Restrictions affecting activity and travel patterns (eg work start times, store hours, carpooling and parking costs). The purpose of this study is to analyze household activity and travel patterns in the context of travel demand analysis for transportation planning purposes.
Objectives and overview of approach
Examine the relationships between ICT use and physical/virtual activity participation for discretionary and maintenance activities. Analyze the interactions between perceived daily travel patterns (represented by the dimensions of travel frequency and travel duration), ICT use and individual's activity characteristics.
Structure of thesis
Introduction
Second, an attempt is made to synthesize current knowledge on activity-based analysis, particularly in the context of the objectives of this study (presented in Chapter 1). The chapter concludes with a discussion of significant gaps and limitations in existing studies in relation to the purpose of this thesis.
Conceptual and modeling frameworks
A fourth category of models aims to analyze the planning of activities through the analog of computerized production systems that include a set of rules in the form of condition-action (If-Then) pairs (See Garling et al., (1994)). Computational process models are models of production systems that are based on a set of rules in the form of condition-action pairs (Garling et al., (1994); and Miller et al., (2003)).
Activity-types and empirical analysis
The authors found that seniors (over 65 years old) are the most likely to participate in physically active travel recreation compared to other recreation categories. Younger respondents were more likely to participate in recreational activities such as exercise and physical fitness, while older respondents had more time available to engage in other discretionary activities.
Household role and within-household interactions
The authors reported the following findings: "household heads' working hours negatively affect the decision to shop during the day, a female non-worker is more likely to be assigned the shopping responsibility compared to male non-workers and both adults on the weekend are more likely to perform shopping activities in community' (Bhat, (2004)).Gliebe and Koppelman, (2001) estimated time allocation for various activities outside the home by adult household members using proportional share model.
Role of ICT and advanced technologies on activity allocation in the household
- Role of ICT’s on telecommuting
- Influence of ICT’s on e-shopping behavior
- Effect of different types of ICT devices on overall travel and activity patterns
- Other ICT related studies
- Limitations of these studies
Casas et al., (2001) also investigated the effects of online shopping on travel based on one-day activity data. Another interesting line of inquiry relates to the connection between travel patterns and ICT use is to evaluate the effect of ICT devices on travel.
Summary
Furthermore, the impact of ICT use on interpersonal interdependence within a household has not been sufficiently examined, and the effect of substitutability between different types of activities and different uses of ICT and travel should be investigated. The modeling frameworks and specific objectives of this analysis are presented in the next chapter. The modeling framework used for this analysis and salient findings are also presented in this chapter.
Introduction
Given that research in this area is still in its infancy, there are several methodological and definitive challenges that are not adequately addressed in current person segmentation models (these are discussed in section 3.4.2 in detail). In section 3.2, previous literature related to intra-household interactions and distribution of persons' activities is reviewed. The modeling framework used to analyze the distribution of persons for household maintenance and discretionary activities is described in Section 3.4, while the hypotheses of interest are explained in Section 3.5 and the modeling results are presented in Section 3.6, followed by Section 3.7 discussing the model validation. . tasks performed in this study.
Literature review related to person allocation
For example, Simma and Axhausen, (2001) examined the division of activities between the head of the household and the spouse. Bhat and Srinivasan, (2004); and Vovsha, Petersen and Donnelly, (2004) have also analyzed the factors that influence participation in the activity of the head of the household role. Most researchers were interested in the primary couple (i.e., the head of household and the spouse) in a household (Simma et al., (2001); Vovsha et al., (2004); and Bhat et al., (2004 ) ) where as the authors in this study have also analyzed the activity patterns of other individuals.
Data and descriptive statistics
Almost 35% of the households are single-person households and were not considered for further analysis. Regarding the distribution of household role in the sample, 44% of individuals are heads of households, 36% are spouses, 4% are parents/in-laws, 12% are children, and the remaining 4% of individuals correspond to other household role (brother, grandparent, aunt/uncle). As expected, most of the eligible individuals (about 88% of the sample) have a driver's license and the remaining 12% do not.
Modeling framework and calibration procedure
Behavioral framework
Special features of this framework
X2hki = explanatory characteristics that influence the utility of conditional person assignment for individual i of household h, for activity k (eg age, gender etc.). 1hk = independent component of the time-varying error of the utility that varies across different activities k of household h and. 2hki = independent part of the person and trip-specific random component of conditional solo utility for individual i of household h for activity k, and ~ IID Gumbel(0, ? 2/6).
Modeling modifications to accommodate these features 1. Variable Choice Set Problem
Uhksolo and Uhki become mutually independent between individuals in the household and the activities of the household. To capture the generic effects within households due to the role of the household and the life cycle group, indicator variables are defined for each household and member (e.g. yperson1,head = 1 if person 1 is the head of the household and zero otherwise ; .. otherwise indicators for spouse etc. are defined in the same way). These probabilities are then aggregated to obtain the average probability of an estimate for the head of household, head's spouse, other solo and joint activity participation for each household.
