Formulation of an Activity-Based Utility Measure of Time Use Application to Understanding the Influence of Constraints Xin Ye, Karthik Konduri, Ram M. Pendyala, and Bhargava Sana In this paper, a methodology to compute a composite measure of time use that accounts for in-home and out-of-home activity engagement and time allocation patterns of individuals is demonstrated. There are two motivating factors. First, in the context of the implementation of travel demand management strategies that influence individual behavior (possibly by influencing constraints), it would be of interest to have a time use utility measure that is capable of offering a measure of welfare or satisfaction that an individual derives from his or her activity travel pattern (2, 8, 9). Presumably, individuals like to engage in activities and travel that are pleasurable. Having this time use utility measure as a postprocessor for activity-based model systems would provide the ability to measure directly the change in benefit an individual experiences as a result of a policy or system change. One could also measure time use utility associated with a range of feasible activity travel patterns and evaluate them on a comparative basis to determine the pattern an individual is most likely to adopt in response to a policy (10). Second, in the context of understanding the role of constraints, it would be interesting to assess the impacts of various constraints— such as household, monetary, and temporal factors—on time use utility measures. How do these constraints affect the time use utilities of various socioeconomic groups? Does less time spent out of home by the elderly imply a lower quality of life? Or are they deriving more utility from engaging in certain in-home activities? These questions motivate the part of this paper in which the influence of temporal, monetary, and household constraints on women’s time use utility measures is assessed. The time use utility measure presented here is intended to serve as a postprocessor for activity-based travel modeling systems that predict activity travel patterns at the level of the individual traveler in response to a wide range of socioeconomic, demographic, land use, modal level-of-service, and accessibility scenarios. This measure is not intended to replace what an activity-based model system does but merely complements such a system by offering a mechanism for quantifying the composite utility an individual may associate with his or her activity travel pattern based on time use allocation to various in-home and out-of-home activities and associated travel. This paper is organized as follows. Formulation of the time use utility measure is presented in the next section. The data set and survey sample are described in the third section. Model estimation results are presented in the fourth section. A comparison of time use utility measures across different sample groups is presented in the fifth section. Concluding thoughts are offered in the final section.
This paper presents and demonstrates a methodology to compute a composite time use utility measure that accounts for in-home and outof-home activity engagement and time allocation patterns of individuals. The measure could be used for welfare analysis in the context of a policy intervention and to model the search and adaptation routine that individuals may follow in choosing an alternative activity travel pattern in response to a policy intervention. The proposed measure can be implemented as a postprocessor for activity-based model systems to evaluate the satisfaction that travelers derive from their overall daily activity travel pattern. With data from the 2005 American Time Use Survey, the analysis was performed for a sample of women stratified by employment status, income, and presence of children. Comparisons of time use utility measures across these cross-classified groups offer insights into the influence of temporal (employment), monetary (income), and household obligation (children) constraints on the utility individuals derive from their activity travel pattern. In general, it was found that time use utility values were affected most adversely by temporal constraints, followed by monetary constraints, and then by the presence of children.
The notion of time use lies at the heart of the activity-based microsimulation modeling framework (1, 2). Time is a limited resource and much research has been devoted to understanding and describing people’s time use patterns across a wide range of socioeconomic, demographic, and geographic location contexts (3– 6). Not only does time serve as a constraint directly, but many other constraints result in changes to temporal constraints. For example, the presence of a child at home may impose temporal constraints as an adult may have to be home at certain hours of the day to take care of the child. Institutional constraints (store hours) constitute temporal constraints, shifting to or using slower modes of transportation influences temporal constraints, work-related schedules impose temporal constraints, and so on. Therefore, the notion of time is central to understanding how people shape their activity travel patterns around constraints (7 ).
