An Activity Scheduling Process Approach to Understanding Travel Behavior Sean T. Doherty Department of Civil Engineering University of Toronto Toronto, Ontario Canada M5S 1A4 Phone: (416) 978-5049 Fax: (416) 978-6813 E-mail:
[email protected]
Paper presented at 79th Annual Meeting of the Transportation Research Board, Washington, DC, January 9- 13th, 2000 (#00-0944).
ABSTRACT The need to understand and forecast individual travel behavior over longer periods of time and space has placed tremendous stress on available data collection and modelling techniques. Traditional travel survey methods tend to focus exclusively on observed choices, using diary and stated preference techniques. However, improving our understanding requires more than just an improved accounting of observing outcomes – rather, travel behavior researchers are increasingly recognizing the need to improve our understanding of the decision processes that underlying observed outcomes. Assumptions about these decision processes form the basis of an emerging class of activity scheduling models. This paper is concerned with outlining a new approach to understanding travel behavior that intrinsically recognizes that observed travel patterns are really the result of unobserved and underlying activity scheduling decision process. Empirical evidence gathered through the use of a new computerized household activity scheduling elicitor survey is used to describe the fundamentals of this process. In particular, evidence presented in this paper demonstrates that activity scheduling is a highly dynamic process occurring over many time horizons, with significant levels of revisions and continued pre-planning during schedule execution. This represents a significant challenge to past modelling assumptions that activities are planned simultaneously, or that they are planned and executed in the same sequence. The paper concludes with a discussion on modelling implications and future directions. Keywords: Activities, travel, behavior, scheduling, decision processes, survey methods.
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INTRODUCTION Individuals and households seem to make decisions about what to do and where to go on a variety of time scales, including both pre-planned and impulsive decisions. This process of planning and organizing our lives is something we all do, yet as researchers we know relatively little about. The outcome is a complex pattern of daily and weekly activities and travel that serve to define the functionality and structure of urban areas, and is of vital importance to the environment. This paper proposes an approach to understanding travel behavior that explicitly recognizes that observed activity-travel patterns are really the result of an underlying activity scheduling process. Empirical evidence derived from a Computerized Household Activity Scheduling Elicitor survey (CHASE) is used to describe the fundamentals of this process and address key issues related to the sequencing and priority of activity decisions. The implications of these results for activity schedule model development is explored, particularly in reference to the validity of past assumptions. Overall, this paper is meant to challenge planners, researchers and modellers to think more deeply about underlying decision processes and the assumptions inherent in their models. Travel, Activities, and the Activity Scheduling Process In the literature, it is widely accepted that travel is derived from the need to participate in activities outside the home. This realization has lead to the development of an activity-based approach during the last few decades, in an effort to improve travel demand forecasting and better assess the impacts of emerging transportation policies. The rational for an activity-based approach has been well documented (1, 2, 3). Travel is considered to form part of a continuous pattern of daily behavior, depicted as a single sequence of activities in time and space. This pattern of activities for a day (or longer), often termed an activity schedule, includes the interdependent choices of what activities to participate in, where, for how long, in what sequencing (including choices of start and end times), coupled with mode and route choices. Observed activity schedule patterns are arguably the outcome of an unobserved activity scheduling decision process. For the purposes of this paper, this scheduling process is defined as the planning and execution of activity-related decisions over time within a household. Analogous to the way travel is considered to result from the formation of activity patterns, Figure 1 illustrates how activity patterns result from a process of schedule formation, depicted as a sequence of scheduling phases over time. In the initial sequencing phase (part a), a person’s schedule may consist of a set of routinely planned activities, such as sleeping, eating breakfast, working and dropping-off of children at school. The rest of the person’s time remains unplanned. Moving closer to execution (part b), a person may make further scheduling decisions on the same day, or a few days in advance, such as planning to make dinner at home or planning to go to the cinema. At this time, the person may also need to modify previous plans, such as is the case with night sleep. During execution of the schedule (part c), a series of impulsive decisions may be made in reaction to unexpected events or to fill in unplanned time. For instance, as a result of planning to make dinner, a person may realizes on the way home from work that they need to go grocery shopping. This may also lead to subsequent modifications in meal preparation time. Unplanned changes in the duration of activities may also occur, such as the case with the time spent at the cinema. The result of this process is the final observed activity-travel pattern.
