropolitan area. Our aim is to investigate if people who share a residence neighborhood also share work locations to a degree beyond what would be explained by distanhe observed patterns is the role neighborhood level and work place social networks play in locating jobs and residences respectively.     

Social networks,social influence and activity-travel behaviour: a review of models and empirical evidence

ABSTRACT

The study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research.     

Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

     

Decision makers and socializers, social networks and the role of individuals as participants

Models explaining and predicting human travel behavior have gone through many changes in the past few decades. As researchers attempt to explain more and predict with more accuracy, the inclusion of social interactions in modeling and simulation is being recognized as a necessity. Among these efforts, researchers have focused on issues such as the composition of social networks, and the constraints and influences that others have on spatial decisions. An important aspect that has been understudied however is the variability or heterogeneity of individuals both as social network members and as participants in these social networks. Understanding the role individuals play in decision-making in different social networks can further define our models to include more accurate representations of human behavior. This research explores the differences between social network composition, and the decision roles members play within different social networks specifically when deciding where to participate in activities. A survey was conducted in Santa Barbara, California on social network involvement, network attributes and decision-making roles within each network. Two separate latent class cluster analysis models were developed to classify social network involvement and roles. Results show that there are clearly different types of social involvement and roles within networks. Further data collection and analysis will be used to better understand how these decision-making roles manifest themselves in activity decision-making.     

A conceptual and methodological framework of leisure activity loyalty accommodating the travel context

As leisure travel continues to grow, it has become a critical subject for planners and decision-makers since it significantly impacts regional economic and social development as well as contributes to emission levels and congestion. Despite being a significant percentage of our travel, however, leisure travel behavior is still not very well understood. The goal of this article is to contribute to our understanding of leisure activity participation by considering leisure activity loyalty within the travel context. In particular, this study focuses on one specific dimension of travel context: travel extent (i.e., whether an individual participates in a leisure activity on a daily versus a long-distance basis). As such, this article first introduces a unified conceptual framework for measuring leisure activity loyalties within a travel context, based on two distinct dynamics of leisure loyalty behavior—destination attachment and activity involvement. Additionally, this article uses a unique 2001 NHTS dataset comprised of households’ daily and long-distance leisure activities to undertake a unique empirical analysis of five distinct leisure activities using the conceptual framework and a copula-based model methodology. The findings confirmed that households demonstrate significant loyalties to travel contexts across all leisure activities, especially resting and sightseeing.     

Exploring the links between personal networks, time use, and the spatial distribution of social contacts

Although the study of the role of the social context in travel behavior and activity patterns has recently gained attention, the empirical evidence supporting the relationship between social networks and the temporal and spatial characteristics of social activities is still limited. With this motivation, this paper studies the link between “longer term” (social networks) and “shorter term” (social activities) social decisions, by exploring the intertwined relationship between the individuals’ personal networks attributes, and the spatiotemporal characteristics of their daily social activities. The paper contributes to the literature by adding two key aspects to the study of the role of social networks on travel behavior: the social networks’ structure, and the spatiality of all individuals participating on the social activities. Based on data which link people’s personal networks and time use, and using a structural equation modeling approach, the paper studies the influence of individual and interactional attributes on the duration, distance, and number of people involved in social daily activities. The results show that aspects such as tie social closeness, gender and age similarity, and network density, help to understand social activity duration and distance, complementing traditional socio-demographic aspects such as income, occupation, and accessibility to services. In this way, socio-demographic attributes are not enough to explain the spatiotemporal dimension of daily activities which makes necessary to include variables related to the social context to explain with a higher level of accuracy both the duration and distance traveled to the activity.     

A coupled multi-agent microsimulation of social interactions and transportation behavior

Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior.     

Substitution between working at home and out-of-home: The role of ICT and commuting costs

This paper analyzes the trade-off between working at home and out-of-home, ICT and commuting time. To this end, we develop a microeconomic demand system, which explicitly incorporates both time and income constraints. Commuting time is considered as the price to be paid for working out-of-home and a decrease in earnings as the price for working at home. For the latter, we find that working at home leads to a (marginally significant) reduction of the wage rate of about 19%, but this gap largely disappears when ICT is used for at home work. To examine the relation between out-of-home and at home work empirically, we estimate a translog indirect utility function, from which we are able to estimate substitution and price elasticities between working at home and out-of-home for The Netherlands. The results show that changes in ICT and commuting time display rather weak substitution effects on working out-of-home and at home, respectively. Moreover, individual characteristics – especially age and education – seem to be more important for the choice between working at home and out-of-home than ICT availability or commuting time.     

An activity-episode generation model that captures interactions between household heads: development and empirical analysis

In this paper, we develop an approach for modeling the daily number of non-work, out-of-home activity episodes for household heads that incorporates in its framework both interactions between such members and activity setting (i.e. independent and joint activities). Trivariate ordered probit models are estimated for the heads of three household types – couple, non-worker; couple, one-worker; and couple, two-worker households – using data from a trip diary survey that was conducted in the Greater Toronto Area (GTA) during 1987. Significant interactions between household heads are found. Moreover, the nature of these interactions is shown to vary by household type implying that decision-making structures and, more generally, household dynamics also vary by household type. In terms of predictive ability, the models incorporating interactions are found to predict more accurately than models excluding interactions. The empirical findings emphasize the importance of incorporating interactions between household members in activity-based forecasting models.