Analysis of within-household effects and between-household differences in maintenance activity allocation

This paper investigates the allocation of household individuals to out-of-home maintenance activities using the rich activity-travel diary data from the San Francisco Bay Area. Two inter-related decisions are considered in this context: (i) whether the given activity episode is performed individually (solo) or jointly, and (ii) the person who participates in the activity, if it is a solo activity. To account for the conditional nature of the solo activity person selection, a nested mixed logit modeling framework is proposed and implemented to jointly analyze person allocation for all maintenance activities performed by a household on a given day. The model is used to investigate within-household effects and between-household differences. The proposed model relaxes some important restrictions in person allocation models by accounting for various sources of correlations and relaxing the assumption of constant variance across households. The proposed model is used to analyze the differences in person allocation between different types of households. The results indicate that life-cycle and household role, income, gender, employment status, and several types of constraints (activities including cost, time-availability, vehicle-availability, coordination constraints, and child-care obligations) affect person allocation decisions in the context of maintenance activities. The empirical results indicate the presence of various sources of correlations across persons, over activities, and within-household that are significant. In addition, the data also provides evidence that the unobserved variances in person selection utilities are not constant across households. A better understanding of these within-household interactions and between-household differences may be used in activity-based simulation models and to develop more effective and focused demand management measures.     

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.     

Regimes in social–cultural events-driven activity sequences: Modelling approach and empirical application

In this study we propose and apply a Bayesian-network model to predict and analyse the factors that influence activity-travel sequences that are triggered by social–cultural events. The study is motivated by the intention to examine the wider context in which activity-travel decisions are made and to model such decisions under longitudinal time horizons. We assume that social events trigger a series of interrelated activities and corresponding trips. Data about events and related activities are collected using a month-diary and involving a large sample of households in the Eindhoven region, The Netherlands. A learning algorithm is applied to derive a Bayesian-network model from the event diary. The results show that indeed many travel choices are influenced by particular events, that these influences vary by socio-demographic variables and that the learned Bayesian-network model is able to represent these interdependencies among all these variables. We demonstrate how the model can be used to predict event-driven activity-travel sequences in a micro-simulation.     

An activity-based approach of investigating travel behaviour of older people

The paper presents the results of an investigation on daily activity-travel scheduling behaviour of older people by using an advanced econometric model and a household travel survey, collected in the National Capital Region (NCR) of Canada in 2011. The activity-travel scheduling model considers a dynamic time–space constrained scheduling process. The key contribution of the paper is to reveal daily activity-travel scheduling behaviour through a comprehensive econometric framework. The resulting empirical model reveals many behavioural details. These include the role that income plays in moderating out-of-home time expenditure choices of older people. Older people in the highest and lowest income categories tend to have lower variations in time expenditure choices than those in middle-income categories. Overall, the time expenditure choices become more stable with increasing age, indicating that longer activity durations and lower activity frequency become more prevalent with increasing age. Daily activity type and location choices reveal a clear random utility-maximizing rational behaviour of older people. It is clear that increasing spatial accessibility to various activity locations is a crucial factor in defining daily out-of-home activity participation of older people. It is also clear that the diversity of out-of-home activity type choices reduces with increasing age and older people are more sensitive to auto travel time than to transit or non-motorized travel time.     

A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty

An understanding of the interaction between individuals’ activities and travel choice behaviour plays an important role in long-term transit service planning. In this paper, an activity-based network equilibrium model for scheduling daily activity-travel patterns (DATPs) in multi-modal transit networks under uncertainty is presented. In the proposed model, the DATP choice problem is transformed into a static traffic assignment problem by constructing a new super-network platform. With the use of the new super-network platform, individuals’ activity and travel choices such as time and space coordination, activity location, activity sequence and duration, and route/mode choices, can be simultaneously considered. In order to capture the stochastic characteristics of different activities, activity utilities are assumed in this study to be time-dependent and stochastic in relation to the activity types. A concept of DATP budget utility is proposed for modelling the uncertainty of activity utility. An efficient solution algorithm without prior enumeration of DATPs is developed for solving the DATP scheduling problem in multi-modal transit networks. Numerical examples are used to illustrate the application of the proposed model and the solution algorithm.     

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.     

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.     

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.     

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.     

A reference-dependent user equilibrium model for activity-travel scheduling

Existing user equilibrium models of activity-travel scheduling generally fall short in representing travelers’ decision-making processes. The majority have either implicitly or explicitly assumed that travelers follow the principle of utility maximization. This assumption ignores the fact that individuals may be loss–averse when making activity-travel decisions. Allowing for the situation that travelers possess accurate information of the urban-transportation system due to modern technologies, studies on reference-dependent decision-making under near-perfect information are receiving increasing attention. In view of traveler heterogeneity, individuals can be divided into multiple classes according to their reference points. In this paper, we propose a reference-dependent multi-class user equilibrium model for activity-travel scheduling, which can be reformulated as a variational inequality problem. Moreover, comparative analyses are conducted on the equilibrium states between utility-maximization (no reference) and reference-dependency of exogenous and endogenous references. A numerical example regarding combined departure-time and mode choice for commuting is conducted to illustrate the proposed model. The simulated results indicate that reference points and loss aversion attitudes have significant effects on the choice of departure time and mode.     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

A computerized household activity scheduling survey

Household activity scheduling is widely regarded as the underlying mechanism through which people respond to emerging travel demand management policies. Despite this, very little fundamental research has been conducted into the underlying scheduling process to improve our understanding and ability forecast travel. The experimental survey approach presented in this paper attempts to fill this gap. At the core of the survey is a Computerized Household Activity Scheduling (CHASE) software program. The program is unique in that it runs for a week long period during which time all adult household members login daily to record their scheduling decisions as they occur over time. An up-front interview is used to define a household's activity agenda and mode availability. A sample of 41 households (66 adults and 14 children) was used to assess the performance of the survey. Analysis focuses on times to completion, daily scheduling steps, activity-travel patterns, and scheduling time horizons. Overall, the results show that the computer-based survey design was successful in gathering an array of information on the underlying process, while minimizing the burden on respondents. The survey was also capable of tracing traditionally observed activity-travel outcomes over a multi-day period with minimal fatigue effects. The paper concludes with a detailed discussion on future survey design, including issues of instrument bias, use of the Internet, and improved tracing of spatial behaviour. Future use of the survey methodology to enhance activity-travel diary surveys and stated responses experiments is also discussed. This revised version was published online in June 2006 with corrections to the Cover Date.     

An integrated framework to jointly model decisions of activity time allocation and work-related travel

Five activity-travel choice dimensions, including three activity time allocation decisions and two work-related travel choices, are jointly modeled using the structural equation model in order to accommodate the complex interactions among them. Via a two-step estimation approach, the behavioral pattern underlying activity-travel decisions is explicitly revealed. For example, it demonstrates the priority with respect to subsistence activity, maintenance activity, and recreation activity due to a limited time budget; and bus commuting behavior positively influences the time allocated to the maintenance activity. In addition, two attitudinal factors are constructed and confirmed to have important effects on the five behavioral dimensions, which contribute to reveal the decision-making process from the perspective of psychology. This comprehensive framework is expected to provide important implications for mobility management and urban planning.     

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.

     

A retrospective and prospective survey of time-use research

The central basis of the activity-based approach to travel demand modeling is that individuals' activity-travel patterns are a result of their time-use decisions within a continuous time domain. This paper reviews earlier theoretical and empirical research in the time-use area, emphasizing the need to examine activities in the context or setting in which they occur. The review indicates the substantial progress made in the past five years and identifies some possible reasons for this sudden spurt and rejuvenation in the field. The paper concludes that the field of time-use and its relevance to activity-travel modeling has gone substantially past the "tip of the iceberg", though it certainly still has a good part of the "iceberg" to uncover. Important future areas of research are identified and discussed.     

Urban activity pattern classification using topic models from online geo-location data

Location-based check-in services in various social media applications have enabled individuals to share their activity-related choices providing a new source of human activity data. Although geo-location data has the potential to infer multi-day patterns of individual activities, appropriate methodological approaches are needed. This paper presents a technique to analyze large-scale geo-location data from social media to infer individual activity patterns. A data-driven modeling approach, based on topic modeling, is proposed to classify patterns in individual activity choices. The model provides an activity generation mechanism which when combined with the data from traditional surveys is potentially a useful component of an activity-travel simulator. Using the model, aggregate patterns of users’ weekly activities are extracted from the data. The model is extended to also find user-specific activity patterns. We extend the model to account for missing activities (a major limitation of social media data) and demonstrate how information from activity-based diaries can be complemented with longitudinal geo-location information. This work provides foundational tools that can be used when geo-location data is available to predict disaggregate activity patterns.     

Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

Incorporating time and income constraints in dynamic agent-based models of activity generation and time use: Approach and illustration

Existing theories and models in economics and transportation treat households’ decisions regarding allocation of time and income to activities as a resource-allocation optimization problem. This stands in contrast with the dynamic nature of day-by-day activity-travel choices. Therefore, in the present paper we propose a different approach to model activity generation and allocation decisions of individuals and households that acknowledges the dynamic nature of the behavior. A dynamic representation of time and money allocation decisions is necessary to properly understand the impact of new technologies on day to day variations in travel and activity patterns and on performance of transportation systems. We propose an agent-based model where agents, rather than acting on the basis of a resource allocation solution for a given time period, make resource allocation decisions on a day-by-day basis taking into account day-varying conditions and at the same time respecting available budgets over a longer time horizon. Agents that share a household interact and allocate household tasks and budgets among each other. We introduce the agent-based model and formally discuss the properties of the model. The approach is illustrated on the basis of simulation of behavior in time and space.     

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.