Transportation Systems Planning Methods and Applications 07

Transportation Systems Planning Methods and Applications 07 Transportation engineering and transportation planning are two sides of the same coin aiming at the design of an efficient infrastructure and service to meet the growing needs for accessibility and mobility. Many well-designed transport systems that meet these needs are based on a solid understanding of human behavior. Since transportation systems are the backbone connecting the vital parts of a city, in-depth understanding of human nature is essential to the planning, design, and operational analysis of transportation systems. With contributions by transportation experts from around the world, Transportation Systems Planning: Methods and Applications compiles engineering data and methods for solving problems in the planning, design, construction, and operation of various transportation modes into one source. It is the first methodological transportation planning reference that illustrates analytical simulation methods that depict human behavior in a realistic way, and many of its chapters emphasize newly developed and previously unpublished simulation methods. The handbook demonstrates how urban and regional planning, geography, demography, economics, sociology, ecology, psychology, business, operations management, and engineering come together to help us plan for better futures that are human-centered.

II Data Collection

and Analysis0273_book Page 1 Friday, October 25, 2002 8:33 AM

© 2003 CRC Press LLC

Operational Definitions • Specific Investigative Goals

7.4 Investigating the Activity Scheduling Decision Process

Individuals vs Households • Investigating Activity Agendas • Investigating the Dynamics of the Activity Scheduling Process

7.5 Applications: Separate and Combined Investigations7.6 Data Types and Analysis

7.7 Discussion and ConclusionsAcknowledgments

References

7.1 Introduction

In the field of transportation, a strong argument has been made for the use of an activity-based approach

to improve the behavioral foundations of travel forecasting models (Axhausen and Gärling, 1992; Ettema and Timmermans, 1997) While this approach offers considerable theoretical appeal and potential, the data collection that it has inspired has been largely limited to a retooling of traditional diary-based survey

methods from recording trips to recording activities While activity diaries have several practical

advan-tages, the implications for analysts is the more challenging task of trying to understand and model a

more complex set of observed activities and travel patterns.

The main criticism of diary-based methods is that they focus on revealed outcomes, providing little, if any, information on the underlying behavioral process that led to the outcomes in the first place To meet this need, a new class of survey methods has emerged that focuses on the activity scheduling decision process Their main point of departure from traditional diary methods is an explicit focus on tracing the

underlying process of how activity–travel decisions are planned, adapted, and executed over time, space, and across individuals — often termed an activity scheduling process The results of this process are an observed pattern of activities and travel over time and space and across individuals — or an activity schedule.

It is only relatively recently that travel behavior researchers have begun to emphasize the need for depth research into the activity scheduling decision processes that underlie observed activity–travel patterns, as a means to both improve our understanding and provide a basis for new model development Sean T Doherty

in-Wilfrid Laurier University

(e.g., Pas, 1985; Polak and Jones, 1994; Lee-Gosselin, 1996; Axhausen, 1998) Recent Transportation Research Board millennium papers also highlight the need for more judicious use of new technologies

to augment existing survey techniques, the challenge of reducing the respondent burden in light of the

demand for more detail (Goulias, 2000; Griffiths etþal., 2000), and the need for realistic representation

of decision-making behavior in travel forecasting models to improve their ability to forecast the more complex responses to travel demand management (TDM) strategies, such as telecommuting and con-gestion pricing (Bhat and Lawton, 2000) This latter point is particularly important, as emerging TDM

and Intelligent Transportation Systems (ITS) solutions implicitly invoke a rescheduling response from

individuals and households that is rarely confined to single trips, single people, or even single days, but rather has significant secondary effects across multiple activities, trips, days, and individuals — effects that may more or less contribute to the desired impacts of the policy

In lieu of empirical insights into underlying behavioral processes, emerging activity scheduling models have had to make several types of assumptions that often limit their potential Early scheduling models most often assumed either a simultaneous (all decisions made at once, and executed without revision)

