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A Nested Logit Model of Commuters' Activity Schedules
A Nested Logit Model of Commuters' Activity Schedules
Sachin Gangrade
ZS Associates
Ram M. Pendyala*
University of South Florida
Robert G. McCullough
Florida Department of Transportation
This paper presents a nested logit model of activity scheduling behavior that can be used to predict a daily activity pattern for commuters. The behavioral paradigm embodied in the model suggests a two-stage decision process in which commuters first plan or identify the nonwork activities that need to be undertaken during the day, and second, schedule these activities in relation to the work activity schedule. Three possible scheduling periods are considered in the model: before work, at work, and after work. Alternative nested logit model structures are estimated on the 1996 San Francisco Bay Area activity survey sample to identify a plausible and statistically acceptable structure. Numerical examples are presented to show how the model, when combined with a Monte Carlo simulation and simple heuristics, can be used to generate daily activity schedules for commuters.
The conceptual deficiencies of the conventional four-step trip-based travel demand modeling procedure, combined with the shift in transportation planning and policy initiatives from transportation infrastructure development to transportation systems management, have led to the emergence of activity-based approaches to modeling individual travel behavior (Jones et al. 1983; Kitamura 1988; Axhausen and Gärling 1992). Activity-based analysis follows the premise that travel demand is a derived demand and the resulting travel demand models are applicable to a wider range of situations than the conventional four-step trip-based procedure (Ettema and Timmermans 1997). The activity-based perspective of travel can also reliably be used to evaluate travel demand management policies, because it explicitly models activity patterns and considers these patterns to be the fundamental influence on individual travel decisions (Golob 1998; Kuppam and Pendyala 2001).
The activity-based approach has seen substantial development in the past few years (Kitamura and Fujii 1998). Bhat and Koppelman (1999) broadly classified these developments into activity time allocation studies and activity episode analysis studies. Activity time allocation studies classify activities into one of the several categories available and then examine the time allocated to these activity types based on socioeconomic and demographic characteristics of individuals (Kitamura 1984). However, these approaches ignore the context in which individuals pursue activities; that is, these approaches do not consider the time of day of the activity performance, the sequence in which activities are performed in a continuous temporal domain, and the location characteristics of activity participation (Kitamura et al. 1997b).
Activity episode analysis is closer to the original theory behind activity-based approaches. This approach analyzes activities and trips and their associated spatial and temporal constraints in a comprehensive model framework. These studies describe in detail the sequence, context, and duration of activity participation, thus leading to more detailed models of activity choice and travel behavior. Many of the earlier activity episode analysis studies focused on participation of individuals in one or more activity episodes, along with one or more accompanying characteristics of the episodes such as duration, location, or time window of activity participation (Damm 1982; Hamed and Mannering 1993; Bhat 1996, 1998; Bhat et al. 1999).
Over the last decade, several researchers have developed models that attempt to microsimulate the daily activity participation and trip schedules of individuals (Chen et al. 1999). Examples include, but are not limited to, STARCHILD (Recker et al. 1986), SCHEDULER (Gärling et al. 1994), and AMOS (Kitamura et al. 1995; Pendyala et al. 1998). The development of such models has been further accelerated by the availability of data that capture household activity scheduling behavior (Doherty and Miller 2000). Development of tour-based model frameworks is another line of research in activity-based modeling, where "tours" are considered to be the basic unit of travel. These approaches use a combination of multinomial logit and nested logit models to simulate activity-travel patterns in the context of tours (Bowman and Ben-Akiva 1997; Wen and Koppelman 1999, 2000).
Activity-based approaches are the foundation of the next generation of travel demand models in the United States and other countries. The objective of this paper is to contribute to the operationalization of the activity-based approach by proposing a simple analysis framework to generate weekday activity engagement and trip scheduling patterns of commuters. Using data from the 1996 San Francisco Bay Area activity survey, nested logit models of activity scheduling behavior were developed and estimated. The proposed nested logit models, though not comprehensive or exhaustive, are practical, provide a plausible behavioral basis, and present activity scheduling and travel behavior of commuters in a simple model system. We believe that the nested logit models of activity scheduling behavior proposed in this paper can be effectively combined with other models of activity behavior (e.g., models of activity frequency and activity duration) and rule-based algorithms (e.g., Pendyala et al. 1998) to develop a full-fledged activity-based model system.
The remainder of this paper is organized as follows. First, we provide a brief description of the survey sample. We then describe the overall modeling approach and nested logit methodology, respectively. Descriptions of the nested logit model specification and estimation results follow. We then present an example of how the nested logit model can be used to predict a commuter's activity pattern. Conclusions are drawn in the final section.
This research paper utilizes activity and trip information collected as part of the 1996 San Francisco Bay Area activity survey. Gangrade et al. (2000) provide a detailed description of the survey and sample characteristics. Only a brief summary is provided here. A two-day activity-based time-use and travel survey was conducted in the nine counties of the San Francisco Bay Area in 1996. Detailed information on both in-home and out-of-home activities and trips undertaken by an individual were recorded.1 Information was requested for all out-of-home activities and trips but only in-home activities of 30 minutes or more. However, many respondents provided detailed information on all in-home activities regardless of their duration.
The original survey dataset includes a sample of 8,817 individuals residing in 3,919 households who provided detailed activity and trip information over a 48-hour period. After extensive data checking, cleaning, and merging/organizing, the final dataset obtained for use in this study included 7,982 individuals residing in 3,827 households. The dataset identified 4,331 persons as commuters (3,651 persons were noncommuters).
Sample Profile
Table 1 presents the demographic characteristics of households in the sample. The average household size was 2.3 persons per household, while the average number of workers was 1.4 per household. Forty percent of the households were single-worker households, while another 43% were multiple-worker households.
The income variable, which categorized households into low (less than $30,000), medium ($30,000$75,000), and high (greater than $75,000) income groups, showed as expected that a large percentage of the households in the survey fell into the medium income bracket. Average car ownership in the sample was 1.9 vehicles per household, and 86% of the households in the survey sample have vehicle ownership levels greater than or equal to the total number of commuters in the household. This is indicative of a high level of commuter auto availability.
Person characteristics (provided separately for commuters and noncommuters) of the sample are also shown in table 1. Respondents were categorized into young (29 years or less), middle (30 49 years), and old (50 years or more) age groups. More than 50% of the commuter sample fell into the middle age bracket, while over 50% of the noncommuter samples fell into the young age group (the noncommuter group includes children). More than 80% of the commuters were full-time workers. Also, as expected, the percentage of licensed drivers in the commuter sample was substantially higher than that in the noncommuter sample (95% versus 49%).
Clearly, the noncommuter sample is quite different from the commuter sample. In light of these differences, one would expect noncommuters to have substantially different activity and time-use patterns than commuters. These differences call for the development of separate models of activity scheduling behavior for commuters and noncommuters. In this paper, out-of-home nonwork activity engagement and trip scheduling behavior is modeled only for the commuter sample, and thus the remainder of this paper focuses exclusively on the commuter sample.
Activity Participation and Trip Frequency Analysis
The original dataset had more than 30 categories of activities broadly aggregated into 11 activity types in order to study activity and trip frequencies by purpose. Average frequencies (including both in-home and out-of-home activities) for the commuter sample are presented in table 2. As mentioned earlier, the Bay Area travel survey was conducted over a 48-hour time period. The activity and trip frequencies presented in the table are two-day averages.
The average number of work and work-related activities was 1.6 activity episodes per day for the Bay Area commuter. This value is along expected lines, because many commuters undertake two work activity episodes in a dayone before lunch and one immediately after lunch.
As expected, eating/meal preparation (eat/meal), in-home entertainment, personal care and childcare, and sleep/nap activities averaged one or more activity episodes per day. On the other hand, shopping and personal business, out-of-home entertainment, and out-of-home other activities averaged less than one activity episode per day.
With respect to travel, commuters, on average, took 4.8 trips per day. Table 2 shows trip rates by purpose for both commuter and noncommuter samples in the dataset. Work/work-related and return home trips comprise almost 50% of the trips taken by commuters over a day. The average trip frequency for out-of-home shopping/personal business, entertainment, and maintenance/other related activities was found to be about 0.5 trips per day for each purpose. The average trip frequency associated with childcare was rather low at 0.1 trips per day.