Hypothesis
Other constraints, including the presence of children, may result in greater participation in joint maintenance activities and possibly lower participation of female household members (if the children are young). The primary interest of this aspect is to investigate the differences in the activity participation of the household head, the spouse and the other individuals belonging to different types of households (classified according to household composition, structure or social- economic characteristics). The differences in activity participation between different households are also analyzed in terms of age groups of couples, combinations of vehicles and adults and household income.
Model results and discussion
- Household role effects
- Person characteristics
- Role of constraints
- Within-household differences in activity allocation across different episodes
- Between-Household Differences
As expected, the role of the head decreases (48 to 40% for maintenance and 44 to 23% for discretionary activities). In contrast, in lower income households the head of the household plays a primary role in maintenance activities (head = 49%, spouse = 26%). Interestingly, the role of the spouse (e hhh) is also increased (36% with children, and 30% without children for maintenance; while the corresponding probabilities are 38% versus 29% for discretionary activities).
Assumptions and validation
In contrast, when both hhh and spouse belong to the older age category (> 50 years), joint activity participation increases between 15-17% for both maintenance and discretionary activity. Note that the participation of other individuals (only) is the maximum (15%) for the couple aged 31-50 and may coincide with the presence of children aged 6-15 and their participation in recreational activities. In contrast, the participation of other individuals is much lower 4-6% for couples of other age groups.
Summary
Notably, the calibrated model performs quite well in predicting total market shares on the predicted data set, with errors of <1.2% for maintenance activities and. Household with 0-5-year-old children*female Travel home*full-time worker Non-home-based travel* without license Home travel*female. Three or more car households (eligible drivers - vehicles)>=2 (eligible drivers - vehicles)==1 (eligible drivers - vehicles)<=0 Low income tt (<40K) Middle income tt (40-74k) High income tt ( >=75k) Zero child household Presence of 0-5 year old children Presence of 6-15 year old children HHH is full time, but not spouse Spouse is full time, but not HHH HHH and spouse is full time Couple <=30 years.
Introduction
The second objective examines the relationships between ICT use and physical/virtual activity participation for discretionary and maintenance activities. The role of ICT usage characteristics and the virtual activity propensity on these decision dimensions is explicitly modeled in this objective. The final objective aims to investigate the relationships between perceived daily travel patterns (represented by the dimensions of travel frequency and travel duration), ICT use and individual's activity characteristics.
Data description and descriptive statistics
Analysis of descriptive statistics for the sample used in this study revealed that the sample profile matched reasonably closely in terms of activity and travel characteristics with the 1996 SF activity data and the 1998 Miami survey data. Nearly three-quarters of the sample (72%) had a telephone in their household, while 28% had two or more phones. Among individual characteristics, the sample consisted of 48% males and almost 69% of respondents were workers.
Modeling structure
Logit model structure
Vij = deterministic utility component of alternative i for person j εij = random utility component of alternative i for person j εij ∼ IID Gumbel Distribution.
Assumptions and exceptions
Results and Discussion
Models of ICT use patterns
People who use the Internet for recreational activities are less likely to perform subsistence, browsing, and maintenance activities online. For example, people with licenses are more likely to use the Internet than those without a license. People of African-American or Hispanic ethnicity are less likely to use the Internet than people of other races.
Relationship between physical and virtual activity participation
Patterns of ICT use also influence the duration of episodes of indoor and outdoor activity. Users who use the Internet more often for recreational purposes (obtained from the models in Section 4.1) are also likely to spend less time on leisure activities outside the home. Work characteristics have a strong influence on the duration of maintenance work indoors and outdoors.
Interactions between travel, activity pattern, and ICT use
Summary and Conclus ions
Variable (→) Internet Use Livelihood Browsing Leisure Maintenance Ind variable (↓) Coefficient Coefficient Coefficient Coefficient Coefficient Constant. Note: Normal size is significant at 5% or better, Italic is significant at 10%, --- indicates insignificant at 10%. Note: Physical activities can be performed inside the home and virtual activities can be performed outside the home, but are not modeled in this study due to lack of disparate data.
Overview
In the third objective, this study also investigated the relationships between perceived daily travel patterns (represented by the dimensions of travel frequency and travel duration), ICT use and individual's activity characteristics. In particular, the relationship between virtual activity participation, physical activity participation, internet usage patterns and perceived travel dimensions was analyzed. The salient findings of the two sets of tasks are discussed in the next section.
ICT use patterns
ICT use can also lead to substitution across virtual activities, in addition to substitution between physical and virtual activities. Time constraints and familiarity with newer technologies tend to increase ICT use and virtual activity participation (workers, professionals, etc.). ICT use can promote more frequent but more efficient trips (more frequency-higher internet use, more duration-less internet use).
Directions for future research
Paper presented at the Transportation Research Board 80th Annual Conference, Washington, D.C. Domestic telephone habits and daily mobility. Paper presented at the Transportation Research Board 80th Annual Conference, Washington, D.C. What about people in regional science. Paper presented at the Transportation Research Board 82nd Annual Conference, Washington, D.C. Synthesis of historical activity analysis applications.