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It is the scheduling process depicted in Figure 1, occurring over various time horizons, that is proposed in this paper as the behavioral process that underlies observed activity pattern outcomes. The validity of this framework is explored further in the discussion section of this paper. While this approach may lead to an improved understanding of travel behavior, it comes at a cost in terms of the complexity of observation task. Developing models of activity schedules has also been met with considerable challenge. Part of the problem is that data collection and modelling efforts have focussed largely on observed activity schedules rather than underlying decision processes. The important question to ask is whether better description of activities and trips will actually lead to increased understanding of travel behavior? The continuous focus on collecting larger samples of more detailed observed patterns, especially those provided by activity diaries, panels, and emerging technologies (e.g. GPS traces of spatial movements), may only lead to more confusion by analysts and modellers trying to understanding and replicate what they see. Given the lack of understanding and availability of data sources, travel behavior researchers are increasingly recognizing the need for more in-depth research into scheduling processes. One of the earliest to recognize this need was Pas (4) who noted that existing theories and methodologies dealt almost exclusively travel behavior at particular points in time, but that travel behavior “requires the development of models of the process by which travel and related behavior change” (p. 461). Subsequent researchers, such as Jones et al. (5), Axhausen and Gärling (6), Lee-Gosselin (7), and Axhausen (8) have emphasized in general, that the (re)scheduling process is at the core of many of the changes in travel behavior brought on by recent policy initiatives related to information technology and transportation demand management. Activity Schedule Models Two basic approaches have been adopted for activity schedule modelling. The first approach focuses on the simultaneous choice among a set of activity-travel patterns. Examples include CARLA (3), STARCHILD (9, 10), and Kawakami and Isobe (11). In most cases, a utility maximization framework is adopted to predict an individuals choice of activity-travel pattern. From a behavioral standpoint, these models have been criticised for assuming that the scheduling process is static, and that decisions are made based on utility maximization. Gärling et al. (12) argues that an even more serious issue relates to the tendency of traditional models to be confined to specifying what factors affect the final choice of pattern whereas the process resulting in the choice “is largely left unspecified”. In the case of the STARCHILD model, the authors did concede that “extensive rethinking of planned versus unplanned activities appears appropriate” and that “a less static simulation structure which can reflect pattern formation as a dynamic process” needs to be integrated into their model. A second approach to activity schedule modelling focuses more explicitly on the replication of the sequencing of decisions made during the scheduling process. Some of the first attempts focused on the sequential choice of activities and locations (13, 14, 15). Building on this approach, Kitamura et al. (16) recently proposed a simulation model of activity-travel patterns in which the next activity type to be engaged in is first chosen, followed by it’s duration, then location. This model assumes that activities are planned and executed in sequence with reference to the past history of the behavior. Estimation results using a multinomial logit activity type choice model indicated that history dependence was significant in explaining the likelihood
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of repeating certain activity types (e.g. social recreational) but not all (e.g. return to work). Although innovative in the use of past history variables, the authors did not consider the effects of future planned activities in the model, citing the lack of data availability. Vaughn et al. (17) takes a two-step approach to generating activity-travel patterns. First, a “skeletal” activity pattern is generated based on demographics, followed by more detailed simulation based on observed probability distributions. Recognizing the behavioral and computational difficulties associated with econometric approaches, the authors opt instead to use a combination of sampling and simulation methods to generate the skeletal patterns from observed data. In the initial application, only out-of-home activity locations were left out of the simulated skeletal patterns, and were later replicated using a spatial interaction model. This in turn led to some modifications in activity start and end times. Although this applied model could be considering somewhat limited in scope, it demonstrates the challenge of attempting to incorporate process in the model when only observed data is available. A series of additional sequential models have evolved from the SCHEDULER conceptual framework as proposed by Gärling et al. (12, 18). These include SMASH (19), and models by Gärling et al. (20) and Kwan (21). The SCHEDULER framework proposes that a set of activities is chosen from the individual’s memory and a schedule is formed by selecting activities with the highest priority followed by attempts to find less prioritized activities that fit into open time slots. It is further recognized that priorities are generally enduring but change depending the saliency of different goals. In practise, simplifying assumptions have been adopted in the specification of priority. Kwan (21) classified priority according to whether the activities were considered obligatory or