Overall, work activity was shown to be a major part of a commuter's daily activity and travel pattern. In the case of commuters, one may conjecture that other discretionary activities (e.g., shopping, personal business, recreation, and childcare) are scheduled and performed around relatively fixed work schedules. This hypothesis, which is consistent with the literature on activity-based approaches (see, e.g., Damm 1982) forms the basis for the nested logit model of activity scheduling developed in this paper.
In this paper, nested logit models are formulated with a view to predict activity and trip schedules of commuters over a one-day period. This section provides the behavioral framework underlying the specification of the nested logit models of commuter activity scheduling behavior.
There is increasing interest in applying microsimulation approaches to forecast activity-based travel demand (Kitamura et al. 1997a; Pendyala et al. 1998). In microsimulation approaches, the researcher is often attempting to simulate, at the level of the individual traveler, an entire activity schedule and travel itinerary over the course of a day. This involves modeling a series of choices that travelers make, including those related to activity type, duration, timing and scheduling, location and destination, and path. Considering that many of these choices are made under constrained situations, the argument can be made that there are finite spatio-temporal action spaces or space-time prisms within which one can engage in activities and travel (Pendyala et al. 2002).
Space-time prisms provide a means of representing the spatial and temporal constraints that influence activity and travel patterns. For example, from a spatial standpoint, one can conjecture that home and work locations are potential anchors that constrain the potential range of destinations a person can visit. Because of data limitations, this paper does not consider the spatial aspect of commuter activity scheduling behavior.
From a temporal standpoint, several events in time may constrain the range of activity-travel patterns that an individual can pursue. These events and their associated beginning and ending times can play an important role in determining how individuals schedule, sequence, and plan their activities and trips. Six temporal events that might dictate how a commuter schedules and plans activities are identified here.
- Wake-up time
- First time of departure from home
- Work start time
- Work end time
- Final time of arrival at home
- Sleep time
Gangrade et al. (2000) have provided detailed descriptions of these temporal events for the commuter and noncommuter groups in the survey sample. These six events potentially define five temporal prisms2 within which commuters schedule their activities. For example, wake-up time and first time of departure from home define an "initial at-home prism." Similarly, work start time and end time define an "at-work prism." In reality, these events may not truly describe the temporal dimension of a prism. The real vertices (or extremities) of a prism are unobserved (e.g., earliest wake-up time, latest possible work arrival time, earliest possible work departure time, latest possible sleep time), and therefore the observed events are used as surrogates to represent the temporal dimensions of prisms.
A commuter can engage in out-of-home activities within prisms that lie between the first time of departure from home and the final time of arrival at home. Only in-home activities can be pursued prior to the first home departure and following the last home arrival. The period between the first home departure and the final home arrival may be further subdivided into:
- Before-work time period: This time period comprises time available between the first departure from home and work start time. During this period, a commuter may pursue activities on the way to work and/or pursue activities and return home prior to departing for work.
- During-work time period: This time period is defined by the work start time and end time. On average, work accounts for approximately 30% of a commuter's day and 50% of the waking hours. As such, this prism is likely to be an important determinant of a commuter's daily activity pattern. Commuters are often temporally constrained by their work schedules. Nonwork activity engagement typically occurs during the lunch break (about one hour for most commuters).
- After-work time period: The time period available to a commuter after work ends and prior to the final time of arrival at home constitutes the after-work time period. During this time period, a commuter may undertake nonwork activities such as shopping, running errands, recreation, etc., either on the way home from work or separately after a temporary stop at home. The latter choice of activity engagement would generate another set of trips before the commuter finally returns home.
Within the framework adopted in this paper, we postulate that commuters choose to engage in out-of-home nonwork activities within one or more of these broad prisms. For example, a commuter may choose to shop before work, during work, or after work, including the possibility of multiple shopping activities in the same or different time prisms. By scheduling out-of-home nonwork activities in various prisms (periods of the day), a commuter's activity schedule can be identified.
For purposes of model development and estimation, we aggregated the various out-of-home nonwork activities undertaken by commuters in the 1996 Bay Area survey sample broadly into eat/meal preparation, shopping/personal business, and entertainment/social recreation. In addition, in an attempt to capture temporary trips home that occur between the first home departure and last home arrival, an additional activity category called "return home" is included in the model formulation. It should be noted, however, that return home really represents in-home activity engagement by commuters in the survey sample. Previous research has clearly shown that there are significant tradeoffs and complementarity between in-home and out-of-home activity engagement (Kitamura 1984; Kuppam and Pendyala 2001). Within the model framework of this paper, relationships between in-home and out-of-home activities and the identification of specific activities pursued during trips home are not explicitly included. Future efforts will involve the integration of such models with the model developed in this paper. However, the inclusion of return trips home as an explicit category in the model framework helps determine the activity sequencing and trip chaining behavior of commuters (e.g., does a commuter shop on the way home from work or after returning home from work?).
In summary, the behavioral framework adopted in this paper takes the form of a two-stage process. In the first stage, commuters choose among activities to be pursued (outside home and work) and in the second stage, they choose when the activity will occur. Such a two-step process may be conveniently represented using nested logit model structures. For example, in one postulated structure, the various out-of-home nonwork activities pursued by individuals, namely eat/meal preparation, shopping/personal business, entertainment/social recreation, and return home comprise the upper level (composite) alternatives. The three time periods available to pursue these activities form the lower level (elemental) alternatives available to an individual. Figure 1 provides a visual depiction of this postulated behavioral structure.
It should be noted that the two-stage behavioral paradigm suggested in this paper is not necessarily the only factor motivating the adoption of a nested logit modeling methodology. A nested logit model is usually adopted when there is a potential for shared unobservable attributes across alternatives, if these attributes were to be arranged in a simple multinomial structure. The potential for shared unobserved attributes across alternatives, coupled with the two-stage behavioral paradigm, motivated us to adopt the nested logit methodology in this paper.
Because the behavioral framework adopted in this paper does not include in-home versus out-of-home activity substitution, and instead uses an aggregate activity-type categorization, it is not comprehensive in its treatment of commuter activity and travel behavior and thus simplifies the behavioral process underlying activity and travel pattern formation. Nevertheless, it provides a practical and convenient way to extract activity schedules and sequences and trip chains of commuters given standard socioeconomic variables.
The nested logit model is a widely used form of the discrete choice model and has been extensively presented and described in the literature (see e.g., Ben-Akiva and Lerman 1985; Lerman 1984; Train 1986; Ortuzar and Willumsen 1994). There are at least two ways to express the nested logit structure, namely, the Non-Normalized Nested Logit model (NNNL, described by Daly 1987) and the Utility Maximizing Nested Logit model (UMNL, described by McFadden 1978). A detailed discussion of both model structures is beyond the scope of this paper; however, the merits and demerits of the two model structures have recently been discussed in the literature (Koppelman and Wen 1998; Hensher and Greene 2000).
Despite the potentially more appealing nature of the UMNL model, the NNNL model specification is used in this research primarily because of the availability of convenient software to estimate it (e.g., LIMDEP). Also, from a behavioral interpretation standpoint, it was considered sufficient to adopt the NNNL modeling methodology.
The lower level choice in a nested logit model is a multinomial logit choice and can be expressed as
P ( k | i ) is the probability of alternative k from subset D i to be chosen on the condition that alternative i on the upper level has been chosen,
D i is the lower level choice set, which is associated with alternative i on the upper level,
V ik is the deterministic portion of the utility associated with choice k in nest i ,
is a vector of model parameters,
x is a vector of exogenous variables.
An inclusive value I i (or logsum) associated with the upper level alternative i is defined as
The upper level choice probability is then expressed as
P ( i ) is the probability of choosing alternative i ,
is a vector of model parameters,
z is a vector of exogenous variables.
The parameter is referred to as the inclusive value parameter. The value of this parameter should lie between zero and one. When the parameter equals unity, the structure collapses to a multinomial logit model without a nested structure. The levels are separated and present independent and separate choice situations if the value of the parameter is equal to zero. If <0, an increase in the utility of an alternative in the nest (which should increase the probability of the nest being chosen), actually diminishes the probability of selecting the nest. In virtually all choice modeling situations, this is implausible. If >1, an increase in the utility of an alternative in the nest not only increases its selection probability but also the selection probability of the rest of the alternatives in the nest. That is, improvements in one alternative could increase not only the probability of that alternative being chosen, but some other alternatives would also gain a bigger share (Ortuzar and Willumsen 1994). While this may be plausible under certain limited conditions, it is generally not applicable to a wide variety of choice modeling situations. Therefore, the nesting structure that provides inclusive value parameter estimates between zero and one is generally adopted as long as the structure offers a plausible behavioral framework and interpretation.
Several possible alternative nesting structures may describe the activity scheduling behavior of commuters. This section discusses the alternative nested logit model structures that were tested and presents model estimation results for the structure that provided desirable statistical and plausible behavioral indications.
Figure 1 illustrates the nested logit model structure that is most consistent with the behavioral framework postulated earlier in the paper. This structure suggests that a commuter, in formulating an activity schedule, first chooses the out-of-home nonwork activities to be pursued in a day. The choice of the appropriate time period in which to undertake each of the chosen activities comprises the second step in the choice process.
Nested logit model estimation results for this structure are found to offer plausible coefficient estimates, except for those associated with the inclusive value parameters. Because the model did not offer acceptable inclusive value parameter coefficients, it was not adopted, and therefore detailed model estimation results and parameter estimates are not included in the paper. The inclusive value parameter estimate for the nest comprising eat/meal alternatives was 1.45, while that for the nest comprising shopping/personal business alternatives was 1.09 (significantly different from one). These two inclusive value parameter estimates suggest that when the probability of a commuter pursuing either an eat/meal activity or a shopping/personal business activity in one of the three time periods increases, then the probability that the commuter undertakes the same activity in a different time period (on the same day) also increases simultaneously. While this result may hold true for a few commuters, it is not likely to hold true across the sample. The inclusive value parameter estimate for the nest comprising entertainment/social recreation activities was 0.51. This value indicates that a potential tradeoff is involved when pursuing entertainment/social recreation activities during different time periods in a day. This inclusive value is certainly behaviorally intuitive as one would expect commuters to trade off the pursuit of entertainment activities across different time periods.
Because the nested structure shown in figure 1 provided counter-intuitive inclusive value coefficient estimates for two nests, an alternative structure was developed (shown in figure 2). This framework proposes a bottom-up decisionmaking process where a commuter first breaks up the day into various periods (prisms) and then chooses the activity (or activities) to be undertaken in each period. In figure 2, the before-work, at-work, and after-work time periods comprise the three composite alternatives placed at the upper level in the nest structure. The various out-of-home nonwork activities undertaken by individuals (eat/meal, shopping/personal business, and entertainment/social recreation) comprise the elemental alternatives in each nest. However, return home is still retained as an upper level choice as this alternative pertains to the choice to return home temporarily during the day. Because it was considered appropriate to distinguish between out-of-home activity scheduling (in the other three nests) and trip scheduling (for in-home activities), return home was retained in a manner similar to that in the first nesting structure in figure 1.
This model offered plausible coefficient estimates and acceptable goodness-of-fit measures. However, similar to the first structure, the inclusive value coefficient estimates for two nests significantly exceeded one. For the nest comprising activities undertaken before work, the inclusive value coefficient estimate was 1.40, while that for the nest comprising activities pursued after work was 1.29. These inclusive values imply that commuters who are likely to engage in a nonwork activity before work or after work are also likely to simultaneously engage in other activity types during the time period under consideration.
One could posit that these model results are plausible, particularly in the context of the after-work period. During the after-work periods (typically in the evenings), quite a few commuters engage in multiple activities, suggesting that elemental choice alternatives in the after-work nest are not competing but complementary in nature. However, in the presence of household, work, and other institutional and temporal constraints, it is unlikely that this will apply across the entire sample. Also, with respect to the before-work period (typically in the morning), it is very unlikely that commuters treat activities as complementary to one another (with possibly a few exceptions). If this is the case, then, the inclusive value parameter estimate for the before-work nest should be less than one, even if that for the after-work period is acceptable.
In addition, the inclusive value coefficient estimate for the nest comprising activities undertaken while at work was 1.03. This was not significantly different from one at the 95% confidence level, suggesting that activities undertaken while at work are independent of one another (no tradeoffs) and do not belong in a nesting structure. Similarly, the inclusive value for the nest comprising return home trips was also one, suggesting that various return- home trips undertaken by commuters over a day are independent of one another. Again, neither of these findings is consistent with behavioral expectations. Even if the finding that activities undertaken while at work are independent of one another is potentially acceptable, the finding that return-home trips are independent across time periods is behaviorally inconsistent. For a commuter, work and other temporal constraints would undoubtedly result in interdependence (and therefore tradeoffs) among various time periods for undertaking return-home trips. This would call for the nest comprising return-home trips to have an inclusive value coefficient estimate of less than one. The behaviorally inconsistent inclusive value coefficient estimates prompted us to reject this structure and search for a structure that was both behaviorally plausible as well as statistically acceptable.
After an extensive exploratory analysis of commuter activity engagement patterns in the dataset, we found that the most prevalent activity participation behavior included an eat/meal activity pursued while at-work, shopping/personal business pursued (in the evening) after work, and entertainment/social recreation pursued after work. Other types of activity participation behavior before work or while at work occur less frequently in the sample possibly because constraints do not allow the scheduling of activities during those periods for most commuters or simply because those activity patterns are less preferred.
This activity scheduling behavior of commuters may be captured by placing the more prevalent alternatives as separate and independent choices. All of the less-prevalent activity scheduling alternatives may be combined into a single nest to represent their rare nature and the fact that, if a commuter does participate in one of these alternatives, the likelihood of that person participating in another less prevalent alternative (in the same nest) is virtually none. Eat/meal activities while at work, shopping/personal business activities (in the evening) after work, and entertainment/social recreation activities (in the evening) after work are the more prevalent alternatives. They are all treated as separate and independent choice alternatives. Once again, as in the previous case, return-home trips are placed in a separate nest to distinguish between activities and trips. Figure 3 shows the nesting structure for representing the activity scheduling behavior of commuters.
Variables used in this nested logit model are defined in table 3. They include a series of socioeconomic variables describing the individual and the household. There are two inclusive value parameters for this nesting structure, one associated with the "less frequent" nest and the other associated with the "return home" nest. Model estimation results for the proposed structure are presented in table 4. This model offered plausible and behaviorally sound coefficient estimates for the inclusive value parameters. In addition to this, it offered the same level of goodness-of-fit as the prior two structures we considered and provided coefficient estimates on all other explanatory variables according to expectations. Although several coefficients had low t -statistics from a statistical standpoint, they were retained in the model as they offered behaviorally plausible interpretation and sensitivity. This model structure was finally chosen to represent activity scheduling behavior of commuters in the San Francisco survey sample.
The inclusive value coefficient estimate of the nest comprising the less frequent activities was 0.80, while that for the nest comprised of return home trips was 0.72. Both of these inclusive value parameter estimates were statistically significant at the 99% confidence level and significantly less than one. The inclusive value parameter estimate of 0.80 for the nest comprising rare and less frequent activities indicates that the activities in this nest share unobservable attributes and considerable tradeoffs are involved when pursuing these activities. The inclusive value parameter estimate of 0.72 for the nest comprising return home trips indicates that these trips, typically undertaken over a day, share unobserved attributes. If a commuter returns home temporarily during a certain time period during the day, then the same commuter would show less preference to again return home temporarily at some other time period in the day. This inclusive value is consistent with behavioral expectations that commuters are temporally constrained and are rarely inclined to make multiple return trips home during the day.
With respect to socioeconomic variables, the model offers very consistent coefficient estimates. In general, males in the sample were more likely to engage in the eat/meal activity while at work. On the other hand, females were more likely to engage in shopping/personal business activities after work. However, more males pursued entertainment and other social recreation activities after work compared with their female counterparts. These findings are consistent with traditional gender-based differences in household roles and obligations. Strangely, males also exhibited a greater tendency to return home while at work, which clearly shows the importance of integrating models of in-home activity engagement with models of out-of-home activity engagement. Furthermore, the reasons for males' return-home trips while at work merits further investigation.
