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.     

Estimating a model of dynamic activity generation based on one-day observations: Method and results

In this paper we develop and explore an approach to estimate dynamic models of activity generation on one-day travel-diary data. Dynamic models predict multi-day activity patterns of individuals taking into account dynamic needs as well as day-varying preferences and time-budgets. We formulate an ordered-logit model of dynamic activity-agenda-formation decisions and show how one-day observation probabilities can be derived from the model as a function of the model’s parameters and, with that, how parameters can be estimated using standard loglikelihood estimation. A scale parameter cannot be identified because information on within-person variability is lacking in one-day data. An application of the method to data from a national travel survey illustrates the method. A test on simulated data indicates that, given a pre-set scale, the parameters can be identified and that estimates are robust for a source of heterogeneity not captured in the model. This result indicates that dynamic activity-based models of the kind considered here can be estimated from data that are less costly to collect and that support the large sample sizes typically required for travel-demand modeling. We conclude therefore that the proposed approach opens up a way to develop large-scale dynamic activity-based models of travel demand.     

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.     

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.     

A model of joint activity participation between household members

A proportional shares model of daily time allocation is developed and applied to the analysis of joint activity participation between adult household members. The model is unique in its simultaneous representation of each decision maker's decisions concerning independent activity participation, allocation of time to joint activities, and the interplay between individual and joint activities. Further, the model structure ensures that predicted shares of joint activity outcomes be the same for both decision makers, an improvement over models that do not make interpersonal linkages explicit. The empirical analysis of travel diary data shows that employment commitments and childcare responsibilities have significant effects on tradeoffs between joint and independent activities. In addition, evidence is presented for the continued relevance of gender-based role differences in caring for children and employment participation.     

Incorporating planned activities and events in a dynamic multi-day activity agenda generator

Daily agenda formation is influenced by formal commitments, satisfaction of needs surpassing some threshold and the desire to conduct particular activities in anticipation of socially and religiously driven events such as birthdays, Christmas, etc. As part of a research program to develop a dynamic activity-based model of transport demand, this paper proposes a model to represent dynamic agenda formation, including these different underlying processes. Bayesian estimation of the model is based on data collected through a Web-based survey for a sample of approximately 300 respondents. The survey uses an extension of a 1-day activity diary where respondents are asked to recall activities in retrospect and to identify planned activities in prospect. Estimation results suggest that planned activities influence agenda formation in general, but that their significance and size depends on activity type, socio-demographics and dwelling characteristics.     

The interactions between e-shopping and traditional in-store shopping: an application of structural equations model

As the proliferation of e-commerce leads to ever greater numbers of on-line transactions, transportation planners are interested in the impacts of e-shopping on our strained transportation systems. Although the substitution effect of e-shopping is appealing, previous studies provided mixed results on its impact. Using 539 adult internet users in the Minneapolis-St Paul metropolitan area, this study applied a structural equations model to investigate the interactions among online purchases, in-store shopping, and product information search via internet. We found that online searching frequency has positive impacts on both online and in-store shopping frequencies and online buying positively affects in-store shopping. In particular, the marginal effects of online-buying frequency and online-searching frequency on in-store shopping frequency were estimated at 0.153 and 0.189, respectively. Since the internet as a shopping channel tends to have a complementary effect on in-store shopping, the rise of e-shopping is not likely to be a solution but a challenge to travel reduction.     

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.     

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

     

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.     

Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area

Transportation - This paper presents a longitudinal analysis of activity generation behaviour in the Greater Toronto and Hamilton Area (GTHA) between 1996 and 2016 for various activity types: work,...     

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.     

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.     

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.     

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’.     

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.

SMART: simulation model for activities, resources and travel

This paper proposes the development of an activity-based model of travel that integrates household activities, land use patterns, traffic flows, and regional demographics. The model is intended as a replacement of the traditional Urban Transportation Planning System (UTPS) modeling system now in common use. Operating in a geographic-information system (GIS) environment, the model's heart is a Household Activity Simulator that determines the locations and travel patterns of household members daily activities in 3 categories: mandatory, flexible, and optional. The system produces traffic volumes on streets and land use intensity patterns, as well as typical travel outputs. The model is particularly well suited to analyzing issues related to the Clean Air Act and the Intermodal Surface Transportation Efficiency Act (ISTEA). Implementation would, ideally, require an activity-based travel diary, but can be done with standard house-interview travel surveys. An implementation effort consisting of validation research in parallel with concurrent model programming is recommended.     

Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach

Transportation - Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict...