A time-use investigation of shopping participation in three Canadian cities: is there evidence of social exclusion?

Increasing awareness and concern about the status of mobility-disadvantaged groups in society has given rise to a wide body of research that focuses on the social exclusion dimension of transportation. To date, much of the empirical work on this topic is mainly spatial in nature despite recent developments that call for the inclusion of time use analyses in social exclusion research. In this paper we attempt to fill this gap by estimating activity and trip durations to determine whether poverty, old age, or being a single parent results in time use patterns indicative of exclusion. Given the importance of shopping and using services for social inclusion objectives, these activities are the focus of this investigation. In terms of methods, use of a multiple equation approach allows for the estimation of the daily duration of shopping activities and trips while simultaneously controlling for daily durations of four broad categories of activities as well as their associated travel times. The results indicate: that being a senior citizen increases travel durations while decreasing shopping activity durations; that coming from a low income household decreases shopping activity durations; and single-parent status does not impact shopping activity durations when holding income and other activity durations constant. These results highlight the feasibility and challenges of time-use and activity analysis in social exclusion research.     

Duration of trip-making activities by men and women

The purpose of this paper is to present the results of a survival analysis for the duration of particular trip-making activities based on sex. Specifically, this study investigates the duration of those activities related to household and family support shopping, personal business, and free time and how these durations vary between men and women. It was found that there were no significant differences in the survival curves (i.e., durations) of free-time or personal business activities; this suggests that men and women spend approximately similar amounts of time on these activities, although it is not known if the activities themselves are similar (for example, banking versus getting gas). Alternatively, sex was found to be a very significant indicator of the duration of household and family support shopping activities. In the model specification, assuming all variables except sex are the same, it was found that women were 1.32 times more likely than men to spend a longer period of time in a household and family support shopping activity. Additionally, it was found that women are 1.33 times more likely than men to have a longer household and family support activity duration if the activity is nested in the journey to work trip.     

Assessment of spatial transferability of an activity-based model,TASHA

Spatial transferability has been recognized as a useful validation test for travel demand models. To date, however, transferability of activity-based models has not been frequently assessed. This paper assesses the spatial transferability of an activity-based model, TASHA (Travel Activity Scheduler for Household Agents), which has been developed for the Greater Toronto Area (GTA), Canada. TASHA has been transferred to the context of the Island of Montreal, Canada using the 2003 Origin–Destination (O–D) travel survey and the 2001 Canadian Census. It generates daily schedules of activities (individual and joint) for each individual in this region. The modelled activity attributes (frequency, start time, duration and distance) from TASHA and observed attributes from the 2003 O–D travel survey are compared for five different activities (i.e. work, school, shopping, other, and return to home). At the aggregate level, TASHA provides quite reasonable outcomes (in some cases – better results than for the Toronto Area) for all four attributes for work, school and return to home activities with few exceptions (for instance, school start time). The model outcomes are also promising for shopping frequency and start times; however, TASHA provides larger differences for average shopping durations and distances. Only the forecasts for all four attributes for the ‘other’ activity type differ greatly with the observed attributes for the Montreal Island. These large differences most likely indicate the differences in behaviour between the Montreal Island and the Toronto Area. In general, we conclude that re-estimation of model parameters and the use of local activity attribute distributions (frequency, start time and duration) is a desirable step in the transfer of the TASHA model from one context to another.     

Characteristics of household activity and travel patterns in the Mumbai metropolitan region

Activity-based modelling approaches require a typical survey instrument which can collect the finer details of activities of each individual over both time and space. This paper focuses on the design of a new survey instrument called an activity-travel diary; examines its method of administration; and analyses activity-travel behaviour in the context of developing countries. The Mumbai Metropolitan Region in India is selected as the study area. With the aim of understanding the activities of each individual over a period of time, a pilot survey was conducted in a continuous time frame for a period of 15 days, followed by a main survey. The analysis of data collected by the instrument reveals some interesting facts regarding the relationships between socioeconomic attributes, activities and trip making behaviour. Identification of interactions among households and other members were also facilitated by the newly designed diary, which is not a well-versed topic for research in the context of a developing economy like Mumbai's.     

