The Impacts of Ict on leisure Activities and Travel: A Conceptual Exploration

This paper offers a conceptual exploration of the potential impacts of ICTs on leisure activities and the associated travel. We start by discussing what leisure is and is not. We point out that the boundaries between leisure, mandatory, and maintenance activities are permeable, for three reasons: the multi-attribute nature of a single activity, the sequential interleaving of activity fragments, and the simultaneous conduct of multiple activities (multitasking). We then discuss four kinds of ways by which ICT can affect leisure activities and travel: the replacement of a traditional activity with an ICT counterpart, the generation of new ICT activities (that may displace other activities), the ICT-enabled reallocation of time to other activities, and ICT as a facilitator of leisure activities. We suggest 13 dimensions of leisure activities that are especially relevant to the issue of ICT impacts: location (in)dependence, mobility-based versus stationary, time (in)dependence, planning horizon, temporal structure and fragmentation, possible multitasking, solitary versus social activity, active versus passive participation, physical versus mental, equipment/media (in)dependence, informal versus formal arrangements required, motivation, and cost. The primary impact of ICT on leisure is to expand an individual’s choice set; however whether or not the new options will be chosen depends on the attributes of the activity (such as the 13 identified dimensions), as well as those of the individual. The potential transportation impacts when the new options are chosen are ambiguous.     

Exploring the propensity to perform social activities: a social network approach

Conceptual and empirical models of the propensity to perform social activity–travel behavior are described, which incorporate the influence of individuals’ social context, namely their social networks. More explicitly, the conceptual model develops the concepts of egocentric social networks, social activities, and social episodes, and defines the three sets of aspects that influence the propensity to perform social activities: individuals’ personal attributes, social network composition, and information and communication technology interaction with social network members. Using the structural equation modeling (SEM) technique and data recently collected in Toronto, the empirical model tests the effect of these three aspects on the propensity to perform social activities. Results suggest that the social networks framework provides useful insights into the role of physical space, social activity types, communication and information technology use, and the importance of “with whom” the activity was performed with. Overall, explicitly incorporating social networks into the activity–travel behavior modeling framework provides a promising framework to understand social activities and key aspects of the underlying behavioral process. Juan Antonio Carrasco a PhD candidate in Civil Engineering at the University of Toronto, holds a MSc degree in Transportation Engineering from the Pontificia Universidad Católica de Chile. His doctoral research explores the relationships between social networks, activity–travel behavior, and ICTs. His research interests also include microsimulation, land use-transportation, and econometric modeling. Eric J. Miller is Bahen-Tanenbaum Professor of Civil Engineering at the University of Toronto where he is also Director of the Joint Program in Transportation. His research interests include integrated land-use/transportation modeling, activity-based travel modeling, microsimulation and sustainable transportation planning.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

Measuring the impact of efficient household travel decisions on potential travel time savings and accessibility gains

Using the conceptual framework of time–space geography, this paper incorporates both spatio-temporal constraints and household interaction effects into a meaningful measure of the potential of a household to interact with the built environment. Within this context, personal accessibility is described as a measure of the potential ability of individuals within a household not only to reach activity opportunities, but to do so with sufficient time available for participation in those activities, subject to the spatio-temporal constraints imposed by their daily obligations and transportation supply environment. The incorporation of activity-based concepts in the measurement of accessibility as a product of travel time savings not only explicitly acknowledges a temporal dimension in assessing the potential for spatial interaction but also expands the applicability of accessibility consideration to such real-world policy options as the promotion of ride-sharing and trip chaining behaviors. An empirical application of the model system provides an indication of the potential of activity-based modeling approaches to assess the bounds on achievable improvements in accessibility and travel time based on daily household activity patterns. It also provides an assessment of roles for trip chaining and ride-sharing as potentially effective methods to facilitate transportation policy objectives.     

GHG emissions in daily travel and long-distance travel in Germany – Social and spatial correlates

There is considerable research on the climate effects of daily travel, including research on the spatio-temporal and socioeconomic impact factors of daily travel and associated climate change effects. However, this is less true with respect to long-distance trips. This paper uses national transport survey data from Germany to point out differences in GHG emissions related to demographic, socioeconomic and spatial characteristics for daily and long-distance travel. Daily travel and long-distance travel are investigated simultaneously and separately using Logit and OLS regressions. The results show that transport-related GHG emissions from long-distance trips and daily trips are affected by sociodemographics in largely the same direction. In contrast, spatial attributes, like municipality size or density grade of the region, show a different picture. Per capita emissions in rural and suburban areas are higher for daily trips, but lower for long-distance trips than emissions caused by urban residents. While we cannot rule out the possibility of residential self-selection, our findings challenge the idea that compact urban development may help reduce CO₂ emissions once long-distance trips are taken into account.     

Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset

Internet is capturing more and more of our time each day, and the increasing levels of engagement are mainly due to the use of social media. Time spent on social media is observed in the American Time Use Survey and recorded as leisure time on Personal Computer (PC). In this paper, we extend the traditional analysis of leisure activity participation by including leisure activities that require the use of a PC. We study the substitution effects with both in-home and out-of-home leisure activities and the time budget allocated to each of them. The modeling framework that includes both discrete alternatives and continuous decision variables allow for full correlation across the utility of the alternatives that are all of leisure type and the regressions that model the time allocated to each activity. Results show that there is little substitution effect between leisure with PC and the relative time spent on it, with in-home and out-of-home leisure episodes. Households with more children and full-time workers are more likely to engage in in-home and PC related leisure activities (especially during weekends). Increments in the travel time of social trips result in significant reductions in leisure time during weekdays.     

A conceptual framework of individual activity program generation

The research in this paper attempts to better understand the process by which activities are generated at an individual level. Activity-based travel analyses have gained popularity in recent years because they recognize the complexity of activity behavior and view travel as a derivative of this behavior. Most activity-based studies have focused on the spatial and temporal linkage of trips; that is, the scheduling of activities. They consider the agenda of activities for participation, and associated attributes of the activity participation (such as mode to activity and location of activity performance), as predetermined. This paper develops a comprehensive conceptual framework of the relatively unexplored area of activity agenda generation. Such a framework will be valuable in empirical modeling of activity generation behavior. A subsequent paper focuses on translating a part of this conceptual framework into an empirical model.     

Investigating the temporal changes in the relationships between time spent on the internet and non-mandatory activity-travel time use

The amount of time we spend online has been increasing dramatically, influencing our daily travel and activity patterns. However, empirical studies on changes in the extent to which the amount of time spent online are related to changes in our activity and travel patterns are scarce, mainly due to a lack of available longitudinal or quasi-longitudinal data. This paper explores how the relationships between the time spent using the Internet, and the time spent on non-mandatory maintenance and leisure activities, have evolved over a decade. Maintenance activities include out-of-home activities such as shopping, banking, and doctor visits, while leisure activities include entertainment activities, visiting friends, sporting activities, and so forth. Our approach uses two datasets from two major cross-sectional surveys in Scotland, i.e. the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and formed. The multiple discrete–continuous extreme value (MDCEV) model and difference-in-differences (DD) estimation are applied and integrated to examine how the relationships between the time spent on the Internet and travel have changed over time and the direction and magnitude of the changes. Our findings suggest that the complementary associations between Internet use and individuals’ non-mandatory activity-travel time use are diminishing over time, whereas their substitutive associations are increasing. We additionally find that such temporal changes are significant in the case of those who spent moderate to high levels of time on the Internet (5 h or more online) per week.

     

Synthesis of first practices and operational research approaches in activity-based travel demand modeling

Regional travel models in the United States are clearly evolving from conventional models towards a new generation of more behaviorally realistic activity-based models. The new generation of regional travel demand models is characterized by three features: (1) an activity-based platform, that implies that modeled travel be derived within a general framework of the daily activities undertaken by households and persons, (2) a tour-based structure of travel where the tour is used as the basic unit of modeling travel instead of the elemental trip, and (3) micro-simulation modeling techniques that are applied at the fully-disaggregate level of persons and households, which convert activity and travel related choices from fractional-probability model outcomes into a series of discrete or “crisp” decisions.While the new generation of model has obvious conceptual advantages over the conventional four-step models, there are still numerous technical issues that have to be addressed as well as a better understanding of practical benefits should be achieved before the new generation of models can fully replace conventional models. The paper summarizes the recent successful experience in the development and application of activity-based demand models for Metropolitan Planning Organizations in the US. Moving activity-based approaches into practice is analyzed in a broad context of travel demand modeling market tendencies and policy implications.     

Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities

This study presents a unified framework to understand the weekday recreational activity participation time-use of adults, with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis is important for a better understanding of how individuals incorporate physical activity into their daily activities on a typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore, such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling, since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation. The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how much’ individuals choose to participate in ‘what type of (recreational) activity’.     

A time-use activity-pattern recognition model for activity-based travel demand modeling

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).