(e.g., Recker etþal., 1986; Kawakami and Isobe, 1990) or a strict sequential decision process (decisions

made in the same order as execution) (e.g., Kitamura and Kermanshah, 1983; van der Hoorn, 1983) More recent models attempt to replicate the process of schedule building by replicating the sequence of additions, modifications, and deletions to a schedule over time, based on a notion of the priority of

activities (e.g., Ettema etþal., 1993; Arentze and Timmermans, 2000) But even these most recent models

continue to make stringent assumptions concerning activity priority (that it is fixed) and the sequence

of scheduling decisions concerning the various attributes of activities (fixed sequences of choices for activity type, location, start and end times, duration, involved persons, and mode choice) Other models make similar assumptions concerning how tours are formed (e.g., Bowman and Ben-Akiva, 2001) or the sequence of decisions in the logit-based modeling structures (e.g., Bhat and Singh, 2000) Addressing the validity of these assumptions is a key step to future model development and their applicability to the forecasting of emerging policies that inherently invoke a rescheduling response

7.2 Objectives

The focus of this chapter is on an emerging class of interactive survey methods that explicitly target activity scheduling decision processes What is shared by these methods is a desire to interactively observe

how decisions are made and their dynamics, not just the results of these decisions in the form of static

observed activity–travel patterns Given these dynamics, these methods tend to elicit and trace such

behavior in a continuous and interactive way over a period of time — meaning that the sequence and

types of questions asked depend on the particular responses and inputs of subjects

An outline of the basic components of the activity scheduling decision process is first proposed Each component of this framework is then discussed in depth in terms of specific survey method opportunities and challenges, including applied examples where applicable The types of data that result and priority areas

of analysis are then discussed This chapter is based in part on cumulative experiences in developing, testing, and applying activity scheduling process surveys with collaborative research teams in Canada, the United States, and Europe It includes discussion on the latest state-of-the-art techniques and technologies adopted

in the field, many of which are still evolving in design It is hoped that this chapter provides a framework that encourages further in-depth exploration of this exciting and emerging field of inquiry

7.3 The Nature of the Activity Scheduling Decision Process

Figure 7.1 presents a simple schema of the major components of the activity scheduling process that are

the focus of investigation in this chapter The process takes as its starting point an agenda of household

activities (similar words such as “listing” or “repertoire” could also be used) These activities are derived from the basic needs, desires, and goals of individuals and households, and embody a range of practical

and physical constraints An example agenda is shown in Table 7.1 Each activity in the agenda is defined

as the act of satisfying a need that has unique attributes These attributes influence how the activity is scheduled and eventually executed On the agenda, these attributes are measured in terms of their relative

degrees of fixity and flexibility and are meant to be “fuzzy” in nature — once executed, a final observed

static choice of attributes is made.

Taking the activity agenda as given, the activity scheduling process depicted in Figure 7.1 is

concep-tualized as a dynamic and continuous process involving preplanned, impulsive (i.e., little or no preplanning involved), and adaptive decisions concerning the various components of activities: activity type, location,

duration, start and end times, sequencing, involved persons, and mode and route choice This process

continues up to and during actual execution of activities, which leads to the formation of observed

activity–travel patterns over time and space As in all conceptualizations, an endless array of

interdepen-dencies (i.e., arrows) could be drawn in this diagram

As a visual example of the scheduling process, consider Figure 7.2 The example is of a person who starts out with a preplanned schedule that includes empty time windows, but that goes through further

FIGURE 7.1 Simple schema of the main aspects of the activity scheduling decision process.

TABLE 7.1 A Simplified Household Weekly Agenda Example

Activity Label

Applicable Household Members General Location

Attributes Duration

(mean)

Mean Frequency (per week)

# Perceived Locations Etc.

Etc.

Adaptation Execution

Preplanning

Household Activity Agenda

Activity–Travel Patterns Learning

planning, adaptation, and impulsive changes, leading to the final observed space–time pattern Note that

in reality, the planned activities at any stage in the process may only be partially elaborated — meaning that certain aspects of activities may be more or less planned, whereas the figure implies that all observed attributes are decided at once

Also depicted in Figure 7.1 are two other important factors that influence scheduling in the longer

term — habits and learning Over time, habits in the form of set activity–travel decision routines may

form, which are executed with very little thought during the process These habits can be viewed as being

FIGURE 7.2 Step-by-step visualization of the activity scheduling decision process that underlies observed space–time

paths.