discretionary, citing the lack of alternative information. The SMASH model starts with a list of activities to schedule and uses a static measure of priority measured on a 10-point scale, together with the activities earliest start/latest end time and duration, to determine how an individual’s schedule is successively constructed. The recent simulation scheduling model operationalized by Gärling et al. (20) considers the priority of activities to vary over time, but takes this variation as a given in the subsequent model. A similar assumption is made for the recently proposed ALBATROSS model (22, 23) in that a sequencing rule is proposed that stipulates that mandatory activities be completed before discretionary ones, and out of-home before in-home activities. One of the overriding themes in these models is the notion of activity priority acting as a primary determinant in the sequencing of activities. While the purposes of using priority is often made clear (i.e. to determine what activity(s) to scheduled first and so on) few provide an operational definition of what it really represents or attempt to model it endogenously. Instead, priority is often assigned by activity type (i.e. mandatory and discretionary) or by some other static method, which may result in a model that is insensitive to differences across individuals and situations. Many of the authors cite a lack of information on this issue as the reason for their assumptions, and recognize that further empirical work is needed to support advances in this key component of their models. DATA COLLECTION APPROACH Despite the need, very few data collection efforts have targeted this underlying activity scheduling process as a means to improve model development. Exceptions include Hayes-Roth and Hayes-Roth (24) who used a “think aloud protocol” to investigate the kinds of behavior exhibited when people are posed with a series of errands to perform, and Ettema et al. (25) who used an interactive computer experiment to identify the types of steps people used to construct a
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hypothetical one-day schedule. The Computerized Household Activity Scheduling Elicitor (CHASE) survey developed by Doherty and Miller (26) goes beyond these methods by providing a means to observe the scheduling process as it occurs in reality in a household setting over a multi-day period. In this way it is able to capture both routine and complex scheduling processes as well as observe those scheduling decisions made during the actual execution of the schedule, which has turned out to be substantial (see Results section). The CHASE program is designed to track the sequence of activity scheduling steps (additions, modifications and deletions) taken by individuals in a household to construct their weekly schedule. An upfront interview is used to establish a household’s activity “agenda” consisting of a full list of activities potentially performed by household members, along with their attributes. This information is displayed by the CHASE program to the user in choice situations. Users are basically instructed to login daily to the program for a week long period starting on a Sunday, and continuously add, modify, and delete activities to an ongoing display of their weekly schedule (Monday-following Sunday), not unlike a typical dayplanner. Aside from these basic scheduling options, the program automatically prompts the user for all additional information. Most importantly for the analysis in this paper, the program queries respondents on the timing/sequencing of their scheduling decisions when needed. For instance, when adding activities to their schedule after-the-fact, respondents are asked “When did you actually make the decisions to add this activity?”, followed by a check list of options including “During the activity”; “Just before the activity”; “Prior to the activity on the same day”; “More than a day ago on ”; “Prior to this week”; or “It is a routine even with no real decision associate with it.” This information is necessary to place the decision in the context of previous scheduling steps and to avoid over-reporting of impulsive decisions. The result of the CHASE survey is a highly detailed trace of a households scheduling decisions along with the resulting activity-travel pattern outcomes. Doherty and Miller (26) showed that the program has a relatively low respondent burden (an average of 18 minutes per day was needed to complete the survey) and minimizes fatigue effects commonly associate with multi-day surveys. This approach goes a long way towards solving the data collection problem highlighted by Axhausen (8) and addresses the modelling concerns of Bowman and Ben-Akiva (27) that simulation models of activity scheduling require “very complex surveys for model estimation” wherein “respondents must step through the entire schedule building process”. In fact, stepping through the scheduling process, which we all do everyday, appears to be a more natural approach to obtaining information on final outcomes involving no more burden then traditional diary-based techniques. Despite the efficiency of the program, several areas for improvement have been identified related to possible instrument biases and the improved the tracing of decision sequencing, and are being addressed as part of future development of the program (28, 29). CHASE data from a sample of 40 households (65 adults) in Hamilton, Ontario, Canada in 1997 is used as a basis for the analysis in this paper. The households represented a roughly equal mix of married couples, married couples with children, and single person households. The majority of households were located within two kilometers of the McMaster University campus, which is situated at the very tip of the western end of Lake Ontario (Hamilton region population: ~600,000). Households were paid $50 for their participation.