Younger commuters in the sample were more likely to eat while at work and undertake entertainment/recreation activities after work. It appears that they were more likely to undertake their after work activities after a temporary trip home as indicated by the positive coefficient associated with the return home after work alternative. Licensed individuals, who presumably have access to an automobile, were more likely to pursue shopping/personal business activities during the day and more likely to return home in the middle of the workday. As expected, commuters employed full time were less inclined to return home during the day, but were more inclined to engage in an eat/meal activity while at work. Students were more likely to participate in activities in the period prior to work (they are more often part-time workers who have the flexibility to undertake before-work activities).
Single people showed a greater propensity to engage in entertainment/social recreation activities in the after-work period than other household types. Single parents were more likely to return home during the day, presumably because of childcare or other household obligations. Commuters in households with children showed a negative propensity to engage in recreation and other out-of-home nonwork activities during the day. This is presumably because they devote much time in-home to childcare activities and other household obligations. As expected, high income commuters and commuters in households with high car availability were more prone to participate in entertainment activities in the after-work period (while these coefficients were not statistically significant at the 95% confidence level, they were retained because of their behaviorally intuitive interpretation). Those who drive to work appeared to have the flexibility and therefore positive propensity to return home during the day (while at work) and undertake shopping/personal business activities after work (possibly on the way home from work).
In summary, the final adopted model structure provides model parameter estimates consistent with behavioral and empirical expectations. Most of the model coefficients are statistically significant at the 95% confidence interval. The inclusive value parameter estimates are also behaviorally plausible. Therefore, we may conjecture that this nesting structure provides a reasonable representation of activity scheduling for commuters. However, we draw this conclusion with caution, because this nested logit structure generated consistent inclusive value parameter estimates in the context of the 1996 San Francisco Bay Area survey sample. Alternate nesting structures estimated on several different sample datasets should be examined and tested before drawing conclusions about the behavioral paradigm underlying commuter activity scheduling behavior.
The previous section described a nested logit model structure intended to represent commuters' activity scheduling behavior. In this section, a sample numerical simulation is provided to show how the model can be used to predict activity scheduling patterns of commuters.
For the purpose of this exercise, six hypothetical individuals are considered. Their characteristics in relation to the socioeconomic variables included in the nested logit model are shown in table 5. In general, the six hypothetical individuals cover a range of socioeconomic characteristics thus providing a means of examining whether the model is truly sensitive to differences among individuals. Prior to the application of the nested logit model of activity scheduling behavior, the total number of out-of-home nonwork activities (including temporary return trips home) pursued by each commuter was predicted using Poisson- and negative-binomial regression-based activity frequency models similar to those developed by Ma and Goulias (1999). These activity frequency models provided a basis for determining the number of activities that need to be drawn and included in the commuters' activity schedules. The predicted activity frequency for each individual is shown in the last column of table 5.
The nested logit model includes a total of 12 different alternatives as shown in the first column of table 6. In this table, predicted probabilities associated with each alternative are shown for person number 4 from table 5. This person is a young, high-income female single parent. The predicted probabilities for the 12 alternatives are shown in the second column of table 6. In order to determine the activities that need to be included in this person's schedule, a simple Monte Carlo simulation method was adopted. Using a random number generation process, random numbers between 0 and 1 were repeatedly drawn to determine the choices according to the predicted probability distribution of the alternatives for each individual. The number of draws is equal to the predicted activity frequency as provided by the Poisson- or negative-binomial regression-based activity frequency model.
For this individual, the activities that were drawn in the simulation included return home before work, shop/personal business before work, entertainment after work, eat/meal at work, eat/meal after work, and shop/personal business after work. In order to develop the pattern, we started at the beginning of the day and assumed that the person is at home (initial location). According to the model, this person undertakes shop/personal business before work. During the aggregation of activity types, serve-child activities were combined with shopping and personal business activities. Because this person is a single parent, we conjectured that this must be a serve-child activity. Also, the person returns home before work. Then, it appears that this person drops off a child and then returns home prior to work. So far, the pattern is as follows: home → child drop → (return) home.
Next the individual goes to work. While at work, the individual undertakes an eat/meal activity. No other activities are undertaken while at work. The individual presumably returns to work after the eat/meal activity. The pattern thus far has become: home → child drop → return home → work → eat/meal → work.
There are a total of three activities undertaken in the after-work period. They are shopping/personal business (presumably serve or pickup child), entertainment, and eat/meal. There is no temporary return home trip in the after-work period. Thus, it appears that this individual undertakes all of these activities after work prior to returning home. The question then becomes: how are these three activities sequenced? Many factors influence the sequencing of activities and one would need richer preference data and possibly rule-based heuristics to determine activity sequencing (Pendyala et al. 1998). At this point in the model development, we adopted a simplified heuristic rule to determine the activity sequence. Using the values of predicted probabilities in table 6 as an ordering mechanism, the individual would first proceed to an entertainment activity, then turn to an eat/meal activity, and finally pick up the child (shop/personal business after work) prior to returning home. The final pattern then becomes: home → child drop → return home → work → eat/meal → work → entertainment → eat/meal → child pickup → home.
Figure 4 shows the activity scheduling patterns generated for the six hypothetical individuals considered in table 5. The model offers very plausible and reasonable activity schedules. For example, person number 2 is a high-income single male. Consistent with expectations, this person undertakes several after-work activities including eating out, shopping/personal business, and entertainment/recreation. On the other hand, person number 1, who is a low-income single male, undertakes fewer activities and does not engage in out-of-home entertainment. Person number 3 is very similar to person number 4, except she is a low-income individual. The difference between their activity patterns reflects their income difference: whereas person number 4 eats out and pursues entertainment after work, person number 3 does not.
The model may predict activity scheduling patterns that are not possible due to situational or institutional constraints (Kitamura et al. 2000). For example, an individual may be constrained to pick up a child prior to engaging in other after-work activities. These aspects of activity pattern generation need to be incorporated by combining this model with other models of activity behavior, including rule-based, econometric, or behavioral decision approaches. By combining this model with models of activity sequencing and prioritization, activity duration, departure time choice, prism constraints, etc., realistic and plausible activity patterns (with detailed time-of-day information) that do not violate various constraints can be generated.
This paper presents a simple and practical nested logit model that can be used to predict the daily activity schedule of a commuter. The model schedules nonwork activities in relation to the work activity, thus explicitly recognizing the limited spatial and temporal flexibility associated with the work schedule. The model schedules various nonwork out-of-home activities in three possible time periods (or prisms), namely, before work, at work, and after work. In addition to scheduling out-of-home non-work activities, the model includes the capability of scheduling temporary return-home trips, thus facilitating the identification of both home-based and work-based trip chains for commuters.
The behavioral paradigm suggested in this paper is that individuals first plan the nonwork activities that they need to accomplish in a day and then schedule these activities in relation to their work activity. Several alternative nested logit structures were estimated on the 1996 San Francisco Bay Area activity survey sample dataset in an attempt to operationalize the behavioral paradigm. The two-stage decision process embodied in the behavioral paradigm was supported by the estimation results, albeit with some modifications to account for the fact that some activity scheduling patterns are far more prevalent than others.
The paper includes a numerical example to illustrate how the model can be used to predict a daily activity schedule for commuters. The model includes the capability of scheduling return trips home that may occur during the day, thus facilitating the identification of home-based trip chains (in addition to work-based trip chains). Six hypothetical individuals with different socioeconomic characteristics were considered and their activity schedules simulated using the nested logit model. This was done by combining the application of the nested logit model with a Monte Carlo simulation method and simple heuristics that facilitated the identification and sequencing of activities in the schedule. A quick check in which predicted schedules were compared against actual observed schedules of very similar (but not always identical) individuals in the dataset showed that the model predictions were virtually identical to observed schedules.
The model presented in this paper is only a small piece of an overall activity-based model system. The model needs to be combined with other models of activity behavior including activity sequencing, activity frequency, activity duration, activity timing, and in-home versus out-of-home activity substitution/complementarity to fully identify and describe an individual's activity-travel pattern. In addition, appropriate rule-based heuristics need to be incorporated to ensure that the predicted pattern is plausible, feasible, and satisfies all constraints.
The nested logit model presented in this paper may itself be improved in several ways both from an empirical as well as a methodological standpoint. In this effort, several activity purposes were aggregated into composite categories (e.g., shopping/personal business/serve child). However, it would be preferable to retain the differentiation among activity categories so that their unique characteristics may be better reflected in the model and the identification of specific activities in the pattern is easier. The inclusion of accessibility variables would be another important enhancement to the model, because activity generation and time-of-day scheduling are highly influenced by spatio-temporal activity accessibility. In addition, it would be helpful to test and estimate alternative structures on different datasets to see whether a more robust and unified theory of activity scheduling behavior can be developed.
The assumptions implicit in the nested logit model (for example, that the Independence from Irrelevant Alternatives (IIA) assumption holds at each level of the nested structure) should be tested to ensure that the nested logit modeling methodology and the nested structure adopted are appropriate for modeling activity scheduling behavior. Also, the nested logit model structure proposed in this paper does not accommodate multiple episodes of the same activity type in one time period. Some individuals will pursue multiple episodes of the same activity (say, shopping) in one time period (this was found to be extremely rare in the San Francisco Bay Area dataset, but nevertheless worthy of accommodation in the model structure). Finally, another issue that merits improvement concerns the use of the nested logit model in the presence of interrelated choice alternatives.
From an application standpoint, the model should be enhanced to incorporate the capability of responding to a range of transportation policy scenarios. A major benefit of the activity-based approach is that it offers a behaviorally robust framework in which the impacts of transportation policies on individual travel behavior can be assessed (Pendyala et al. 1997). Increases in congestion, travel demand management strategies, transportation control measures, or new transportation investments (highway or transit expansion) may lead to adjustments in daily activity schedules. In its current form, the model is not wholly sensitive to such variables. Either the model needs to incorporate such variables so that it is sensitive to the changes brought about by alternative transportation policies or it needs to be combined with another model capturing such sensitivities (e.g., a stated preference model). The development of such models would, however, also place greater demands on data requirements.
The authors thank the Florida Department of Transportation for providing funding for the research described in this paper. The authors also thank Ken Vaughn and Chuck Purvis of the San Francisco Bay Area Metropolitan Transportation Commission for providing the data used in this study and taking the time to answer many questions. The contents of this paper reflect the views of the authors who are responsible for the facts and the accuracy of the information presented here. The contents do not necessarily reflect the official views or policies of the Florida Department of Transportation.
Axhausen, K. and T. Gärling. 1992. Activity-Based Approaches to Travel Analysis: Conceptual Frameworks, Models, and Research Problems. Transport Reviews 12:32341.
Ben-Akiva, M. and S.R. Lerman. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: The MIT Press.
Bhat, C.R. 1996. A Hazard-Based Duration Model of Shopping Activity with Nonparametric Baseline Specification and Nonparametric Control for Unobserved Heterogeneity. Transportation Research 30(3):189207.
____. 1998. A Post-Home Arrival Model of Activity Participation Behavior. Transportation Research 32B:36171.
Bhat, C.R. and F.S. Koppelman. 1999. A Retrospective and Prospective Survey of Time-Use Research. Transportation 26(2):11939.
Bhat, C.R., J.P. Carini, and R. Misra. 1999. Modeling the Generation and Organization of Household Activity Stops. Transportation Research Record 1676:153161.
Bowman, J.L. and M. Ben-Akiva. 1997. Activity-Based Travel Forecasting. Activity-Based Travel Forecasting Conference Proceedings, June 25, 1996: Summary, Recommendations and Compendium of Papers. Edited by L.J. Engelke. Washington DC: U.S. Department of Transportation, Travel Model Improvement Program. Also available at http://tmip.fhwa.gov/clearinghouse/docs/abtf .
Chen, C., R. Kitamura, and J. Chen. 1999. Multimodal Daily Itinerary Planner: Interactive Programming Approach. Transportation Research Record 1676:3743.
Daly, A. 1987. Estimating "Tree" Logit Models. Transportation Research 21B:25167.
Damm, D. 1982. Parameters of Activity Behavior for Use in Travel Analysis. Transportation Research 16A(1):135148.
Doherty, S.T. and E.J. Miller. 2000. A Computerized Household Activity Scheduling Survey. Transportation 27(1):7597.
Ettema, D. and H. Timmermans. 1997. Theories and Models of Activity Patterns. Activity-Based Approaches to Travel Analysis. Edited by D.F. Ettema and H.J.P. Timmermans. Oxford, England: Elsevier Science Ltd.
Gangrade, S., K. Kasturirangan, and R.M. Pendyala. 2000. A Coast-to-Coast Comparison of Time Use and Activity Patterns. Transportation Research Record 1718:3442.
Gärling, T., M. Kwan, and R. Golledge. 1994. Computational Process Modelling of Household Activity Scheduling. Transportation Research 28B:35564.
Golob, T.F. 1998. A Model of Household Choice of Activity Participation and Mobility. Theoretical Foundations of Travel Choice Modeling. Edited by T. Gärling, T. Laitila, and K. Westin. New York, NY: Elsevier.
Hamed, M.M. and F.L. Mannering. 1993. Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology. Transportation Science 27(4):38194.
Hensher, D.A. and W.H. Greene. 2000. Specification and Estimation of the Nested Logit Model: Alternative Normalizations. Working Paper, Stern School of Business, New York University, NY.
Jones, P.M., M.C. Dix, M.I. Clarke, and I.G. Heggie. 1983. Understanding Travel Behavior. Aldershot, England: Gower Publishing.
Kitamura, R. 1984. A Model of Daily Time Allocation to Discretionary Out-of-Home Activities and Trips. Transportation Research 18B(3):25566.
____. 1988. An Evaluation of Activity-Based Approaches. Transportation 15:934.
Kitamura, R. and S. Fujii. 1998. Two Computational Process Models of Activity-Travel Behavior. Theoretical Foundations of Travel Choice Modelling. Edited by T. Gärling, T. Laitila, and K. Westin. New York, NY: Elsevier.
Kitamura, R., R.M. Pendyala, E.I. Pas, and D.V.G.P. Reddy. 1995. Application of AMOS, an Activity-Based TCM Evaluation Tool to the Washington, DC, Metropolitan Area. Proceedings of the 23rd Summer Annual Meeting, PTRC Ltd., Glenthorne House, Hammersmith Grove, London, England.
Kitamura, R., C. Chen, and R.M. Pendyala. 1997a. Generation of Synthetic Daily Activity-Travel Patterns. Transportation Research Record 1607:154162.
Kitamura, R., S. Fujii, and E.I. Pas. 1997b. Time-Use Data, Analysis and Modeling: Toward the Next Generation of Transportation Planning Methodologies. Transport Policy 4(4):22535.
Kitamura, R., T. Yamamoto, K. Kishizawa, and R.M. Pendyala. 2000. Stochastic Frontier Models of Prism Vertices. Transportation Research Record 1718:1826.
Koppelman, F.S. and C-H. Wen. 1998. Alternative Nested Logit Models: Structure, Properties and Estimation. Transportation Research 32B(5):28998.
Kuppam, A.R and R.M. Pendyala. 2001. A Structural Equations Analysis of Commuters' Activity and Travel Patterns. Transportation 28(1):3354.
Lerman, S.R. 1984. Recent Advances in Disaggregate Demand Modeling. Transportation Planning Models. Edited by M. Florian. Amsterdam, The Netherlands: North-Holland.
McFadden, D. 1978. Modeling the Choice of Residential Location. Spatial Interaction Theory and Planning Models. Edited by A. Karlquist, L. Lundquist, F. Snickars, and J.W. Weibull. Amsterdam, The Netherlands: North-Holland.
Ma, J. and K.G. Goulias. 1999. Application of Poisson Regression Models to Activity Frequency Analysis and Prediction. Transportation Research Record 1676:8694.
Ortuzar, J.D. and L.G. Willumsen. 1994. Modelling Transport. New York, NY: John Wiley & Sons.
Pendyala, R.M., R. Kitamura, C. Chen, and E.I. Pas. 1997. An Activity-Based Microsimulation Analysis of Transportation Control Measures. Transport Policy 4(3):18392.
Pendyala, R.M., R. Kitamura, and D.V.G.P. Reddy. 1998. Application of an Activity-Based Travel Demand Model Incorporating a Rule-Based Algorithm. Environment and Planning B: Planning and Design 25:75372.
Pendyala, R.M., T. Yamamoto, and R. Kitamura. 2002. On the Formulation of Time-Space Prisms To Model Constraints on Personal Activity-Travel Engagement. Transportation 29(1): 7394.
Recker, W.W., M.G. McNally, and G.S. Root. 1986. A Model of Complex Travel Behavior: Part I, Theoretical Development. Transportation Research 20A:30718.
Train, K. 1986. Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand. Cambridge, MA: The MIT Press.
Wen, C.H. and F.S. Koppelman. 1999. Integrated Model System of Stop Generation and Tour Formation for Analysis of Activity and Travel Patterns. Transportation Research Record 1676:136144.
____. 2000. A Conceptual and Methodological Framework for the Generation of Activity-Travel Patterns. Transportation 27(1):523.
Address for Correspondence and End Notes
Ram M. Pendyala, University of South Florida, ENB 118, Department of Civil and Environmental Engineering, 4202 East Fowler Avenue, Tampa, FL 33620-5350. Email: email@example.com.
1. The survey also collected data on Bay Bridge usage in connection with a peak-period toll study; however, variables in the dataset related to this aspect of the study are not used in this paper.
2. The term prisms, in the context of activity-based travel behavior, has been widely used to represent the limited time and spatial accessibility travelers typically have.