     

Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour

This paper presents a comprehensive econometric modelling framework for daily activity program generation. It is for day-specific activity program generations of a week-long time span. Activity types considered are 15 generic categories of non-skeletal and flexible activities. Under the daily time budget and non-negativity of participation rate constraints, the models predict optimal sets of frequencies of the activities under consideration (given the average duration of each activity type). The daily time budget considers at-home basic needs and night sleep activities together as a composite activity. The concept of composite activity ensures the dynamics and continuity of time allocation and activity/travel behaviour by encapsulating altogether the activity types that are not of our direct interest in travel demand modelling. Workers’ total working hours (skeletal activity and not a part of the non-skeletal activity time budget) are considered as a variable in the models to accommodate the scheduling effects inside the generation model of non-skeletal activities. Incorporation of previous day’s total executed activities as variables introduces day-to-day dynamics into the activity program generation models. The possibility of zero frequency of any specific activity under consideration is ensured by the Kuhn-Tucker optimality conditions used for formulating the model structure. Models use the concept of random utility maximization approach to derive activity program set. Estimations of the empirical models are done using the 2002–2003 CHASE survey data set collected in Toronto.

Travel time ratio: the key factor of spatial reach

An important aspect of reach and accessibility is the time people are willing to spend on reaching activity places. In this paper we see the issue of travel time in an alternative way. Instead of looking at travel time separated from time spent on activities, we examine the relation between travel time and stay time. We operationalize this relation with the concept “travel time ratio”. A hypothetical framework underlying these travel time ratios is displayed. We show that for similar types of activity places the value of travel time ratio are in accordance with each other. We find large differences between trips for mandatory activities and trips for discretionary activities. The results indicate the stability of the travel time ratios. Finally, some implications for future research and policy will be mentioned. This revised version was published online in June 2006 with corrections to the Cover Date.     

Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier

Travel behavior researchers have been intrigued by the amount of time that people allocate to travel in a day, i.e., the daily travel time expenditure, commonly referred to as a “travel time budget”. Explorations into the notion of a travel time budget have once again resurfaced in the context of activity-based and time use research in travel behavior modeling. This paper revisits the issue by developing the notion of a travel time frontier (TTF) that is distinct from the actual travel time expenditure or budget of an individual. The TTF is defined in this paper as an intrinsic maximum amount of time that people are willing to allocate for travel. It is treated as an unobserved frontier that influences the actual travel time expenditure measured in travel surveys. Using travel survey datasets from around the world (i.e., US, Switzerland and India), this paper sheds new light on daily travel time expenditures by modeling the unobserved TTF and comparing these frontiers across international contexts. The stochastic frontier modeling methodology is employed to model the unobserved TTF as a production frontier. Separate models are estimated for commuter and non-commuter samples to recognize the differing constraints between these market segments. Comparisons across the international contexts show considerable differences in average unobserved TTF values.     

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.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodemographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical result support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

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.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodernographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical results support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

Air carrier demand

Modeling air carrier demand is instrumental to understanding the relative importance of competitive forces that shape the airline environment and determine a carrier's market share. This paper develops a conceptual framework for analyzing carrier demand in a competitive context and applies that framework to study air carrier choice. This framework can be used by carriers to assess the market share and revenue implications of service design, pricing, marketing, and promotional strategies. We adopt an individual traveler choice approach to identify and measure the relative importance of factors which influence air travel demand. Travelers' patterns of air travel, perceptions of carrier service, frequent-flyer program membership, and carrier choice behavior are used to estimate models of individual carrier choice. These models indicate the importance of carrier presence in the origin market, carrier service in a city pair market (share of flights), carrier quality of service reflected in ratings by individual travelers, and traveler loyalty reflected in frequent-flyer program membership on carrier choice. The importance of these variables and the specific quantitative relationship estimated, can be used to estimate the market share impact of service design, pricing, marketing, and promotional changes. The empirical results of this study demonstrate the dramatic impact of frequent-flyer program participation on carrier choice for individual flights. These effects are particularly strong among the most important air carrier market, the frequent business traveler.     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

Simultaneous modeling of individuals’ duration and expenditure decisions in out-of-home leisure activities

In this paper, we propose an activity model under time and budget constraints to simultaneously predict the allocation of time and money to out-of-home leisure activities. The proposed framework considers the activity episode level, given that the activity is scheduled. Thus, the model considers the decision of the quantities for duration and expenditure spent during the activity. We use a flexible utility function and show how the simultaneous equations can be estimated by using structural equations model (SEM) estimation techniques to handle the endogeneity problem of time and expenditure. The estimation results are based on a large national leisure diary data set collected in 2008 in the Netherlands, which provides detailed information about time and money spent as well as timing and location attributes of the activities. The analysis reveals that socio-demographics, travel party, timing and location variables influence the duration and expenditure of activity episodes. It shows that various socio-demographic groups display different preferences in terms of the time and money spent on activities. The results also indicate substitution relationships between spending more time and money for various activity categories. Thus it is concluded that the analysis provides useful results for a better understanding of combined time and money allocation decisions for leisure activities.