y

home

work shop t

x

a) The most basic need, to sleep (or just be at home), is

part of a long-standing routine, and forms a basic

skeleton schedule Note the unplanned time.

b) A work activity and associated travel are preplanned and added to the skeleton schedule.

y

home

work shop

home

work shop

x

t

home

work shop y

x t

c) Upon execution of the preplanned schedule,

unexpected congestion results in an impulsive increase

to the travel time, and associate delay in work start.

d) A call from spouse during the day results in plan for dinner and movie together at home in the evening.

home

work shop y

x

t

home

work shop y

x t

e) On drive home, impulsively decide to shop for a few

grocery items for the evening.

f) Arrival home delayed slightly Final outcome is the observed activity–travel pattern.

Travel between activity locations Time spent conducting activity Activity location

realized through increased fixity of attributes of activities on the agenda over time, and as skeletal activities

on a person’s schedule (see Figure 7.2(a) and (b)) People may also seek out information during this process and learn of new aspects of activities, such as new locations, new involved persons to conduct them with, or even entirely new activities Similar to habits, learning can be viewed as being realized through changes in the attributes of activities on the agenda (or new additions), but perhaps with more

of a tendency toward increasing their flexibility (e.g., learn of more locations to conduct an activity)

7.3.1 Operational Definitions

The following operational definitions are adopted for this chapter:

Trip — Movement over space

Activity — The act of satisfying a need that has unique attributes

Activity attributes — A broad range of characteristics of activities that affect how they are planned and executed, generally measured in terms of their relative degrees of fixity and flexibility See also, the listing in Section 7.4.2.1

Activity agenda — A listing of activities and their attributes for an individual or household

Activity schedule — A continuous pattern of activities and trips over time and space, including the observed choices of what activities to participate in, where, for how long, in what sequence, coupled with mode and route choices (One way to visualize an activity schedule is as a time–space path,

as shown in Figure 7.2(f)) Note how observed activity attributes (start time, end time, location, duration, etc.) differ from their associated fuzzy counterparts on the activity agenda (earliest start time, latest end time, perceived locations, duration distribution, etc.)

Activity scheduling process — The dynamic and continuous process of planning, adaptation, and execution of activities and their attributes over time and space and across individuals, leading to observed activity–travel patterns

Decision rules — The behavioral mechanism applied to solve a choice problem Could include tional econometric random utility maximization, a range of other sub-optimal satisfying rules, or simple logical rules

tradi-Habits or routines — Aspects of activity–travel patterns that are repeated on a regular basis and

scheduled with very little contemplation, generally characterized with high levels of fixity

Learning — The process of discovering new activity attribute information.

7.3.2 Specific Investigative Goals

The overriding goals of investigation are to improve our basic fundamental understanding of underlying decision processes, to assess and challenge the validity of existing scheduling process models and their assumptions, and to provide a new source of data for the estimation of new functions, algorithms, and choice models for scheduling and rescheduling processes Given the conceptualization presented in the previous section, the specific questions of investigation concern the following:

• How the various decisions are organized and sequenced over time, including the “meta” style

decisions of when to preplan, impulsively plan, adapt or reschedule, execute, and search for new

information (i.e., learning)

• What components of activities (activity type, location, duration, start and end times, sequencing, involved persons, and mode and route choice) are decided upon at each point and in what sequence

• The sequence of rescheduling decisions in response to stimuli (what activities are chosen for change; what attributes are chosen for change, conflict resolutions, etc.)

• What rules are used to make choices at each stage in the process

• When and how are habits and routines formed over time

• The extent of learning that occurs in the short and long term

In addition, given the future modeling objectives, additional information should be sought on the activity attributes and situational factors that serve as potential explanatory factors in this process, including:

• Activity attributes (frequency, duration, spatial or temporal fixity, etc.)