X. Ye, National Center for Smart Growth Research and Education, University of Maryland 1112D, Preinkert Field House, College Park, MD 20742. K. Konduri, R. M. Pendyala, and B. Sana, Department of Civil and Environmental Engineering, Arizona State University, Room ECG252, Tempe, AZ 85287-5306. Corresponding author: B. Sana,
[email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2135, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 60–68. DOI: 10.3141/2135-08
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Ye, Konduri, Pendyala, and Sana
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FORMULATION OF TIME USE UTILITY MEASURE Formulation of the time use utility measure in this paper essentially builds on the one developed by Kitamura et al., which considers time spent at various activities, travel undertaken to engage in outof-home activities, and the total home sojourn (time spent at home) as the key ingredients (9). This formulation enhances their approach by considering time allocation for in-home activities in addition to that for out-of-home activities. Rather than treat in-home time as a single block of time contributing to the utility, this paper considers time spent on different types of in-home activities as contributing to the utility measure in different ways. Presumably, a leisure activity such as watching television or playing a video game contributes more positively to time use utility than a chore such as cleaning or doing laundry. Let the utility derived from the pursuit of activity of type q (Uq) be Uq = ⎡⎣ x qβ + γ ln ( Sq + 1) + ⑀ q ⎤⎦ ln ( Tq + 1)
(1)
where Tq = cumulative daily time expenditure on activity of type q, Sq = cumulative daily time expenditure on travel for activity of type q, xq = vector of covariates affecting utility Uq, γ = scalar coefficient associated with ln(Sq + 1), β = vector of coefficients associated with xq, and ⑀q = independent and identically distributed random error term in Uq. The subscript i corresponding to the individual is suppressed for notational simplicity. The purpose of adding unity to the time expenditures within the logarithmic terms associated with Sq and Tq is to allow the possibility of 0 time expenditures for activities or travel occasionally encountered in travel survey data sets. Differences across individuals with respect to the time use utility due to measurable factors are captured in the vector of covariates xq, which include standard socioeconomic and demographic attributes of the household and the individual. Unobserved factors are accounted for by inclusion of the random error term ⑀q. The merit of this formulation is that it explicitly recognizes the utility (or disutility) one may derive from time expenditures for activities and travel, which is particularly appropriate for nonmandatory activities that one generally pursues by choice. Mandatory activities such as work and school are generally pursued because they serve as subsistence activities that offer compensation (either monetary or educational). As such, the measurement of utility associated with mandatory activities is rather complex because one does not have the ability to untangle time use utility from monetary compensation utility. Also, as mandatory activity time allocation and related travel time expenditures are largely inflexible, it was considered appropriate to limit this time use utility calculation to nonmandatory activities that potentially could be affected by implementation of travel demand management strategies or by changes in constraints identified in the previous section. The formulation in Equation 1 might not be considered a comprehensive representation of the utility of an activity as it does not account for the amount of money spent on the activity (11) and the quality of the activity experience (9, 12). There is virtually no data set that offers information about monetary expenditures at specific activities and the individual’s perception of the quality of the experience.
If and when such data become commonly available, and transportation planning methods are capable of incorporating such considerations, the utility expression of Equation 1 can be extended in a straightforward manner to include additional terms. In this paper, the formulation constitutes a strict time-use-based utility measure; thus, it is capturing the time use component of the overall utility that an individual gains (or loses) from participating in an activity type. For in-home activities, which entail no travel, the value of Sq is 0 and that term is eliminated in the utility computation. Thus, inhome activities offer utility purely from the time allocated to the activity. For out-of-home activities, it is conceivable that utility is derived from both the activity and the travel experience. On the one hand, travel expenditure may contribute positively to the utility because individuals would travel farther only if the destination offered a higher level of utility than a closer destination—that is, the travel time variable may serve as a surrogate for the quality of the experience at the activity destination. If that is true, then γ would be positive. In addition, there is some evidence that travelers derive positive utility from the travel experience (13, 14), which also may contribute to a positive coefficient for γ. On the other hand, if travel time is viewed as a burden, then γ will be negative, suggesting that travel contributes negatively to the utility derived from a certain activity type. Thus, this analysis (through the sign on the coefficient of the Sq term) sheds some light on the extent to which travel contributes positively or negatively to the utility of a daily activity travel pattern. For purposes of normalization, the utility function for the in-home sleep activity (Us) is reduced to the following form: Us = ln ( Ts + 1)
(2)
where Ts is cumulative daily time expenditure on sleep activity. Consistent with the notion of random utility maximization, it is assumed that individuals attempt to maximize their cumulative utility over all activities and travel. Thus, it is possible to formulate the utility maximization paradigm as a constrained optimization problem (where the subscript i corresponding to the individual is suppressed for notational simplicity): maximize
∑U
q
+ Us
(3)
q
subject to
∑T + ∑S q
q
q
+ Ts = Tf
q
where Tf is the total available flexible time for an individual. The value of Tf is calculated as the difference between 24 h and the total time allocation to mandatory activities and associated travel. This problem can be solved by the Lagrangian method. The Lagrangian function (L) is ⎛ ⎞ L = ∑Uq + Us − λ ⎜ ∑ Tq + ∑ Sq + Ts − Tf ⎟ ⎝ ⎠ q q q where λ is the Lagrangian multiplier.