subset: commoncrawl


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Language |
Individual differences |
Personality |
Philosophy |
Social |
Methods | Statistics | Clinical | Educational | Industrial | Professional items | World psychology |
Biological: Behavioural genetics · Evolutionary psychology · Neuroanatomy · Neurochemistry · Neuroendocrinology · Neuroscience · Psychoneuroimmunology · Physiological Psychology · Psychopharmacology (Index, Outline)
|Simple partial seizure
|Classification and external resources
Simple partial seizures are seizures which affect only a small region of the brain, often the temporal lobes or hippocampi. People who have simple partial seizures retain consciousness.[1] Simple partial seizures are often precursors to larger seizures, where the abnormal electrical activity spreads to a larger area of (or all of) the brain, usually resulting in a complex partial seizure or a tonic-clonic seizure.[2] In this case they are often known as an aura.
Simple partial seizures are a very subjective experience, and the symptoms of a simple partial seizure vary greatly between people. This is due to the varying locations of the brain the seizures originate in e.g.: Rolandic. A simple partial seizure may go unnoticed by others or shrugged off by the sufferer as merely a "funny turn". Simple partial seizures usually start suddenly and are very brief.[3] It is unusual for them to occur in isolation as they usually lead to other forms of partial seizure.[3]
While awake some common symptoms of simple partial seizures are:[1]
- preserved consciousness
- sudden and inexplicable feelings of fear, anger, sadness, happiness or nausea
- sensations of falling or movement
- experiencing of unusual feelings or sensations
- altered sense of hearing, smelling, tasting, seeing, and tactile perception (sensory illusions or hallucinations), or feeling as though the environment is not real (derealization) or dissociation from the environment or self (depersonalization)
- a sense of spatial distortion—things close by may appear to be at a distance.
- déjà vu (familiarity) or jamais vu (unfamiliarity)
- laboured speech or inability to speak at all
- usually the event is remembered in detail
When the seizure occurs during sleep, the person will often become semi-conscious and act out a dream while engaging with the environment as normal, and objects and people usually appear normal or only slightly distorted, being able to communicate with them on an otherwise normal level. However, since the person is acting in a dream-like state, they will assimilate any hallucinations or delusions into their communication, often speaking to a hallucinatory person or speaking of events or thoughts normally pertaining to a dream or other hallucination.
While asleep symptoms include:
- onset usually in REM sleep
- dream like state
- appearance of full consciousness
- hallucinations or delusions
- behavior or visions typical in dreams
- ability to engage with the environment and other people as in full consciousness, though often behaving abnormally, erratically, or failing to be coherent
- complete amnesia or assimilating the memory as though it was a normal dream on regaining full consciousness
Although hallucinations may occur during simple partial seizures they are differentiated from psychotic symptoms by the fact that the person is usually aware that the hallucinations are not real.[3]
- ↑ 1.0 1.1 Simple Partial Seizures. Epilepsy Foundation.
- ↑ Partial (focal) seizure. MedlinePlus.
- ↑ 3.0 3.1 3.2 Hart, YM (2007). Epilepsy Questions and Answers, Merit Publishing.
Seizures and epilepsy (G40–G41, 345)
|Related disorders
|Epilepsy organizations
|Issues for epileptics
| Seizure types
Epilepsy types
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|This page uses Creative Commons Licensed content from Wikipedia (view authors).


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Arthritis & Rheumatism, Volume 60,
October 2009 Abstract Supplement
The 2009 ACR/ARHP Annual Scientific Meeting
Philadelphia October 16-21, 2009.
PADI4 Haplotypes Do Not Interact with Smoking, but Interact with Shared Epitope Regardless of Antibodies to Cyclic Citrullinated Pepeptide
Bang1, So-Young, Sung1, Yoon-Kyoung, Jung1, Kyounghee, Han2, Tae-Un, Kang2, Changwon, Bae1, S.-C.
Hanyang Univ Medical Center, Seoul, South Korea
Korea Advanced Institute of Science and Technology(KAIST), Daejeon, South Korea
The association of the peptidyl arginine deiminase 4 (PADI4) gene with rheumatoid arthritis (RA) has been replicated in several Asian populations. Anticyclic citrullinated peptide antibodies (ACPA) are most specific serologic markers of RA. We investigated whether PADI4 polymorphisms contribute differently to two subsets of RA depending on the presence or absence of ACPA and whether PADI4 polymorphisms interact with the HLA-DRB1 shared epitope (SE) alleles or with smoking in a Korean population.
All RA patients (n =1313) and controls (n = 1009) were Korean. Four exonic SNPs of the PADI4 gene (padi4_89, padi4_90, padi4_92, and padi4_104) were genotyped using the MassArray SNP genotyping system. Odds ratios (OR) with 95% confidence intervals (CI) were examined using logistic regression analyses.
The four SNPs and functional haplotype of PADI4 gene were significantly associated with developing ACPA-positive RA and ACPA-negative RA. No interaction was seen between smoking and PADI4 polymorphisms. Gene-gene interactions between homozygous GTT haplotypes and SE alleles were observed in the development of ACPA-positive and ACPA-negative RA (attributable proportion 0.44 [95% CI 0.180.69], 0.57 [95% CI 0.220.91], respectively).
Our findings indicate that PADI4 polymorphisms play a role in the development RA regardless of ACPA status. We could not find a gene-environment interaction between PADI4 polymorphisms and smoking. Gene-gene interaction between homozygous PADI4 haplotypes and SE alleles occurs in conferring an increased risk of ACPA-positive as well as ACPA-negative RA.
To cite this abstract, please use the following information:
Bang, So-Young, Sung, Yoon-Kyoung, Jung, Kyounghee, Han, Tae-Un, Kang, Changwon, Bae, S.-C.; PADI4 Haplotypes Do Not Interact with Smoking, but Interact with Shared Epitope Regardless of Antibodies to Cyclic Citrullinated Pepeptide [abstract]. Arthritis Rheum 2009;60 Suppl 10 :388
DOI: 10.1002/art.25471


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Some sound concrete clarion calls, some are like string quartets, some trumpet brazen marches, and some squeal in sheer discord.
Even with all the money in the bank, most Hollywood stars and executives are loathe to squeal to protect their own hide.
He could not tell the government anything of value even if he wished to "squeal."
They've got us roped and tied and I'd advise you not to squeal.
Rudolf could hardly help a squeal of surprise at the sight of the yellow lion and the big shambling bear.
There came a squeal of amazement from Aggie, a start of incredulity from Garson.
From under the shingle roof there was a sound of struggling—a thump, as a body hit the ground—an old woman's squeal of rage.
When I squeal, Andy, it'll be when there's nothing but the voice left.
Jake gave a squeal and wriggled with might and main, but his ears held the rope from slipping off.
She did not squeal nor shudder, but sat regarding it with gentle pride.
c.1300, probably of imitative origin, similar to Old Norse skvala "to cry out" (see squall (v.)). The sense of "inform on another" is first recorded 1865. The noun is attested from 1747.


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People instantly recognize kebabs as a food originating from the Middle East—except for one man. Larry Miastkowski, from Buffalo, NY, needs to just accept it already with this Groupon.
Choose Between Two Options
- $30 for $60 worth of Persian dinner cuisine for four or more
- $15 for $30 worth of Persian dinner cuisine for two or more
The menu includes beef-tongue sandwiches ($7.45), cornish-hen kebabs with bread ($9.95), and rack-of-lamb shish kebabs with rice ($13.95).
Kabob Bazaar
It seems quite easy to make grilled kebabs—all you need is skewers, an open flame, and hunks of meats. However, it's remarkable how well the chefs at Kabob Bazaar prepare their tasty, charred chunks of swordfish, salmon, lamb, and ground sirloin year after year, as they celebrate their 21st anniversary in 2014. Alongside their namesake cuisine, other Persian eats, such as stuffed grape leaves and beef-tongue sandwiches, which are served on baguettes from the eatery's tandoor oven, meet diners for lunch or dinner. For dessert, guests might sample the housemade baklava or two scoops of housemade ice cream—flavored with rose water and saffron. All of the food is served in a casual dining area that features yellow walls, café chairs, exposed brick, and a bar adorned with three shiny, silver samovars filled with steaming Persian tea.


text:
Charleroi teaches Bentworth a AA lesson, 83-31
Charleroi welcomed Bentworth back into Section 5 AA basketball, handing the Bearcats a stunning 83-31 loss.
Bentworth (2-1) came into the game undefeated and two players averaging over 20 points per game.
However, Charleroi (1-2), playing its home opener, had its entire starting five together for the first time.
Bentworth moved up to play in Class AA for this season while the Cougars showed the Bearcats what it's like to play in the bigger classification, especially in the second half when Charleroi outscored Bentworth, 51-10..
"When they put us in this section we knew we had a shot at it. (Tonight) we sent a statement, we're going to contend," Charleroi coach Bill Wiltz said.
"The injury bug got us the first couple of games and it was nice to put the first five on the floor and give them some substantial minutes together because they're tough when they play together."
Make that the first six players.
The Cougar's sixth man, Brandon Bowman, was the leading scorer on the night with 18 points. Four starters also finished with double figures: Matt Indorante with 15, Jim Diaz with 14, Eric Johnson with 13, and Sheldon Linnman with 11.
"It was nice to see Bowman be our leading scorer with 18 coming off the bench. He works his tail off every practice, every game," Wiltz said.
On the other side, Charleroi held Bentworth's leading scorers Chris Blackburn and Cody Levine to 11 and 10 points, respectively.
The game opened up with Charleroi on an 11-2 run. Bentworth then went on a 7-0 run of its own, with six points coming from Levine.
The Cougars then went on a 6-0 run to finish out the quarter up 17-9. Charleroi hit for 3-pointers in the first stanza.
"Charleroi jumped on us early, put pressure on us out front, and it looks like my guards froze up," said a disappointed Bentworth coach Bob Kennedy.
In the second quarter, Bentworth played its best basketball of the evening. Blackburn, who was scoreless in the first quarter, scored the first six points of the quarter for Bentworth. The Bearcats pulled to within 24-19 with 4:11 remaining in the half.
It wasn't enough though as Charleroi kept up its torrid three-point shooting. Charleroi went into halftime with a 32-21 lead and would make 10 treys in the game.
About his team's awful second half, Kennedy said, "I think they gave up on me after halftime, but we're going to work until we find the right combination."
Bentworth is back in action Monday at Carmichaels and Charleroi resumes play Tuesday at Beth-Center.
Naz Victoria is a freelance writer.


text:
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Translation Disclaimer
ART SHOW 'PET PARADE' OPENING AT THE 1947 CENTER STREET LOBBY GALLERY Art Show open September 10 to December 10, 2010
Berkeley, California (Tuesday, September 21, 2010) - An art show entitled "Pet Parade," with works by five local and international artists, opened at the City of Berkeley 1947 Center Street Lobby Gallery on September 10, 2010. The show runs through December 10, 2010.
The show celebrates the dog lovers who come to the building to register their pets by featuring the work of five artists who are recognized nationally and internationally for their dog and cat artwork. Three of the artists are from the Bay Area and two are from Germany. The two German artists are frequent visitors to the Bay Area and are very well known internationally for their unique pet artwork.
The five featured artists are: local artists Catherine Hamlett, Patti Heimburger, and Lois Cantor, and German artists Ruth Marcus and Doc Bauman.
The "Pet Parade" is curated by Rinna B. Flohr, curator for the City of Berkeley. The show runs from September 10 - December 10 at 1947 Center Street, Berkeley. Please visit the Holiday and Reduced Services web page for building hours.
For additional information or high resolution images of artwork contact: Rinna B. Flohr, (510) 849-3111 or email@example.com.
Keep up with City of Berkeley news via our News page, email or Follow @CityofBerkeley


text:
New stun guns added to Hampton police arsenal
By a vote of Hampton Council, the township plans to spend $9,683 for 10 new Taser X26P stun guns, battery packs and holsters, so that all officers in Hampton Police Department can carry and maintain their own Taser weapons.
"It should save on the wear and tear of the units," Hampton township manager Chris Lochner said.
Hampton police officers now share nine of the devices, originally purchased years ago.
"We've probably only used them a few times in the years that we've had them," Hampton police Chief Mike Pecora said.
Taser devices shoot darts with electrodes that shock a person and immobilize the his muscles.
Hampton's new Taser X26P stun guns are bright yellow and black.
"With the purchase of the new Tasers, each officer will be responsible for his own Taser unit," Hampton police Sgt. Tom Vulakovich said. "They will be exposed to less usage, thus extending the life and integrity of the Taser unit.
"Currently, the Taser units are rotated through officers during each shift and are exposed to the elements and normal wear and tear 24 hours a day," Vulakovich said.
"The Taser unit will only be deployed when either an officer's or citizen's safety is threatened, and that level of force is authorized by law," Vulakovich said. "In 2013, only one Taser was deployed."
Vulakovich likened Taser devices to the firearms, radios and handcuffs assigned to all officers.
"These items are part of their uniform and are vital pieces of equipment, enabling them to carry out their duties," Vulakovich said.
Pecora said police officers generally use devices as needed.
"Obviously, if you don't have to shoot someone with a bullet, the Taser is the next best thing," Pecora said. "You use the least amount of force, and you go up the continuum, based on what the situation dictates."
Equipping officers with their own Taser devices is a trend among local law-enforcement agencies, Taser spokesman Steve Tuttle said.
"When you pass around a device for three shifts, it gets dinged up," Tuttle said. "The maintenance goes bad."
Hampton's new Taser X26P units were introduced last year and replaced the Taser X26, introduced in 2003. "It's all digital," Tuttle said about the Taser X26P. "It's a safer product. It's a better product."
Deborah Deasy is a staff writer for Trib Total Media. She can be reached at 724-772-6369 or email@example.com.