• Travel characteristics (e.g., available modes)

• Personal and household characteristics (e.g., age, gender, personality, lifestyle, family life cycle)

• Structural characteristics (e.g., land uses and transport network, opening hours)

• Situational characteristics (e.g., time since last activity, time to next planned occurrence, available time windows, congestion)

To be realistic, it would be most useful to observe these processes and explanatory variables as they occur in real time However, given our limited capacity to observe the workings of the human mind, we must rely on experiments and self-reports of such behavior as means for investigation The next sections attempt to describe the various approaches and techniques for such investigations

7.4 Investigating the Activity Scheduling Decision Process

Given the description above, it should come as no surprise that investigation of the scheduling process appears daunting We are used to investigating observed and outward patterns of activity–travel behavior that can be recalled and recorded in sequence using simple diary techniques, which have become the primary focus of data collection and refinement over the past many decades The scheduling decision process, on the other hand, is not outwardly viewable, and it involves a combination of a variety of

scheduling decisions concerning when and what to schedule at any given moment, followed by the

application of a variety of decision rules to make the choices

However daunting this may seem, we must remember that the scheduling of our daily life is a problem that each and every one of us solves every day, and is thus a very familiar process It is human nature to

be aware of our needs and desires, and to consciously plan to meet these needs in some fashion Asking people to self-report on their scheduling behaviors (What are you going to do today?) is perhaps just as familiar a task as asking them what they did (What did you do yesterday?) The key realization is that the answer to the former question will continue to change over time, whereas the latter is fixed This implies a need for multiple observations over time to capture the true dynamics of the process

As with any complex problem, it is convenient to separate out key concepts for separate investigation strategies — this is especially so when working with human subjects for which respondent burden is a key limiting factor In this case, the most immediate and convenient separation would be between activity agenda formation and the activity scheduling process that follows As shown in Figure 7.1, the key link between these two processes concerns the formation of habits (which may tend to make the attributes of activities

on the agenda more fixed) and the learning of new opportunities and information (which may tend to make the attributes of activities more flexible) If one assumes that habit formation and learning processes are fixed in the short term (operationally meaning that the attributes of activities on the agenda are not updated in the short term), then one can conveniently consider that the scheduling process proceeds in a top-down fashion in the short term, taking activities from the agenda as a starting point and proceeding with preplanning, execution, etc., as shown in Figure 7.1 The implications of this approach for modeling are clear — two black boxes in sequence in the short term, with longer-term feedbacks and updating The implications for investigation of each of these concepts are taken up in the next sections

Thus, although interesting, the longer-term processes of habit formation and learning are conveniently separated out from the investigation of the scheduling process and left for investigation on their own (interested readers should see also Chapter 3) Most appropriately, it would seem that some form of regular updating of the agenda, perhaps with feedback from the scheduling, should be incorporated into longer-term forecasts of scheduling behavior, such as the case when a scheduling process model is integrated within a larger land use and transportation model

7.4.1 Individuals vs Households

All the methods described below could be applied to an individual or a household or family with due care However, given that households will share an activity agenda and exhibit considerable interdepen-dencies in activity scheduling, they are a much more logical choice as the unit of analysis Whereas parents and spouses can often be assumed to act as good surrogates for recalling observed activity patterns of their children and partners, the validity of this assumption when it comes to the underlying decision process is more questionable In fact, the differences and similarities in decision-making processes across household members should be embraced for investigation

Thus, ideally, all adults and children of decision-bearing age (i.e., once they start generating and uling activities on their own) should be directly involved in any survey method in order to capture the true dynamics and interdependencies of decision-making processes However, if only one partner or parents without children are chosen or available, then special efforts should be made to capture as much of the independencies as possible from single individuals This can be done at the agenda investigation phase by quantifying the activities of other household members that have a bearing on the individual, either because they are joint or service activities or because they serve as important constraints on the individual During investigation of activity scheduling decisions, special queries should then be adopted to trace not only joint activities, but also joint decisions and communication acts with other household members

sched-7.4.2 Investigating Activity Agendas

Although the conceptualization of a household activity agenda as a listing of activities and their attributes seems straightforward, operationalizing this list is a challenging and crucially important task for several reasons Firstly, the flexibility or fixity of attributes of activities such as start and end times, frequency, duration, location, involved persons, and travel mode are obviously strong determinants of when and how an activity is subsequently planned and executed during the scheduling process Thus, capturing the most salient attributes on the agenda is key to the success of the activity scheduling process to follow Secondly, the attributes on the agenda are the means from which to assess the impacts of a variety of policy measures For example, a program of telework inherently affects the spatial and temporal fixity

of work activities via the modification of attributes such as location choices (e.g., not fixed to the workplace anymore) and the times at which the activity could be conducted (e.g., not fixed to office hours, 9 to 5) This in turn influences how the activity is scheduled, having a primary impact on work trips, but also secondary impacts on other activities and trips — thus providing a much more behaviorally realistic impact assessment