(4)
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Transportation Research Record 2135
Setting the partial derivatives with respect to Ts and Tq to be 0, one obtains 1 ⎧ ∂L ⎪ ∂T = T + 1 − λ = 0 s ⎪ s ⎨ ⎡⎣ x qβ + γ ln ( Sq + 1) + ⑀ q ⎤⎦ ⎪ ∂L −λ=0 ⎪ ∂T = Tq + 1 ⎩ q
(5)
The simultaneous equations represented by Equation 5 may be solved to obtain Tq + 1 = x qβ + γ ln ( Sq + 1) + ⑀ q Ts + 1
(6)
Thus, the unknown coefficients, γ and β, can be estimated through a simple linear regression model, where (Tq + 1)/(Ts + 1) is the dependent variable and xq and ln(Sq + 1) constitute independent variables. For in-home activities, the term ln(Sq + 1) is always equal to 0 and only xq needs to be specified into the linear regression model. As long as γ and β are estimated for each activity category, the utility expression in Equation 1 can be directly used to calculate the cumulative utility derived by an individual from all activity and travel time expenditures: U = ∑ Uq + U s
(7)
q
This value serves as a cumulative daily time use utility measure based on in-home and out-of-home time expenditures for nonmandatory activities and associated travel.
This study considered three types of constraints: temporal, monetary, and household. Temporal constraints relate to the limited availability of time. Here, employment status is used as a surrogate to represent temporal constraints. Presumably, those who are employed have greater temporal constraints (in terms of availability of time and flexibility to schedule activities during the day) than those who are not employed (20). Monetary constraints relate to the ability of an individual to allocate monetary resources to discretionary activities. It is conceivable that an individual may not be able to afford to participate in additional discretionary activities (even if time is available). Monetary constraints are represented by household income. It is recognized that monetary constraints are closely linked to employment status; those who are employed may have higher incomes. In this study, the interactions between these dimensions are considered to capture these potential effects. Finally, household constraints are represented by the presence or absence of children. Given that women bear a larger share of childcare responsibilities in many contexts, the presence of children may constitute household constraints that affect women’s time use patterns and associated utility (20). Once again, it is recognized that the presence of children may also affect monetary and temporal constraints. All interaction effects are considered in this study. However, it is not straightforward to isolate the impacts of these constraints on time use utility values because of the nature of these surrogate measures. Despite these complex interactions, it is useful to examine the impacts of these surrogate measures on time use utilities; in the rest of this paper, the surrogate measures and the respective constraints they are intended to capture are used interchangeably. In the original ATUS data set, activities were classified into an extensive list of categories or purposes that were aggregated to broader categories for brevity and to facilitate model estimation. Nine categories of activity travel engagement were considered in this study:
DATA DESCRIPTION Data from the 2005 American Time Use Survey (ATUS) were used to demonstrate application of the methodology. ATUS, conducted by the U.S. Census Bureau for the Bureau of Labor Statistics, is a comprehensive survey of in-home and out-of-home activity and time use patterns of a representative stratified random sample of the population of the United States. One person from each household aged 15 years or older was randomly selected to participate in the survey. The survey was evenly distributed over all months of the year and across all days of the week. It collected detailed socioeconomic and demographic information about households and individuals residing in households. All respondents were asked to provide detailed information about all activities and time use patterns for the “previous day” (yesterday) to maximize recall. The activities were classified into a very detailed and nested schema that allowed one to aggregate activities up to higher levels as appropriate. Extensive details about ATUS are available at the survey website (www.bls.gov/tus/). The empirical application in this paper is motivated by the considerable interest in the study of women’s travel issues and associated behaviors (15, 16). It has been found that women generally bear the larger share of childcare and household care responsibilities and spend more time on these activities than men (17, 18). Women drive children to various activities and thus have travel patterns quite different from those of men (19). As women tend to play distinct roles in the household, it is of interest to observe how various constraints affect their time use utilities.