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NEW YORK — From the mixture of hurt, anger and embarrassment on his face, Gordon seems like one of the last people who would want to open his life to cameras for a reality television show.
His is one of four stories featured in back-to-back episodes of A&E's feel-creepy "Hoarders," which opens its third season Monday at 8 p.m. It's the network's most popular series among young viewers, something of a surprise given that it's about emotionally ill people living amid mounds of garbage.
Gordon (A&E doesn't fully identify people who get treatment through their show) lives in a filthy home with his wife and two adult children. There's no running water. The piles of trash are so deep that his wife got trapped in one and needed help to be extricated.
Clearly, it couldn't have been the direction he had envisioned his life taking. His frustration and embarrassment with his life help explain why he lashed out at a mental health professional who prodded his wife to throw things in the trash and at camera operators recording the scene.
"I'd rather die here than let anyone in the front door," he said.
Yet his tragic life was exposed for all to see. As with many profiled on "Hoarders," being featured on television is a necessary bargain: The production company pays for counseling and aftercare that they would not have been able to afford otherwise.
"He did it because it was the only option left," said Jodi Flynn, executive producer of the series for the Seattle-based Screaming Flea Productions. "They were going to condemn the house if he didn't do something."
"Hoarders" was a hit from the start at A&E, and last winter's second season saw an audience 10 percent higher than the first, the Nielsen Co. said. It averaged 2.3 million viewers an episode and, in the truest mark of success for a nonfiction cable show, produced imitators such as TLC's "Hoarding: Buried Alive" and "Confessions: Animal Hoarding," on Animal Planet.
Screaming Flea was first hired by A&E to do a makeover series focusing on a company that cleans up after hoarders. Although "Dirty Deeds" didn't work, the network was interested in delving deeper into the hoarding disorder and the current documentary-style series was born.
Disturbing as they are, there are only so many pictures of trash piles you can take. Producers look for compelling family stories and track how efforts to treat the hoarders succeed.
In one of the new season's stories, a woman who has been estranged from her hoarding mother for 20 years returns to help her, acting on a promise made to her father before he died. Adella is a challenge; sent to see one psychiatrist, she rooted through his receptionist's garbage can for things to bring home.
The daughter can't hold back tears when she talks about her mother.
"She chose the trash over my dad," Beverly, the daughter, said. "She chose the trash over me and my sister. She chose the trash over our children. She built a wall of trash to keep us all out."
Like popular cable series about pawnbrokers, cupcake bakers, Alaskan fishermen and ice road truckers, "Hoarders" offers viewers an intimate look into a world they probably wouldn't know. Unlike those shows, it's a sad, sometimes horrifying world. If you find something funny, it's hard not to feel guilty about it.
Some of the participants are motivated by a desire to help others beyond themselves, said Rob Sharenow, senior vice president of alternative programming at A&E. Hoarding is a more widespread disorder than many people realize and the series "brings it out of the closet," he said.
"The show really touches on the core of human drama," he said. "These are highly emotional, high stakes stories of people trying to resolve their most critical issues. It isn't light entertainment by any means, but it's vital entertainment. People will feel a visceral connection to the characters, and I do think they show the struggles of the characters."
Besides a variety in families, there is a variety in things that are hoarded. One of the new shows features a colorful character who collects a variety of dolls and artwork that he finds is much less valuable than he thought. Flynn's favorite show remains one from the first season with a woman who hoarded food.
And for some people living in homes with no plumbing, well, you don't want to know what they hoard.
For the first season, producers relied on a network of hoarding experts to find stories for them. Now they get plenty of suggestions from people living through the problem — mostly family, Flynn the executive producer said. Hoarders rarely volunteer themselves.
For some hoarders, the treatment takes. For others, it doesn't. Many remain a work in progress.
"The show does not impose a fake ending on these things," Sharenow said.
Hit 'Hoarders' returns for third season
NEW YORK — From the mixture of hurt, anger and embarrassment on his face, Gordon seems like one of the last people who would want to open his life to cameras for a reality television show.


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American cuisine's roots run deep, from Betsy Ross sewing the first crab cake to Benjamin Franklin's famous kite-cooked pot roast. Taste the heritage with this Groupon.
Choose Between Two Options
- $20 for $40 worth of contemporary American cuisine for dinner
- $10 for $20 worth of contemporary American cuisine for brunch
- See the dinner menu.
- See the brunch menu.
Though John Ly grew up helping out in the kitchen of his family's restaurants, his parents never wanted him to become a chef. The restaurant industry is competitive, they warned him—a lot of hard work with little recognition. Heeding their advice, Ly began pursuing a degree in computer science at the University of Texas, but soon realized that he longed for the creativity and chaos of the kitchen. Propelled by his passion for food and cooking, Ly renounced his degree and enrolled in culinary school. After years spent working his way up through scrubbing floors, washing dishes, and absorbing his managing chef's techniques, Ly finally spearheaded Strata—naming the restaurant after the word for "different layers" to reflect the diverse international influences of his cooking.
As executive chef, Ly captains his kitchen crew as they artfully plate innovative, contemporary American dishes using seasonal ingredients, earning the restaurant Hungry in Houston's Best Restaurant award in 2010. His pan-seared sea bass, cinnamon-rubbed rib eye, and spicy habanero-infused sausage dishes have also enticed the taste buds of reporters from the Houston Press. Ly staffs his modern dining room with a friendly team of servers, while offering up an expansive outdoor patio, an ideal spot for a date night or celebrating recent acquisitions of rival rent-a-bobcat businesses.


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James & Marie
July 19th 2008
by Tracy Martin & Mark Kitaoka
About the Book
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About the Creator
Tracy Martin is a graphic designer and photographer with over 13 years experience. She specializes in working with individuals, small companies and non-profit organizations throughout North America. A graduate of the three year design program at Dawson College in Montreal, she combines a methodical as well as holistic approach to innovative design. Primarily a Graphic Designer, Tracy's creative abilities include Fine Art and Performance photography. In 2010 she was named Company Photographer for TheatreWorks, a nationally acclaimed theatre company in the San Francisco Bay Area.
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A year later and I am staring at my second Ahwatukee Bowl.
I have a better understanding of it, of course, having attended one, hearing from both sides (got to love the anonymous voice mails calling me a homer - for both schools) and found out more about the history.
Last year, I knew going into the game that Mountain Pointe was going to win simply because of the two seasons the two programs were coming off of in 2009.
This year with both teams coming off nearly identical seasons, with terrific defenses and high expectations, I am kind of hemming and hawing as to who I believe is going to come out on top on Friday.
I should have a better idea after doing interviews and seeing some of each team's practices this week.
It doesn't really matter. I'd just end up making one side mad anyway and who needs anonymous voicemails anyhow. The kids on the field will determine the outcome not some prediction.
The game will come down to the usual factors - turnovers, penalties and big plays - of a close game.
How that is going to play out won't be known until about 9:30 Friday night.
Until then check in daily for the latest info on the biggest game of the year.
Contact writer at (480) 898-7915 or email@example.com. Follow him on Twitter @JskodaAFN


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My eyebrows raised when I read this article on IslamOnline. The article, entitled Study: Men Objectify Scantily Clad Women, used a current study conducted by well-known Princeton psychologist, Dr. Susan Fiske, to promote modest clothing.I am familiar with Dr. Fiske's work, and I couldn't help but question whether IslamOnline was misrepresenting the study in an effort to promote the necessity of hijab. As someone doing her Ph.D. in social psychology, I am familiar with how the results of social … [Read more...]
From Bikinis to Hijabs: Using Psychology to Your Advantage
March 24, 2009 by


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The Dude Abideswrote a review of Crispi Diablo Free Ride AT Ski Boot on January 28, 2008
Great Freeride Boot
I bought these boots to drive the new BD Zealot ski (110mm underfoot). They are great in the walk mode and very comfortable on the uphill. They perform nearly as well as an alpine boot on the downhill, even pushing a ski as big as the Zealot.
I have a narrow heel and a wide forefoot, and this boot fit great.
I used to ski the Scarpa Denali, and although that is also a great boot, I've got to say that the Diablo is the better of the two.


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It's very helpful to provide some info before donating your computer(s):
If you're not sure, one of their tech people might help you.
Don't throw away your computer, recycle it!
It's very thoughtful to donate a computer, you can help others less fortunate.
Gifts In
Kind America
Hearts & Minds
(sponsor of
this website)
Even if a computer's old, you may be able to repair it.
The Lazarus Foundation
National Cristina Foundation (NCF)
If you feel that your computer problems are drowning you, recycling it may be the best thing to do.
Online Give-Away Bulletin Boards
Here you can list things you're willing to give away online for free. Not as directly beneficial as giving to a charity, but at least keeps it in use and out of landfills:
www.throwplace.com also includes ways to offer your computers etc. to charities both in the USA and around the world.
Additional NYC organizations for donated computers, etc.
Links for
Related and Other Issues
Latest update by Jenniffer V. (JV), previous by
KG and DME
Helpful Info
Site Guide


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A calendar of some of the notable events and dates in the history of the United States during the Roosevelt Administration.
New Deal Network Photo Library
Photo Library
Civilian Conservation Corps
Camp Scenes
Skyline Constuction
Camp #1 - Skyland
Camp #2 - Big Meadows
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