7.4.2.1 Definition of Activities and Their Attributes

The first challenge faced in investigating activity agendas is deciding upon an operational definition of

an activity The traditional approach is to label a range of activities that involve travel with a set of generic labels such as work, shopping, recreation, etc The trouble with this approach is that the set of activities defined is not universal in type or level of detail, hampering the transferability of the results What is needed is an activity classification that focuses more on the fundamental attributes of activities that make them different from each other From a scheduling perspective, these attributes may include their fre-quency, duration, involved persons, earliest start and latest end times, available locations, etc The challenge is to narrow in on the key attributes, and then seek to define activities based on unique similarities and differences across these attributes

One approach to meeting the activity definition challenge is to establish a set of rules to guide definition For example (based in part on investigations of household agendas reported in Doherty and Miller (2000)):

1 Include all activities that involve travel or could potentially be replaced by travel

2 Include activities that serve as important constraints upon other activities (e.g., attending to children at home), even if very short (e.g., dropping-off or picking-up activities)

3 Define separate activities of the same basic type when their attributes are significantly different.

4 Group multipurpose activities that always occur in sequence together (e.g., washing, dressing, and packing in the morning) or tend to consist of a variety of tasks (e.g., cleaning and maintenance around the house) to avoid unnecessary detail (this rule balanced against rule 3)

Rule 3 is particularly important to the eventual success of any scheduling model For example, an employment activity on the agenda may be traditionally labeled as a “work” and have the following attributes: participated in an average of five times per week; normal duration of 6 to 10 h per day, earliest start at 8:00 A.M., latest end at 7:00 P.M.; located at the office or at home However, given that conducting

work at home implies a different set of attributes, rule 3 implies that it should be defined separately on the agenda, perhaps with the label “telework” and the following attributes: 2 days per week; duration of

6 to 10 h, earliest start at 6:00 A.M., latest end at 11:00 P.M.; located at home The difference in attributes will have a strong effect on how this latter activity is scheduled The challenge is to balance the level of detail in the agenda vs the desired accuracy of scheduling results — if too general (e.g., consider only two activities: in home and out of home), the subsequent scheduling model will lack realism and forecasting power; if too specific, the model may break down and lack computational or operational realism (e.g., considering breathing, moving ones arm, etc., as activities)

Another challenge associated with activity agendas concerns the types of attributes for each activity

that should be investigated These may include:

• Frequency (usual, normal, minimum, maximum, distribution)

• Duration (usual, normal, minimum, maximum, distribution)

• Temporal flexibility (earliest start and latest end times, range of start and end times)

• Spatial flexibility (number of perceived locations, number of possible locations)

• Interpersonal dependency (household members involved/required/optional)

• Interactivity dependency (performance of one activity linked to another activity or longer-term project in time or space)

• Travel modes (available travel modes, most likely mode)

• Perceived travel times

as a preplanned skeletal activity with highly fixed time and location, as opposed to the inclusion of a variable reflecting the presence of children For forecasting purposes, this is particularly valuable, as policy changes are often materialized in the form of modifications to the constraints imposed upon activities

7.4.2.2 Quantifying Activity Agendas

In practice, in-depth investigation of household activity agendas can be a time-consuming and densome task for individuals and households Three possible approaches to investigating activity agendas include:

bur-1 Repeated observation of activities over a sufficiently long observation period to capture the variability in observed attributes that serve as an indicator of the relative flexibility or fixity

2 In-depth face-to-face interviews querying directly for stated attributes, using a computerized form

to speed data entry

3 Similar to number 2, but conducting the interview using computer-automated prompts and dialogs

As an example of the first method, consider the results of a 6-week travel survey conducted recently

in Germany (Axhausen etþal., 2002) The observed variability in the duration of a range of activities

could be taken as an indicator of their relative flexibility or fixity This could also apply to durations, frequencies, locations, involved persons, and mode choices The obvious challenge of this approach is deciding on the length of the survey, which must be a sufficient period long enough to capture the variability Even then, assuming that observed variability is a good indicator of the actual flexibility may

be questioned in the case of activity attributes that have become habitual For instance, a grocery shopping activity may be observed to occur at the same location and time every week out of habit, but assuming

it is relatively fixed in space to just one location based on this information alone may be wholly inaccurate