1. Mandatory activities (e.g., work, education), 2. Sleep, 3. In-home maintenance activities (e.g., household activities, personal care), 4. Out-of-home maintenance activities (e.g., consumer purchases, civic obligations), 5. Travel for out-of-home maintenance activities, 6. In-home discretionary activities (e.g., eating, drinking, relaxing), 7. Out-of-home discretionary activities (e.g., sports, volunteer activities), 8. Travel for out-of-home discretionary activities, and 9. Other activities. Travel times were computed on a round-trip basis, which is done to attribute all travel to the out-of-home activity that motivated the travel in the first place. Adjustments were made to attribute travel appropriately to different out-of-home activities that were part of the same trip chain. The total of these nine categories accounted for the entire 1,440 min that constitute a 24-h period. Thus, these categories are comprehensive and mutually exclusive. Descriptive measures of time use are compared in Tables 1 and 2. Table 1 provides comparisons by employment status, income level, and presence of children for weekdays and weekend days. Households are grouped into low or high income according to whether the annual household income is less than or greater than $40,000, the rounded median household income in the sample.
TABLE 1
Average Time Allocation to Activities for Women by Group (min per day)
Group
Travel for Out-of-Home Maintenance Activities
Out-of-Home Maintenance Activities
In-Home Discretionary Activities
Travel for Out-of-Home Discretionary Activities
Out-of-Home Discretionary Activities
Other Activities
Sample Size
Mandatory Activities
Sleeping
In-Home Maintenance Activities
398 20 184 312 266 225 249
482 535 528 481 497 509 504
198 308 244 240 278 211 242
36 38 33 41 45 29 37
49 68 54 61 63 51 57
174 347 288 194 188 295 241
16 22 18 19 15 21 18
77 81 75 80 72 84 78
10 21 16 13 15 14 14
2,019 1,382 1,463 1,467 1,609 1,792 2,930
102 11 67 69 68 64 68
550 560 569 542 556 552 555
247 263 239 267 284 224 253
38 25 27 39 37 29 33
72 56 59 72 74 57 66
242 356 309 265 240 333 286
33 29 29 34 30 33 31
143 124 129 140 137 135 134
14 15 14 14 14 15 14
2,108 1,385 1,445 1,595 1,718 1,775 3,040
Weekday Employed Unemployed Low income High income Child No child Total Weekend Employed Unemployed Low income High income Child No child Total
TABLE 2
Average Time Allocation to Activities for Women by Subgroup (min per day)
Group
Travel for Out-of-Home Maintenance Activities
Out-of-Home Maintenance Activities
In-Home Discretionary Activities
Travel for Out-of-Home Discretionary Activities
Out-of-Home Discretionary Activities
Other Activities
Sample Size
Mandatory Activities
Sleeping
In-Home Maintenance Activities
381 363 396 408 384 441 19 40 9 26 37 12
503 508 498 472 471 474 550 556 547 506 503 511
186 210 165 202 236 156 292 361 259 354 403 288
32 44 22 37 43 28 33 41 29 52 55 47
46 56 37 53 59 44 60 65 57 86 87 84
187 160 211 166 150 188 373 277 419 275 221 348
16 14 18 16 15 18 20 16 22 26 22 31
79 75 82 76 71 83 71 60 76 95 89 102
10 11 10 10 12 8 21 24 20 19 22 16
670 314 356 1,099 633 466 793 259 534 368 211 157
122 106 140 90 84 98 11 7 13 16 19 13
560 563 557 543 545 541 578 596 569 538 532 545
229 257 197 261 279 235 249 298 224 280 323 234
33 33 32 42 44 38 21 29 17 32 35 29
64 67 61 74 78 68 54 71 45 65 81 48
250 226 277 240 224 264 369 281 414 327 284 372
31 30 32 33 33 33 27 24 28 34 26 42
137 142 132 143 140 148 119 121 118 130 121 140
14 16 12 12 11 14 13 13 13 19 19 18
736 391 345 1,139 678 461 709 239 470 456 235 221
Weekday Empl Lo Inc Child No child Empl Hi Inc Child No child Unempl Lo Inc Child No child Unempl Hi Inc Child No child Weekend Empl Lo Inc Child No child Empl Hi Inc Child No child Unempl Lo Inc Child No child Unempl Hi Inc Child No child
NOTE: Empl = employed, lo = low, hi = high, inc = income, and unempl = unemployed.
Ye, Konduri, Pendyala, and Sana
The descriptive statistics generally show a pattern consistent with expectations. Women who are employed spend more time for mandatory activities, including work and school. On weekdays, the difference is naturally much larger than on weekends, although the difference on weekends is still substantial, presumably due to weekend workers and students. The sleeping duration shows similar patterns. Unemployed individuals spend more time for in-home maintenance activities because they may be taking on a greater share of the household chores. The difference, however, is considerably smaller on weekends. It is interesting to note that travel for maintenance activities out of home shows considerable similarities. On average, travel time expenditures are virtually identical on weekdays; on weekends, employed individuals participate in more travel for out-of-home maintenance activities. It is possible that employed women have higher levels of income, facilitating greater participation in those activities. Also, unemployed women may be constrained by household obligations and therefore their travel for out-of-home maintenance is moderated. A similar trend is observed with respect to allocation of out-of-home maintenance activity time. Unemployed women allocate substantially larger amounts of time to in-home discretionary activities such as watching television. These differences are observed on weekdays and weekend days, although the differences are more modest on weekend days. Differences with respect to time allocation for out-of-home discretionary activities, on the other hand, are far more modest. In general, it appears that employment affects in-home maintenance and discretionary activity time allocation far more than it affects out-of-home maintenance and discretionary activity time allocation (and associated travel). Similarly, comparisons across income groups show trends consistent with expectations. People in the higher-income category spend more time working or on education, particularly on weekdays. Lowerincome individuals sleep longer (presumably because fewer are employed). Income does not appear to be a distinguishing characteristic for in-home maintenance activity time allocation. However, income does play a role in determining out-of-home maintenance and discretionary time allocation (and associated travel). For all variables corresponding to these measures, lower-income individuals allocate less time. Lower-income women spend more time on in-home discretionary activities, partially due to confounding employment effects and partially due to income effects. The presence of children is primarily associated with differences in maintenance activity engagement. One would expect women to engage in more out-of-home maintenance activities to drive children to and from various activities (and school), make purchases, and run other errands to take care of children’s needs. Similarly, large differences are observed in in-home maintenance activity time allocation. People with children spend about 1 h more (on average) than those in households with no children. In total, maintenance activity engagement (whether in home or out of home) for women with children exceeds that for those without children by about 1.5 h. On the other hand, women with children in the household spend substantially less time (about 1.5 h) on in-home discretionary activities, virtually accounting for the maintenance activity effect. With respect to out-of-home discretionary activities, people with children spend a modestly smaller amount of time than those without children. These three socioeconomic variables influence activity time allocation patterns in unique ways, although there are some clear interaction effects, particularly between employment and income. Table 2 provides descriptive statistics for groups of women cross-classified by the dimensions of interest to illustrate the interaction effects among
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these socioeconomic attributes. Eight groups may be defined by employment status (employed or unemployed), income (low or high income), and children (presence or absence). For the sake of brevity, an exhaustive discussion on the trends observed in this table is not presented. However, the trends illustrate the nature of the interactive effects among employment status, presence or absence of children, and household income in influencing time allocation to activities. For example, unemployed high-income women with children in the household spend the largest amount of time on in-home and out-ofhome maintenance activities and associated travel on both weekdays (403 min) and weekends (323 min). On the other hand, unemployed low-income women without children in the household spend the largest amount of time on in-home discretionary activities on both weekdays (419 min) and weekends (414 min). Also, unemployed high-income women without children in the household spend the most time on out-of-home discretionary activities (102 min) and travel (31 min) on weekdays and the most time on travel for discretionary activities on weekends (42 min). How do overall time use utility measures differ across these socioeconomic variables of interest (which clearly result in different time use patterns)? The empirical application sheds light on how the methodology presented here can be used to answer such questions. MODEL ESTIMATION RESULTS The time use allocation models represented by Equation 6 are estimated separately for out-of-home maintenance, out-of-home discretionary, in-home maintenance, and in-home discretionary activities. Model estimation results are presented in Table 3 and are discussed in this section. The sample sizes in the models correspond to the number of women for whom complete information is available on all explanatory variables included in the models. The constant in the out-of-home maintenance activity model is negative, suggesting that the overall tendency of women is not to engage in out-of-home maintenance activities. However, interpretation of the constant is often not clear-cut, and caution should be exercised in drawing any firm conclusions. Travel time is associated with a positive coefficient, suggesting that travel contributes positively to the time use utility measure associated with nonmandatory activity engagement patterns. Individuals travel farther to more preferred destinations to increase the level of utility they can derive from the experience. In other words, travel time is potentially acting as a surrogate for the desirability and quality of experience at the destination. This positive coefficient may also be due to the absence of accessibility measures in the model specifications or it may reflect the positive utility of travel. It is not possible within the scope of this analysis to isolate the contribution of each of these sources to the positive coefficient associated with travel time. In addition, one does expect to see diminishing returns characterized by a nonlinear relationship. In this particular context, no such relationship was found, but the existence of such a relationship is worthy of additional exploration. More time is allocated to out-of-home maintenance on weekends, consistent with the notion that many shopping-related activities and child extracurricular activities tend to be undertaken on weekends. The constant for the in-home maintenance activity model is positive, suggesting that women have a proclivity to engage in this activity, presumably to take care of household obligations. The weekday coefficient is negative, implying that more in-home maintenance activity time allocation occurs on weekdays than on weekends. Students spend less time on in-home maintenance activities, whereas those in multiperson households and in metropolitan areas spend more time for
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Transportation Research Record 2135
TABLE 3
Model Estimation Results
Variable
Out-of-Home Activity
In-Home Activity
Coefficient
Coefficient
t-Statistic
t-Statistic
Maintenance Activity Constant ln(travel time + 1) Day of week (weekday = 1; weekend = 2) Two or more persons ≥35 years in household Student (school or college) Resides in metropolitan area Only one person between 17 and 35 years in household Adjusted R2 Sample size
−0.0302 0.0554 0.0235 — — — −0.0119 .2450 6,894
−3.87 47.28 5.26 — — — −2.19
0.4974 — −0.0240 0.0372 −0.1192 0.0426 — .0047 6,860
15.87 — −1.72 2.65 −4.30 2.45 —
−0.0064 0.1034 0.0334 −0.0140 — — — — .2653 6,894
−.50 48.23 4.26 −1.81 — — — —
0.3812 — 0.0515 — −0.1540 0.0408 −0.0732 0.0448 .0186 6,860
12.34 — 3.98 — −5.90 2.53 −4.49 5.34
Discretionary Activity Constant ln(travel time + 1) Day of week (weekday = 1; weekend = 2) Two or more persons ≥35 years in household Student (school or college) Resides in metropolitan area Only one person between 17 and 35 years in household Number of persons ≥35 years in household Adjusted R2 Sample size NOTE: — = variable is not included in the model.
in-home maintenance. It is possible that the latter groups have greater levels of household obligations that contribute to these findings. The goodness-of-fit for this model is quite poor, suggesting that other unobserved factors influence in-home maintenance activity time allocation. With a few dummy variables serving as explanatory variables, it is very difficult to obtain higher R2 values in disaggregate regression models with large sample sizes. This model is sufficient for the purposes of this paper, which is intended to demonstrate the methodology. However, in light of the poor fit, results should be interpreted with caution. For the out-of-home discretionary activity model, the constant is negative but statistically insignificant. Travel time is once again associated with a positive coefficient, with three possible reasons contributing to the positive sign. Travel time may be serving as a surrogate for the quality of the destination, travelers may derive positive utility from the travel experience itself, and the absence of accessibility measures in the model specification may be leading to a positive sign for the travel time variable. The positive coefficient associated with the weekend variable may be due to the higher levels of discretionary activity engagement that occur on weekends. The presence of multiple persons is associated with a negative coefficient, suggesting that women in multiadult households are likely to have more household obligations (see the positive coefficient in the in-home maintenance activity model) and less time for discretionary activities. The model for in-home discretionary activity time allocation shows that students spend less time on these activities, presumably because they have to take care of school work. A higher level of time allocation to in-home discretionary activities occurs on weekends, as individuals presumably relax at home. Those in metropolitan areas and multiperson households spend more time on in-home dis-
cretionary activities as evidenced by the positive coefficients on these covariates. A single young person, on the other hand, allocates less time to in-home discretionary activities, possibly because there are no other young adults to engage in such activities at home. However, this variable did not appear to be statistically significant in the out-of-home discretionary activity time allocation model. Overall, the models offer plausible results consistent with those reported previously (9) and provide a basis to compute time use utility measures. The models were used to compute time use utility values for women cross-classified by employment, presence or absence of children, and high or low income. The results of the model application are presented in the next section.