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ANALYSIS OBJECTIVES The analysis objective of this paper are two-fold: 1) to explore the fundamentals of the activity scheduling process and 2) to address the specific hypothesis/assumption that activities are planned and executed in order. The first objective is meant to provide support for the “activity scheduling process” approach proposed in the introduction to this paper. While of interest on its own, the applied value of such analysis rests in its implications for model development. Hence, the second objective focuses more specifically on one of the primary assumptions of existing activity scheduling models. The implications of these results are discussed in context of the sequencing and priority assumptions of past models in an effort to identify new avenues for future model development. RESULTS Fundamentals of the Scheduling Process Empirical evidence derived from the CHASE survey provides considerable insights into the fundamentals of the activity scheduling process. Results indicate that households begin the week with a pre-planned set of routine weekly activities. On average, 45% of weekday and 20% of weekend activities were pre-planned on the First Sunday of scheduling (remembering that users began scheduling on a Sunday for the activities that take place Monday to the following Sunday). This represents an average of 34 activities per adult pre-planned on the first Sunday. Of the decisions, a full 70% were part of multi-day entries (the activity was added on 2 or more days simultaneously), with 80% of these consisting of entries across 4+ days. Comparatively, on Monday, only 21% additions were part of multi-day entries, followed by 2%, 6% and no more than 1% on remaining days of the week. Such repetitive entries are indicative of highly frequent, routine activities. Other characteristics, such as longer durations (double those of other planned activities) and a higher degree of spatial-temporal fixity, differentiate these routine activities. After this first Sunday, a more active, opportunistic, and impulsive mix of decisions follows. On average, adults make about 8 additions, 2 modifications, and 1 deletion per day during the execution of their schedule over the course of the week, which include an average of 12.4 activities and 4.9 trips per adult per day. These scheduling decisions are made on a variety of time horizons. Outside of the routine activity additions made on the first Sunday (38%), a substantial proportion of additions are scheduled impulsively just before execution (28%), on the same day (20%), or are pre-planned one or more days in advance during the week (15%). When pre-planning during the week, adults were found to reach out beyond one day 38% of the time to make an addition, in an opportunistic fashion. The distribution of time horizons for modifications and deletions differed, as more modifications occurred impulsively (62%), while more deletions are made the same day (38%), reflecting more forethought for deletions compared to modifications. Sequencing As an alternative means to investigating the activity scheduling process, a graphical investigation of the sequencing of activity decisions was performed. For all 65 adults in the sample, a graph was created showing each scheduling decision (add, modify and delete) on the x-axis ordered in sequence, by what day/time it was planned for on the y-axis. For instance, in
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the example in Figure 2, the individuals first decision was to add an activity for Monday 0600 hours, followed by an addition for Tuesday 0600 hours, etc. A ^ is added above any add/modify/delete point on the graphs to indicate that it was impulsively planned. For instance, on Monday, the user impulsively added five activities, then pre-planned two activities for the next day, etc. This format was carefully chosen in order to reveal the sequential nature of the scheduling process. Most importantly, if an individual planned and executed their activities in sequence, then the points on the graph would line up in a linearly increasing fashion within the boxes bounded by the intersections of the days on each axis. The joining of the points by a line is meant to illustrate this concept. Any deviation from this pattern indicates a mixture of planning at different time horizons. Modifications in previously planned activities indicates further variety in the scheduling process. It should be noted that entries on the first Sunday may or may not be in exact sequence, as users were not prompted for the precise order in which these decisions were made. However, entries to follow the first Sunday are assumed to fit more closely to the sequencing of decisions in reality, as information from the user about when the decision was actually made (as derived from prompts in the CHASE program) was used to re-order them in context of previous decisions. Upon visual examination of all 65 adults in the survey, three unique styles of scheduling behavior were evident. An example of each of these scheduling styles is shown in Figures 2, 3 and 4, and explained below. Overall, approximately 20% of adults in the sample resembled example 1 (“Straightforward”), 50% resembled example 2 (“Semi-structured”), and 30% resembled example 3 (“Highly structured, unordered, and opportunistic”). While all three individuals in the examples had a similar number of observed activities per day (averaging 9.7, 10.9, 10.7 activities per day respectively), their underlying scheduling behavior was clearly much different. Example 1 – Straightforward scheduling behavior (see Figure 2) This type of scheduling behavior is characterized by the scheduling of few routine activities (in the example, only 8% of activities routinely planned in advance) and a low level of subsequent scheduling activity (in the example, a total of 76 scheduling decisions were recorded) consisting mostly of impulsive decisions (in the example, 64% of decisions made impulsively). This is an example of a person who pretty much lives life as it comes, with very little structure, generally planning and executing activities in the same order. Further examination revealed that a majority of these people were single. Example 2 – Semi-structured, but straight-forward scheduling behavior (see Figure 3) This type of scheduling behavior is characterized by a higher proportion of routinely preplanned activities (22%), and a higher degree of overall scheduling activity. This includes an increase in total scheduling decisions (98), with a higher proportion modifications (17%) and deletions (3%) over the proceeding example. However, a high proportion of these decisions continue to be made impulsive (55%). This is an example of a person continues to live life as it comes, albeit with a higher degree of initial structure, resulting in the need for more scheduling decisions and modifications during execution.
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Example 3 – Highly structured, unordered and opportunistic scheduling behavior (see Figure 4) This type of scheduling behavior is characterized by a high proportion of routine activities (25%), plus more same-day (29%) and continued weekly pre-plan decisions (19%) compared to the previous examples. The total number of scheduling decisions is also much higher (133), with a higher proportion of these consisting of modifications (22%) and deletions (10%). Note also the development of “sub-plans” in their schedule – groupings of 2 or more activities pre-planned during the week for a day later in the same week. This is an example of person with a high degree of structure and need to continue pre-planning during the execution of their schedule, resulting in a very unordered and somewhat opportunistic mix of scheduling decisions. SUMMARY AND DISCUSSION “The main problem for most existing model types is that they are trying to capture policies aiming to modulate demand in time and space in a static framework. The frameworks are static as they assume that the whole period they treat, be it an hour or a whole day, is planned and optimized in one step and that all activities are optimized with reference to that period only. If the reverse is true, that is, that persons do not construct their behavior in one step and that all of their activities have meaning a longer time frames, then new types of models are necessary to describe the effect of policies with confidence … At this stage, I can only speculate given the absence of suitable empirical work – assuming it is possible” Axhausen (8) pp. 306 – 307.
Overall, the results presented in this paper provide straightforward evidence that it is indeed possible to collect empirical evidence on activity scheduling as it occurs in reality, and that activities are definitely not planned and optimized in one step. These results also challenge the assumption that activities are planned and executed in order without further revision. Only a small proportion of people in the sample exhibited such behavior, consisting of mostly single people. This may partially explain the previous conclusions of Ettema et al. (25) who found that a sample of students in their study used rather straightforward planning strategies in which the activities are scheduled in the expected order of execution with few later modifications. Instead, the evidence presented in this papers suggests that activity scheduling is a highly dynamic process occurring over many time horizons, and that for many people it involves significant amounts of rescheduling during execution and can be both highly impulsive and opportunistic. The time horizons for activity scheduling ranged from those activities planned routinely in advance before the scheduling week commenced, to those planned impulsively just before or during execution of activities. Intervening in this process was continued pre-planning efforts throughout the week. While psychological factors likely play a role, evidence suggests that it is the attributes of activities in a persons life (or on their “agenda”) that are the root cause of subsequent scheduling and sequencing style. For instance, analysis of the characteristics of pre-planned activities suggests that people who tend to do more multi-day and longer duration activities will end up pre-planning more of their activities in advance. In turn, these “routine” like activities provide a level of structure that constrains subsequent decisions, and leads to an increase in scheduling modifications. These results lend support to the conceptualization presented in Figure 1 depicting the phases in the activity scheduling process. Such an approach represents a natural extension of the activity-based approach, in that activity pattern did indeed appear to be derived from a process involving the scheduling of activities over time. However, it also suggests that the extent and variety of decisions in each phase may differ across individuals, and that these differences are
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related to characteristics of the planned activities. This depiction also does not consider that some planned activities may not be fully realized to the extent that all attributes are planned ahead of time. Further development of this scheduling process approach is clearly needed. It is hoped that this development encourages new data collection efforts that parallel the increasing attention being paid to the collection of larger samples of more detailed observed patterns. The contribution of such an approach to model development, as discussed below, is also significant. Modelling Implications Given the results of this paper, the search for new types of models to describe the effects of polices does indeed appear necessary, as suggested earlier by Axhausen. In the least, the analysis in this paper disproves the assumption of simultaneous scheduling models that activity scheduling is a static process wherein people choose amongst a set of feasible schedules to conduct without further revisions. Instead, this analysis lends support to the approach adopted by Vaughn et al.’s (17) who have integrated in their simultaneous model a dual-step process of “skeletal” schedule construction, followed by subsequent location and timing decisions. Future development of such models should consider, however, that not all activities and their attributes are included in the skeleton, and that many more types of choices are made in subsequent steps. The results of this paper lend more significant support to an emerging class of sequentially based scheduling models. One of the initial challenges of these models is to accurately reflecting the nature of activity decision sequencing with a minimum of assumptions. Given a lack of data, the majority of sequencing models have assumed that activities are planned and executed in the same order, in contrast to the findings of this paper. Instead, the sequencing patterns observed in Figures 2-4 suggests that the circumstances within which decisions are made include the consideration of what has already been planned for future time periods, which also limits and how much time is available. Modifying existing scheduling models to allow the scheduling of activities for future time periods, and conditioning subsequent decisions upon the nature of these future decisions, would appear to bring about an improvement in behavioral validity and policy sensitivity. For instance, in the model developed by Kitamura et al. (13), a dummy variable indicating the “future” planning of activities could be incorporated in the model, as could other new variables reflecting the future state of the schedule. In the case of sequential models that use activity “priority” as a major determinant in the sequential choice of activities to add to a schedule (e.g. 19, 20, 21, 22), the results suggest that the assignment of priority by activity type needs to be expanded. If one assumes that priority is linked to sequencing, then it follows that the “routine” activities planned on the first Sunday were of high priority, whereas those to follow were of lessor priority. Given that routine activities exhibited unique characteristics (e.g. high frequency, longer durations), it is plausible that a model could be developed to differentiate then from other activities based on an expanded set of characteristics. Further analysis of how different situational factors effect the priority of activity choice at any given moment during the scheduling process could lead to the identification of additional factors that add a dynamic quality to the priority model (e.g. time since last performance, time to next scheduled performance, or the duration of the activity relative to the duration of unplanned time windows available). Knowing these same attributes for other household members allows additional factors to be considered. Thus, instead of assigning priority statically by activity type (e.g. the assumption that mandatory activities are high priority while discretionary are low), it could be modeled depending upon the characteristics of the activities being considered and the situation at hand. This is especially
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important in light of emerging policies that will tend to blur the boundaries between what is mandatory versus discretionary, such as the case when “work” (normally considered a mandatory activity) becomes more flexible both in time (e.g. flex-hours) and space (e.g. telecommuting). It may also be important for modelling households that do not have traditionally defined mandatory activity types. Avenues for Further Research The proceeding analysis has already hinted at several avenues for further research, including: further development of the scheduling process approach depicted in Figure 1; the advancement of sequential scheduling models over simultaneous ones; and the need to expanded the definition of activity “priority”. This sections explore several additional avenues for further research. One important future analysis issue is the need to more thoroughly examine the characteristics of routine, weekly pre-planned , and impulsive activities. A more rigorous multivariate classification of these activities would help to more definitively assess the contribution of activity attributes such as frequency and duration in explaining the choice of activities at each stage in the scheduling process. A whole range of additional attributes related to the spatial and temporal fixity of activities should also be investigated (e.g. number of locations available for the activity, or the ratio of activity duration to the difference in the activities earliest and latest possible end time). An important future modelling issue is the extent to which existing activity/travel diary data could be used to simulate these activity attributes for a given population as input to a scheduling simulation model. If so, the proposed classification could be used as a means to order the selections of activities in a purely sequential model, or serve as a means to limit the set of activities to be included in an initial skeletal-type scheduling model. A related issue is the extent to which differences in scheduling behavior can be attributed to personal or household-related characteristics. It may be that certain types of people simply prefer different ways of planning their lives. For instance, a “busy” person may exhibit highly structured, unordered and opportunistic scheduling behavior (e.g. Figure 4), whereas a person with a more “laid back” personality may exhibit a straightforward scheduling style (e.g. Figure 2). While this may be the case, the results in this paper suggest that it may be the attributes of activities on a person’s agenda that are the root cause of scheduling style. For instance, it may be that the “busy” person has a lot of highly frequent, routine activities that simply must be preplanned , which constrains many of their latter activities and leads to the more varied scheduling style, whereas the more “laid back” person has fewer routine activities and is able to plan and execute their life as it comes with less scheduling pressure. Including personal/household related characteristics in the classification of activities suggested above would help quantify the relative contribution. Additional exploration of factors such as personality and cognition may shed further light on this issue. One of the shortcoming of data collection tool is that it did not allow subjects to omit specific attributes of activities that were yet to be decided (the new version described in (28) addresses this issue). This meant that it was not possible to examine whether activity attributes such as location, involved persons, timing, or mode were planned differently from others. While the choice and activities is normally regarded as a first step in sequential scheduling models, little is known about how subsequent choices are sequenced. Such an issue should be a priority for future data collections. Also related to survey design is a need to investigate how “routine” activities evolve. In the present survey, people started with a blank slate on the first Sunday, whereas in reality it is