Examination of relationships between urban form,household activities,and time allocation in the Atlanta Metropolitan Region

A substantial body of research is focused on understanding the relationships between socio-demographics, land-use characteristics, and mode specific attributes on travel mode choice and time-use patterns. Residential and commercial densities, inter-mixing of land uses, and route directness in conjunction with transportation performance characteristics interact to influence accessibility to destinations as well as time spent traveling and engaging in activities. This study uniquely examines the activity durations undertaken for out-of-home subsistence; maintenance, and discretionary activities. Also examined are total tour durations (summing all activity categories within a tour). Cross-sectional activities are obtained from household activity travel survey data from the Atlanta Metropolitan Region. Time durations allocated to weekdays and weekends are compared. The censoring and endogeneity between activity categories and within individuals are captured using multiple equations Tobit models.The analysis and modeling reveal that land-use characteristics such as net residential density and the number of commercial parcels within a kilometer of a residence are associated with differences in weekday and weekend time-use allocations. Household type and structure are significant predictors across the three activity categories, but not for overall travel times. Tour characteristics such as time-of-day and primary travel mode of the tours also affect traveler’s out-of-home activity-tour time-use patterns.     

Trend analysis of activity generation attributes over time

Activity generation models are relatively poorly developed in activity-based travel demand modelling frameworks. This research investigates whether observed distributions of activity attributes (activity frequency, start time and duration) used as inputs in the activity generation component of an activity-based travel demand model have changed over time. This research empirically examines changes in the distributions of activity generation attributes over time in the Greater Montreal area (GMA), Quebec, Canada. It also focuses on how these attributes vary with peoples’ socio-demographic characteristics. This research relies on the 1998, 2003 and 2008 origin–destination (O–D) household travel surveys of the GMA. The comparative analysis at three time points in a 10-year period clearly reveals that distributions of activity attributes are significantly changing over time. Work and school activities show similar trends; frequency “1” has increased and frequency “2+” has decreased over time. The occurrence of shopping activity on weekdays is decreasing over time. Start time and duration distributions for each activity have also changed significantly over time. The research allows preparing activity attributes for the application of an activity-based model, TASHA, such that they reflect temporal changes in travel behaviour of the GMA.     

A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute

The model developed in this paper generalizes (in the context of multiple exit states from a duration spell) extant competing risk methods which tie the exit state of duration very tightly with the length of duration. In the current formulation, the exit state is modeled explicitly and jointly with duration models for each potential exit state. The model developed here, however, is much broader in its applicability than only to the competing risk situation; it is applicable to multiple durations arising from multiple entrance states, multiple exit states, or a combination of entrance and exit states. Multiple entrance states occur frequently in many situations, but have received little attention in the literature. Explicit consideration of the entrance state is important, even in single or multiple competing risk models in order to accommodate the sample selection in duration based on the no-entry/entry (to the duration spell) outcome. The generalized multiple durations model developed in the paper is applied to an empirical analysis of activity behavior during the return home from work.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.     

An empirical investigation on the dynamic processes of activity scheduling and trip chaining

Investigation of the dynamic processes of activity scheduling and trip chaining has been an interest of transportation researchers over the past decade because of its relevance to the effectiveness of congestion management and intelligent transportation systems. To empirically examine the processes, a computerized survey instrument is developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes the investigation on the dynamic processes of activity scheduling and trip chaining based on data collected from a pilot study of the instrument. With the data, ordered logit models are applied to identify factors that are pertinent to the scheduling horizon of activities. Results of the empirical analysis show that a daily schedule often starts with certain activities occupying a portion of the schedule and other activities are then arranged around these pre-occupants. Activities of shorter duration are more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Persons with children often expect more constraining activities than those with no children. The analysis also shows that female respondents tend to be more structured in terms of how the week is planned. Additionally, analysis of travel patterns reveals that many trip-chains are formed opportunistically. Travel time required to reach an activity is positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

A structural equation model based analysis of non-workers’ activity-travel behaviour from a city of a developing country