A second alternative, or even supplemental approach, is to hold an in-depth interview in order to

investigate a household’s stated range of activities and their attributes In order to structure such an

interview, a set of preliminary activity types should be defined as a basis for initial discussions A typical listing is provided in Table 7.2, although the exact number and types of activities should be tailored to

TABLE 7.2 Example Listing of Generic Activity Types That Could Be Used as a Starting Basis to Define a Household Activity Agenda

Delivered or picked-up meal

Coffee or snack shops

Other basic needs

Work School Day care Volunteer work Special training Other work or school

Cleaning, maintenance Meal preparation Chauffeuring Chauffeuring and passively observing Attending to children

Pick up involved person Other errands Other obligations

Video store Library Other service

Minor groceries (<10 items) Major groceries (10+ items) Housewares

Clothing and personal items Drug store

Mostly browsing Convenience store Pick-up meal Other shopping

Tag along with parent

Play, socializing

Homework

With babysitter

Other just for kids

Exercise or active sports Movies, theatre Other spectator events Playing with kids Parks, recreation areas Regular TV programs Unspecific TV Movie video Relaxing, pleasure reading, napping Hobbies (crafts, gardening, etc.) Other recreation, entertainment

Visiting Hosting visitors Cultural events Religious events Planned social events Bars, special clubs Phone or e-mail >10 min.

Helping others Other social

Tag along travel Pleasure driving

the unique sociodemographic nature of the population in question Household members should be asked

to describe, in their own words, the specific activities of each type that they perform, along with their attributes In practice, some further structure is needed to guide households along in this procedure An example protocol used in practice to define activity types and their typical locations is provided in

Figure 7.3 Following this step, household members can be asked to describe the specific attributes of each of the activities mentioned This can be done immediately or could be delayed until the end of an associated scheduling survey, which offers some advantage in terms of avoiding certain attribute questions for activities that were observed repeatedly enough

The number, format, and precision of attribute responses garnished can vary considerably, as can the length of time needed for the interview For example, the definition of an average of 42 activities per household, along with five main attributes (common locations; normal frequency; normal, minimum, and maximum duration; applicable days; earliest start and latest end times), required an average of 45 min to

1 h of interview time in two such studies in Canada (see also Doherty and Miller, 2000) Aside from these time constraints, the main difficulty that will arise is deciding what attributes to ask for, how to ask for them, and when (for example questions, see Figure 7.4) In particular, the attributes of activities often depended on who was doing it (one person or joint) and on what days (e.g., weekday vs weekend) In these cases, a new activity could be defined separately for each person or day(s), but in practice, this is often not possible Recording possible locations (especially for flexible activities such as shopping), possible time windows, and normal durations also poses unique challenges to respondents in terms of recall

A third alternative for investigating household activity agendas is to automate all or a portion of the in-depth interview task using a sequence of computer-generated forms and dialogs The face-to-face or telephone interview could be limited to asking households about the typical activities that they perform (using, for example, the protocol in Figure 7.3), entering them directly into a database in the household’s own words, but coded at the same time with generic labels The remainder of the attributes of activities could then be queried on computer (or the Internet) using a sequence of dialog boxes that the user completes independently An example of queries for five possible attributes is shown in Figure 7.4 Using such an approach is likely to reduce the interview time, although the resulting implications for data quality are still relatively unknown

1 Start by informing the subject that you will be asking them about the types of activities they do

in a range of categories

2 Then let them know the purpose of this exercise is to:

a Give them an idea of the level of detail that is sought during the week

b Make it easier during the scheduling exercise to select activity types from a list