COMPARISON OF WELFARE MEASURES The time use pattern of each individual (daily time allocations to various activities and travel) can be used to determine total time use utility values and the contributions of various activity categories to the total time use utility. Equation 7 serves as the basis for this computation. It is found, for example, that the largest components of total time use utility for unemployed high-income women with children are inhome (2.95 units) and out-of-home (0.68 unit) maintenance activities. Unemployed high-income women without children have the highest contribution of utility from their time allocation to out-of-home discretionary activities (1.26 units), presumably because they do not have the same level of household obligations that the prior category of women do. Unemployed low-income women without children have the highest contribution from in-home discretionary activity time allocation
Ye, Konduri, Pendyala, and Sana
67
(3.21 units), which is consistent with expectations as unemployed low-income women are unlikely to be able to afford to participate in out-of-home discretionary activities; also, the absence of children reduces the need to allocate time to in-home or out-of-home maintenance activities. The source of their time use utility is primarily the large time allocation to in-home discretionary activity engagement, such as watching television. The contribution of sleep time allocation to time use utility is substantial (6.2–6.32 units) but varies by only a small amount (0.12 unit) across the entire survey sample of women. On the other hand, the range of utility contributions associated with various maintenance and discretionary activities is quite high because of interperson variability in time allocation. For out-of-home maintenance activities, the range of utility contribution is 0.29 unit; the corresponding value for in-home maintenance activities is 0.50 unit. The ranges for out-of-home and in-home discretionary activities are 0.40 and 0.85. One notices a clear order of the range of activity time allocation contribution to overall time use utility. Sleep, an activity that shows little variation across the groups of women, has the smallest range. Maintenance activities show a greater range, while the most flexible discretionary activity category shows the greatest range of utility contributions. These findings are consistent with the descriptive statistics in Tables 1 and 2. Total time use utility values were computed for all women in the sample and average values of time use utility computed for each of the eight groups. The groups are presented in descending order of time use utility in Table 4. The overall average across the sample is 13.27 units. Women with the fewest constraints have the highest time use utility measure of 13.97 units. This group of women is unemployed (no work constraints), has a high income (fewer monetary constraints), and no children (fewer household constraints). The group with just the household constraint (children) added has the next best utility followed by that with just monetary constraints. In this case, the income effects appear to be moderated by the absence of children because less income is spent on childcare and child-related activities. The fourth-ranked category is that of women who are employed, high-income, and have children. This group has temporal and household constraints but also has the income to outsource some maintenance activities and free up time TABLE 4 by Group
Average Time Use Utility Values for Women
Woman Group Unemployed, children absent, high income Unemployed, children present, high income Unemployed, children absent, low income Employed, children present, high income Unemployed, children present, low income Employed, children absent, high income Employed, children present, low income Employed, children absent, low income Total All, employed All, unemployed Difference All, low income All, high income Difference All, child present All, no child Difference
Time Use Utility 13.97 13.70 13.48 13.31 13.25 13.15 12.87 12.80 13.27 13.08 13.57 0.49 13.13 13.40 0.27 13.24 13.31 0.07
for discretionary activities. These households may outsource items such as house cleaning and yard work to outside service providers. Also, the presence of children and a high income facilitate the pursuit of such pleasurable activities. After the fourth-ranked group, it is clear that time use utility decreases as the level of constraints increases. Those who have income constraints coupled with household constraints (presence of children) come next in the list. The bottom three categories are all employed women groups, suggesting that the presence of temporal constraints imposed by work schedules leads to lower time use utility measures regardless of income and the presence or absence of children. Those who have temporal constraints but no household and income constraints rank third from the bottom. The group that is subject to all three constraints—temporal, household, and income— ranks second from the bottom. It appears that the presence of children may actually enhance time use utility because households engage in some pleasurable travel and activities for and with the children. Overall, it is clear that the removal of temporal constraints and the reduction of monetary constraints lead to activity travel patterns with higher utility values. The effect of the presence of children is more ambiguous; two of the top four categories have children, but the other two categories do not. It appears that children contribute positively in some ways to engaging in nonmandatory activities and travel that raise utility, but they also impose constraints and maintenance activity needs that moderate their utility enhancement effect. The bottom half of Table 4 further illustrates this effect. The difference in utility measures across employment categories is the highest, while that between the children categories is the smallest. In addition to comparing averages, the authors examined distributions of time use utility measures between pairs of groups (defined along single dimensions of employment status, presence of children, and income). For the sake of brevity, the charts are not presented; comparisons of distributions simply corroborated what was found in the comparison of averages in Table 4. The distributions were found to differ the most between employed and unemployed women and the least between those with and without children.
CONCLUSIONS The emergence of activity-based microsimulation approaches to travel demand modeling has ushered in a new era in transportation policy analysis—one that is characterized by the ability to evaluate and forecast changes in human activity travel patterns in response to a policy intervention. As activity-based microsimulation approaches model travel at the level of the individual traveler, it is possible to simulate the activity travel rescheduling, adaptation, and learning process that takes place for each individual and household and evaluate the overall impacts of a policy intervention. Activity-based travel model systems are capable of simulating activity travel patterns at the level of the individual traveler for a wide variety of socioeconomic, demographic, policy, and system scenarios. When scenario or policy analyses are being performed, two complementary needs arise. First, travel modelers are interested in the ability to represent the process in which travelers consider alternative activity travel patterns (under a new scenario) and then choose (or settle into) a new activity travel pattern. Presumably, travelers will evaluate the alternative activity travel schedules and then choose the one that provides a higher level of utility. Second, transportation planners are interested in the ability to perform equity analysis. Planners
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are interested in determining whether the utility or satisfaction that people derive from their activities and travel will increase or decrease in response to a policy intervention. A mechanism that allows the quantification of this utility based on time allocation patterns would be useful in assessing quality-of-life implications of alternative policies. In this paper, a methodology was developed for measuring utility or welfare associated with activity travel patterns, while taking into account time allocation to activities both in home and out of home and travel time expenditures for out-of-home activities. Activities are classified into maintenance and discretionary activities and their associated travel (if the activity is out of home). The formulation results in a series of simple regression models that can be used to estimate utilities associated with time allocations to various activity categories. The application context in the paper focused on using the time use utility measures to assess the impacts of various constraints on time use utility measures for women. With time use data from the 2005 ATUS, utility measures were computed for women subsamples stratified by employment, income, and the presence or absence of children. Comparisons across the eight categories of women groups provided valuable insights into the role of temporal (employment) constraints, monetary (income) constraints, and household obligation (children) constraints. In general, it was found that temporal constraints (represented by employment status) had the largest adverse impact on time use utility, monetary constraints (represented by income) had the second largest adverse impact, and household obligation constraints (represented by children) had a very small adverse impact. Average utility measures were generally found to be larger for the unemployed, higher-income groups, suggesting that relaxation of work constraints and monetary constraints leads to adoption of activity travel patterns that offer higher utility values. This finding is perfectly consistent with expectations and does not necessarily constitute a new set of findings per se; what it does demonstrate, however, is that the enhanced time use utility measure formulated in this paper offers plausible and behaviorally consistent results. From a social policy perspective, the findings clearly suggest that relaxing constraints will enhance people’s ability to engage in activity travel patterns that offer higher time use utility values. Employeefriendly workplace policies—characterized by flexible work hours, telecommuting, and so on—could help ease temporal constraints (without increasing monetary constraints) and lead to activity travel patterns with higher time use utilities. This situation may particularly be true for women, who routinely take on a greater share of household maintenance activities. In rapidly developing countries, it is clear that the easing of temporal and monetary constraints (and even child constraints) is leading to transformative changes in activity travel patterns that clearly offer higher time use utilities to people. In many of these countries, standards of living (income levels) are rising, household sizes are decreasing, and workplace policies are becoming more flexible with the rapid penetration of technology. Moreover, institutions and stores are open longer hours and on weekends, the rapid rise in vehicle ownership is facilitating faster travel (compared with slower nonmotorized modes), and technology is facilitating multitasking and activity engagement any time and any place. Although the rapid rise in travel demand is a concern from a congestion and air pollution standpoint, it appears that people are shifting to activity travel patterns that offer higher time use utility measures. The methodology in this paper provides the ability to quantify these welfare benefits and weigh them against societal costs of congestion and transportation externalities.
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