This paper presents an empirical analysis of non-workers’ activity-travel behaviour from Bangalore city, India. The paper builds a causal model—to describe the relationships among socio-demographics, activity-participation, and travel behaviour of non-workers—following structural equation modelling methodology. The results indicate that in-home maintenance activity-duration drives the time allocation decisions of non-workers. The model also shows the presence of ‘time-budget’ effects i.e., excess travel time cuts into in-hhome discretionary activity duration, implying the trade-off between daily travel time and in-home discretionary activity duration. The out-of-home activity durations of non-workers are found to be insensitive to travel time—an important finding of this research. The model also suggests that mixed residential development reduce travel distance and indirectly contribute to more trips. An indirect effect of mixed residential development on daily travel distance offsets the direct effect, which leads to a limited total effect of this variable on travel distance. The basic model was expanded further by separating the time spent on others’ activity (children and elders) from in-home maintenance activity duration. The stable model reveals that the time spent on others’ activity also influences in-home and out-of-home activities, and travel behaviour. This indicates that the time spent on others’ activity is an important time allocation of its own.     

An analysis of household vehicle holding durations considering intended holding durations

Household vehicle holding durations are examined in this study using panel data. Panel data enable the observation of changes in household vehicle holdings in discrete time periods. If the data set contains retrospective recall data which offer information on the types and the occurrence time points of the transactions since the last survey, direct observation of the transaction process along a continuous time axis is possible. Furthermore, if the data contain information on intentions for future transactions and actual outcomes of the intentions in later waves, the relationship between intended transactions and actual transactions can be observed. In this study, we develop models of actual vehicle holding durations and models of intended vehicle holding durations for the same set of vehicles. Comparing these two sets of models, the effects on household vehicle holding durations of changes in the household’s plans for vehicle holding and unexpected events can be inferred. To represent unaccounted associations between intended vehicle holding durations and actual vehicle holding durations, vehicle specific error components are introduced into the duration models. A non-parametric approach is adopted in model estimation using mass points, which requires no assumption on the distribution of the error components.     

Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey

According to US Census Bureau, the number of individuals in the age group above 65 years is expected to increase by more than 100% from the year 2000 to 2030. It is anticipated that increasing elderly population will put unforeseen demands on the transportation infrastructure due to the atypical mobility and travel needs of the elderly. Consequently, transportation professionals have attempted to understand the travel behavior of the elderly including the trip frequency, trip distance and mode choice decisions. Majority of the research on elderly travel behavior have focused on the mobility outcomes with limited research into understanding the tradeoffs made by this population segment in terms of their in-home and out-of-home activity engagement choices. The goal of the current research is to contribute to this line of inquiry by simultaneously exploring the daily activity engagement choices of the elderly Americans including their in-home and out-of-home activity participation (what activities to pursue) and time alloocation (duration of each activity) decisions while accounting for the temporal constraints. Further, the study attempts to explore the relationship between physical and subjective well-being and daily activity engagement decisions of the elderly; where subjective well-being is derived from reported needs satisfaction with life and different domains of it. To this end, data from the Disabilities and Use of Time survey of Panel Study of Income Dynamics was used to estimate a panel version of MDCEV model. In addition to person- and household-level demographic variables, activity participation and time use choices of elderly were found to vary across different levels of reported physical and subjective well-being measures. The model estimation results were plausible and provide interesting insights into the activity engagement choices of the elderly with implications for transportation policy development. Among other socio-demographic variables, living arrangements (living with family versus in elderly homes) were found to have significant influence on how people participate into different in-home versus out-of-home activities. For example, elderly living in the elderly home were found to participate more into out-of-home activities compared to people living with families. Elderly with disabilities were found to compensate lower participation into out-of-home activities with more participation into in-home activities. Considerable heterogeneity was observed in time engagement behavior of the elderly across reported levels of satisfaction with finance, job and cognitive needs. For example, elderly expressing high satisfaction with job was found to spend less time in in-home social activities. Elderly reporting higher satisfaction with finance were found to spend more time into OH social and shopping activities.     

Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities

Growing recognition that observed travel patterns are the result of an underlying activity scheduling process has resulted in a new stream of data collection and modeling efforts. Of particular focus is the planning or sequencing of activity scheduling decisions over time that precede actual execution of activities/trips. Understanding and potentially modeling these sequences offers particular promise, as strong interdependencies in activity/travel choices likely exist. In practice, however, a fixed order of sequencing by activity type is often assumed that overlooks the strong interdependencies in activity/travel choices and can be misleading. This study presents the process of developing parametric and non-parametric hazard models to predict the duration of time between planning and execution of pre-planned activities based on attributes of activity and characteristics of decision maker. Modeling results suggest that activity type alone may not suffice to fully explain how activities are planned. Rather, the nature of the activity and several overriding personal and situational factors play an important role. This will make the model more amenable to a variety of people and situations and will make it more sensitive to emerging policy action scenarios.     

On distinguishing between physically active and physically passive episodes and between travel and activity episodes: an analysis of weekend recreational participation in the San Francisco Bay area

This paper examines the out-of-home recreational episode participation of individuals over the weekend, with a specific focus on analyzing the determinants of participation in physically active versus physically passive pursuits and travel versus activity episodes (travel episodes correspond to recreational pursuits without any specific out-of-home location, such as walking, bicycling around the block, and joy-riding in a car, while activity episodes are pursued at a fixed out-of-home location, such as playing soccer at the soccer field and swimming at an aquatics center). The above disaggregation of recreational episodes facilitates the better analysis and modeling of activity-travel attributes, such as travel mode, episode duration, time-of-day of participation and location of participation. From a broader societal standpoint, the disaggregation of recreational episodes provides important information to encourage active participatory recreational pursuits, which can serve to relieve mental stress, improve the physical health of the population, and contribute to a socially vibrant society through increased interactions among individuals.The paper employs a mixed multinomial logit formulation for examining out-of-home recreational episode type participation using the 2000 San Francisco Bay area travel survey. A variety of variables, including individual and household sociodemographics, location attributes, and day of week and seasonal effects, are considered in the model specification. Individual-specific unobserved factors affecting the propensity to participate in different types of recreational episodes are also accommodated.     

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Should we abandon activity type analysis? Redefining activities by their salient attributes

This paper poses a challenge and begins a search. The challenge is to reconsider the usefulness of traditional activity types (“work”, “shopping”, etc.) in the understanding and modelling of travel behaviour. The search is for the more salient attributes of activities that may serve to better explain complex travel behaviours—such as activity scheduling and tour formation. In particular, this paper focuses on explicit measures of the spatial, temporal and interpersonal flexibility of activities, along with several traditional attributes (frequency, duration, involved persons, travel time, and location). Data from a recent in-depth week-long activity scheduling survey was used to define and compare these attributes. Results show that considerable variability in the attributes between and within traditional activity groups is evident. This casts considerable uncertainty on assumptions that statically assign levels of spatial, temporal, and interpersonal flexibility to any given activity type. A Principal Components Analysis further revealed eight new distinct clusters of activities that share like attributes. The relative role of each attribute in each component is examined, and subjective interpretations emerged (e.g., “Long and frequent”, “Space and time flexible” “Social networking”). The implications of these results for future model development and research are discussed. Future research should continue to expand the search for salient attributes and link them more directly to decision processes.     

On the structure of weekly activity/travel patterns

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term “structure” refers to the sequence by which various activities enter one’s daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

The validity of using activity type to structure tour-based scheduling models

A unique set of activity scheduling data is utilized in this paper to provide much needed empirical analysis of the sequence in which activities are planned in everyday life. This is used to assess the validity of the assumption that activities are planned in accordance to a fixed hierarchy of activity types: mandatory activities first (work/school), followed by joint maintenance, joint discretionary, allocated maintenance, and individual discretionary activities. Such an assumption is typical of current generation activity and tour-based travel demand models. However, the empirical results clearly do not support such assumptions. For instance, fewer than 50% of mandatory activities were actually planned first in related out-of-home tours; remaining activity types also did not take any particular precedence in the planning sequence. Given this, a search was made for the more salient attributes of activities (beyond activity type) that would better predict how they are planned within tours. Several ordered response choice models for different tour sizes were developed for this purpose, predicting the choice order of the 1st, 2nd, 3rd, etc. planned activity in the tour as a function of activity type, activity characteristics (duration, frequency, travel time, and involved persons), and individual characteristics. Activity duration played the most significant role in the models compared to any other single variable, wherein longer duration activities tended to be planned much earlier in tours. This strongly suggests that the amount of time-use, rather than the nature of the event as indicated by activity type, is a primary driver of within-tour planning order and offers potential for a much improved and valid fit.     

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.