3 Then let them know that they can always enter a “new” activity during the week if they miss it during the interview

4 Finally, let them know that there are three types of activities that you will record for them:

a Generic activities: Activities that most everyone does, in which case you inform them only

of the activity name

b Catch-all activities: Activities similar to the above, except that the activity is a “catch all” for

a variety of activities that are not of interest separately, and will make data entry easier (e.g., cleaning or chores)

c Personal activities: Other activities personal to them, in which case you ask them generally,

“What type of <x> activities do you do,” wherein the <x> values are the general category names: work, shopping, recreation, etc Once they name a specific activity (e.g., grocery shopping), you should type a description that matches the subject’s own words Only if the subject does not come up with any ideas at first should a suggestion be made

5 You should try to define as many activities as possible that are likely to be done in the coming week

FIGURE 7.3 Interview protocol for defining the range of activity types performed by households.

Owing to these challenges, it is too early to draw conclusions about the types and format of attributes that should be sought and the best methods Which attributes are most influential in the scheduling process also remains an important analytical research question Their choice should be balanced against our ability to eventually simulate them as part of a larger activity scheduling model For instance, activity

FIGURE 7.4 Example of automated computer dialog boxes for investigating activity attributes.

frequency and duration could be simulated using traditional diary data — if these two attributes were sufficient as predictors of the subsequent scheduling process, the task of agenda simulation would be eased However, theoretically, it is likely that temporal, spatial, and interpersonal flexibility and fixity play a key role in the scheduling process, the effects of which require further rigorous testing before more definitive progress can be made.

7.4.3 Investigating the Dynamics of the Activity Scheduling Process

Taking activity agenda formation as given, and holding the process of habit formation and learning fixed

in the short term, allows a clear focus on the dynamics of the scheduling process As shown in Figure 7.1, the key components of this dynamic and continuous process include preplanning, impulsive and dynamic, and adaptive scheduling behaviors — each of which is explored in depth in the following sections

7.4.3.1 Preplanning Decision Processes

In everyday life, it is very common for individuals and households to begin any given day or week with a set of routine, regular, or everyday activities that form a type of skeleton around which other scheduling decisions are made In the least, this would include the act of sleeping, a necessity that bounds our daily behavior For others, a range of other activities is included in their skeleton schedule, along with periods

of unplanned time Doherty and Miller (2000) have shown that the number of activities routinely planned

in advance differs substantially between individuals, but averages about 40% of activities (~60% of the time).Observing and eventually predicting what activities are preplanned on the skeleton schedule is a priority challenge The traditional approach is to assume that activity types such as work and school are mandatory and thus constitute the primary pegs in a skeleton schedule However, such an assumption may not necessarily hold for all people at all times — such as teleworkers or unemployed persons who have much more flexible schedules A range of other activity types traditionally considered discretionary may also be included in the skeleton, especially if they share some of the same characteristics of the more mandatory activities (e.g., attending to children, sporting events) Addressing this assumption requires further investigation

Predicting the activity types for inclusion on the skeleton is, however, only the start The remaining specific attributes of the activities on the schedule — precise start and end times, location, involved persons, etc — require further simulation, even if they are relatively fixed or highly constrained The degree to which these interdependent decisions are decided in a fixed vs variable sequence is an important area of investigation, especially in terms of eventual modeling assumptions Is the timing of an activity

or location decided first? What about mode, involved persons, etc.? What attributes may be left undecided? What alternative sequences are possible and under what conditions? Which attributes may later be modified, under what conditions, and to what degree? What agenda attributes and situational factors would serve as the best explanatory variables of whether the activity is preplanned on the skeleton? The answer to these questions is obviously important to the predictive and behavioral validity of models, since each decision is inherently constrained by earlier ones Existing models most often assume a fixed

or simultaneous decision sequence in lieu of any alternative information

In order to investigate these issues, individuals and households should be queried about what they have planned in advance for a given future day or week In practice, the wording of the question requires considerable care For example, people could be asked “What activities have you already thought about conducting for this week/day?” or “What have you planned for the coming day/week?” The difficulty for some people is deciding what “thought about” or “planned” really means Do all attributes of an activity have to be “thought about” before it is considered planned? What about the fact that some attributes of activities may be preplanned, while others are not? Difficulty also arises with routine activities that people conduct with very little contemplation whatsoever

Given these concerns, two types of queries could be asked of an individual or household concerning their preplanned activities: