The effect of location,mobility and socio-demographic factors on task and time allocation of households

This paper investigates the role of location factors in task and time allocation at the household level. It is hypothesized that, if time constraints are less binding as a result of living in an urban area or owning more cars, spouses engage more often and longer in out-of-home activities and schedule their activities more independently. The hypotheses are tested with logistic and Cox regression models of activity participation and time allocation on a data set collected in the Amsterdam–Utrecht region in the Netherlands. Results suggest that the hypotheses are supported with respect to specific household activity scheduling decisions.     

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.     

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.     

Modeling interdependencies between vehicle transaction,residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.     

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.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

A conceptual and methdological framework for the generation of activity-travel patterns

This paper develops a conceptual framework for the generation of activity and travel patterns in the context of more general structures and presents an integrated model system as a step toward development of an improved travel demand forecasting model system. We propose a two-stage structure to model activity and travel behavior. The first stage, the stop generation and stop/auto allocation models, consists of the choices for the number of household maintenance stops and the allocation of stops and autos to household members. The second stage, the tour formation model, includes the choices for the number of tours and the assignment of stops to tours for each individual, conditional on the choices in the first stage. Empirical results demonstrate that individual and household socio-demographics are important factors affecting the first stage choices, the generation of maintenance stops and the allocation of stops and autos among household members, and the second stage choices, the number of tours and the assignment of stops to tours. This revised version was published online in June 2006 with corrections to the Cover Date.     

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.     

Model and a solution algorithm for the dynamic resource allocation problem for large-scale transportation network evacuation

Allocating movable resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning on large-scale networks. The proposed model is built on the earliest arrival flow formulation that significantly reduces problem size. A set of binary variables, specifically, the beginning and the ending time of resource allocation at a location, enable a strong formulation with tight constraints. A solution algorithm is developed to solve for an optimal solution on large-scale network applications by adopting Benders decomposition. In this algorithm, the MILP model is decomposed into two sub-problems. The first sub-problem, called the restricted master problem, identifies a feasible dynamic resource allocation plan. The second sub-problem, called the auxiliary problem, models dynamic traffic assignment in the evacuation network given a resource allocation plan. A numerical study is performed on the Dallas–Fort Worth network. The results show that the Benders decomposition algorithm can solve an optimal solution efficiently on a large-scale network.     

A model of household time allocation taking into consideration of hiring domestic helpers

Household maintenance such as childcare not only induces activities and travel but also impose time constraints on individuals’ participation in other activities and travel. Instead of sharing household responsibilities, households may hire domestic helpers for household maintenance. Alternatively, they may get helps from members of the extended family such as parents of household heads. This paper develops a model to analyze households’ trade-offs between hiring domestic helpers for household maintenance and taking these responsibilities by household members. We will apply household economic theories to develop a time allocation model incorporating interactions among household members. We assume that households trade off the money they are willing to spend for hiring helpers with the time they may need to spend for household maintenance activities to maximize utilities, subject to time constraints. The model may be used to analyze the impacts of domestic helpers on household members’ time allocation to subsistence, maintenance and recreation activities. It may also be applied to analyze the impacts of government policies regarding the minimum salary of domestic helpers and the change of household members’ wage rates on households’ decision to hire helpers. The paper extends the current literature on intra-household activity–travel interactions by considering external helps from domestic helpers, which may contribute to the understanding of activity–travel patterns of household members.     

A model of daily time allocation to discretionary out-of-home activities and trips

This paper presents a model of discrete activity choice and continuous resource allocation which is based on the premise of random utility maximization and which can be conveniently estimated using existing statistical software packages. The model derivation involves virtually no approximations and adheres strictly to the utility maximization concept. The empirical analysis applies the model to the participation choice and resource (time) allocation to nonwork, out-of-home activities by workers. The statistical results show that activity choice and time allocation are governed by the same mechanism as the utilitarian assumptions indicate and support the theoretical framework employed in the model development.     

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.     

Uncertainty modeling for bus selection and allocation in a private transportation system

ABSTRACT

This paper describes the development of a probabilistic formulation that provides global optimum selection and allocation of a fleet of buses in a private transportation system of an organization where a third party is hired to provide transportation for its employees and their dependents. In this private transportation system, a fleet of buses is to be selected and allocated to serve employees and their independents on different prescheduled trips along different routes from the organization’s headquarters and residential compound where round-trip times of scheduled trips are subject to uncertainty due to random delays. We propose a probabilistic approach based on 0-1 integer programming for the selection and allocation to determine the optimal number and size of buses assigned to a set of prescheduled trips in a particular time interval. Examples and a case study are presented to illustrate the applicability and suitability of the proposed approach.     

An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non- working days

The behavior of time allocation to two types of discretionary activities is formulated as a doubly-censored Tobit model. The model is capable of incorporating cases where the entire amount of time available for discretionary activity is allocated to one type of activity and the other type of activity is not engaged at all. The model is applied to examine individuals' allocation of time to in-home and out-of-home discretionary activities on working days and non-working days, using a weekly time-use data set from the Netherlands. Workers' daily activity patterns vary significantly between working days and non-working days, while it can be expected that patterns of time allocation are correlated between working days and non-working days. A set of error components is introduced into the model to represent this correlation, adopting a mass point approach which requires no assumption about the distribution of the error components. The validity of the model is examined statistically.     

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

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

Impacts of Information and Communication Technologies (ICT) on time use and travel behavior: a structural equations analysis

The objective of this paper is to contribute an empirical study to the literature on transportation impacts of Information and Communications Technologies (ICT). The structural equation model (SEM) is employed to analyze the impacts of ICT usage on time use and travel behavior. The sample is derived from the travel characteristic survey conducted in Hong Kong in 2002. The usage of ICT is defined as the experience of using e-mail, Internet service, video conferencing and videophone for either business or personal purposes. The results show that the use of ICT generates additional time use for out-of-home recreation activities and travel and increases trip-making propensity. Individuals at younger age or with higher household income are found to be more likely ICT users. The findings of this study provide further evidence on the complementarity effects of ICT on travel, suggesting that the wide application of ICT probably leads to more, not less, travel. The study also demonstrates the importance of considering the interactions between activity and travel for better understanding of the nature and magnitude of the impacts of ICT on time use and trip making behavior.     

Generic model for resource allocation in transportation. Application to urban parking management

In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a “coopetitive” model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model.     

A multivariate ordered-response model system for adults’ weekday activity episode generation by activity purpose and social context

This paper proposes a multivariate ordered-response system framework to model the interactions in non-work activity episode decisions across household and non-household members at the level of activity generation. Such interactions in activity decisions across household and non-household members are important to consider for accurate activity-travel pattern modeling and policy evaluation. The econometric challenge in estimating a multivariate ordered-response system with a large number of categories is that traditional classical and Bayesian simulation techniques become saddled with convergence problems and imprecision in estimates, and they are also extremely cumbersome if not impractical to implement. We address this estimation problem by resorting to the technique of composite marginal likelihood (CML), an emerging inference approach in the statistics field that is based on the classical frequentist approach, is very simple to estimate, is easy to implement regardless of the number of count outcomes to be modeled jointly, and requires no simulation machinery whatsoever.The empirical analysis in the paper uses data drawn from the 2007 American Time Use Survey (ATUS) and provides important insights into the determinants of adults’ weekday activity episode generation behavior. The results underscore the substantial linkages in the activity episode generation of adults based on activity purpose and accompaniment type. The extent of this linkage varies by individual demographics, household demographics, day of the week, and season of the year. The results also highlight the flexibility of the CML approach to specify and estimate behaviorally rich structures to analyze inter-individual interactions in activity episode generation.