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unlikely that a person’s planned schedule starts blank. One approach would be to prompts users for information on the sequencing of these decisions in CHASE, or possibly to extend the survey to longer periods of time. However, given human memory limitations, it may be more valuable to utilize alternative techniques more amendable to long-term decisions making processes. The decision rules used to actually make the decisions at each step in the scheduling process is an additional area of concern for future model development. While sequencing is important in establish the initial sequence of decisions, the types of decisions rules people actually use in this context are relatively unknown. Most modellers assume that decisions are made based on principles of utility maximization, leading to the specification of logit-type models. However, insights from cognitive psychology about how people perform complex scheduling tasks suggests that people apply a large range of heuristics and strategies when faced with such tasks (24, 30). While an instrument such as CHASE may provides a means to constrain the number of options feasibly considered in a given situation, alternative methods are needed to investigate how these options are actually compared and the relevant factors involved. It may turn out that the decision rules exhibit more stability across individuals and households than the array of seemingly complex observed patterns to result, analogous to the way a simple car-following rule in traffic flow models leads to the emergence of complex queuing formations in traffic flow simulations. Of course, many of these suggestions depend upon the collection of larger samples of data and enhancements to survey design. While we will likely never be able to trace decisions process with complete accuracy, it is hoped that this analysis at least demonstrates what can be accomplished with interactive survey techniques that go beyond observed patterns. Lessons learned in this first application have already been incorporated in new survey developments meant to make larger data collection efforts possible through the use of the internet, and the combination of passive tracing devices such as GPS. It is hoped that the analysis presented in this paper provides fruitful directions for more in-depth future exploration of scheduling processes. CONCLUSIONS One important question implicitly addressed in this is paper is whether the current focus on “outcomes”, both in terms of data collection and model development, is really hampering our ability to understand and forecast the impacts emerging policies. If our goal is simply to describe existing patterns to derive key indicators of travel demand, and to replicate these observed patterns in the form of a model, then we appear to have the tools to do so and no changes are needed. Local and national activity/travel diaries and emerging observation technologies such as GPS provide a plethora of observed data, and modellers have taken on the task of applying more sophisticated computational and simulation techniques to the replication of the patterns they observe. However, if the goal is to forecast travel demand in response to emerging policies or trends, then models must attempt to go beyond replication of observed outcomes, and explicitly account for the behavioral mechanisms by which individuals and households respond to change. The goal of this paper was to propose an approach to understanding travel behavior that explicitly recognizes that observed activity-travel patterns are really the result of an underlying activity scheduling decision making process. Evidence from a computerized household activity scheduling elicitor survey was used to explore the fundamentals of this process. What was shown was that activity scheduling is a rather dynamic process occurring over many time
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horizons, with significant levels of revisions and continued pre-planning during schedule execution. This represents a significant challenge to past modelling assumptions that activities are planned simultaneously, or that they are planned and executed in the same sequence. The analysis also provided an opportunity to make specific suggestions concerning the classification activity priority, and the definition of new explanatory variables related to an individual’s scheduling state at the moment decisions are made. A clear opportunity exists to further utilize this type of data in the development of an emerging stream of sequentially-based scheduling process models with the potential to improve our ability to assessing the impacts of emerging travel demand management policies that inherently invoke a rescheduling response. ACKNOWLEDGEMENTS The author would like to acknowledge the financial support provided by a collaborative research grant from the Natural Science and Engineering Research Council of Canada that supported the data collection, and financial support provided to the author from the Social Science and Humanities Research Council of Canada. Special thanks go out to all those who participated in the survey, and to colleagues who provided valuable commentary on the survey design. The author is also appreciative of the valuable comments received from anonymous reviewers on an early draft of this paper.
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15. van der Hoorn, T. 1983. Development of an activity model using a one-week activity-diary data base. In Recent Advances in Travel Demand Analysis, eds. S. Carpenter, and P. Jones, 335-349. Gower, Aldershot. 16. Kitamura, R., C. Chen, R. Pendyala, 1997. Generation of synthetic daily activity-travel patterns. Transportation Research Record 1607: 154-162. 17. Vaughn, K. M., Speckman, P. and Pas, E. I. 1997. Generating household activity-travel patterns (HATPs) for synthetic populations. Paper presented at the 76th Annual Meeting of the Transportation Research Board, Washington, D.C, January 12-16,. 18. Gärling, T., M.-P. Kwan and R. G. Golledge, 1994. Computational-process modelling of household travel decisions using a geographic information system. Papers in Regional Science 73(2): 99-117. 19. Ettema, D., A. Borgers and H. Timmermans, 1993. Simulation model of activity scheduling behavior. Transportation Research Record 1413: 1-11. 20. Gärling, T., T. Kalén, J. Romanus and M. Selart, 1998. Computer simulation of household activity scheduling. Environment and Planning A 30: 665-679. 21. Kwan, M-P. 1997. GISICAS: An activity-based travel decision support system using a GISInterface computational-process model. In: Activity-based approaches to travel analysis, eds. D. Ettema and H. Timmermans, 263-282. Pergamon, Oxford. 22. Arentze, T.A., Hofman, F. Joh, C.H., and Timmermans, H.J.P. 1999. The development of ALBATROSS: Some key issues. In Traffic and Mobility: Simulation-EconomicsEnvironment, eds. W. Brilon, F. Huber, M. Schreckengerg, and H. Wallentowitz, 57-72. Springer, Berlin. 23. Arentze, T.A., Hofman, Van Mourik, H.F., and Timmermans, H.J.P. 2000. ALBATROSS: A Multi-Agent Rule-Based Model of Activity Pattern Decisions. Paper presented at the at the 79th Annual Meeting of the Transportation Research Board, Washington, DC, January 913th (00-0191). 24. Hayes-Roth, B. and Hayes-Roth, F. 1979. A cognitive model of planning. Cognitive Science 3: 275-310. 25. Ettema, D., Borgers, A., and Timmermans, H. 1994. Using interactive computer experiments for identifying activity scheduling heuristics. Paper presented at the Seventh International Conference on Travel Behaviour, Valle Nevado, Satiago, Chile, June 13-16. 26. Doherty, S. T. and Miller, E. J. (forthcoming). A computerized household activity scheduling survey. Transportation. 27. Bowman, J.L. and M. Ben-Akiva, 1997. Activity-based travel forecasting. In: Summary, Recommendations and Compendium of Papers from the Activity-based Travel Forecasting Conference, June 2-5, 1996, 3-36. Sponsored by the Travel Model Improvement Program.
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28 Lee, M. S., Doherty, S. T., Sabetiashraf, R. and McNally, M. G. 2000. iCHASE: An internet Computerized Household Activity Scheduling Elicitor survey. Paper presented at the 79th Annual Meeting of the Transportation Research Board, Washington, DC, January 9- 13th, 2000. 29. Doherty, S. T., Noël, N., Lee-Gosselin, M., Sirois, C., and Ueno, M. 1999. Moving Beyond Observed Outcomes: Integrating Global Positioning Systems and Interactive ComputerBased Travel Behaviour Surveys. Paper presented at the Transportation Research Board Data Committees Mid-year Meeting, Washington, DC, June 27-June 28. 30. Payne, J.W. , J.R. Bettman, E. Coupey and E.J. Johnson, 1992. A constructive process view of decision making: multiple strategies in judgment and choice. Acta Psychologica 80: 107141.
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Figure 1 An example of the scheduling process underlying observed activity-travel pattern outcomes, noting the different types of decisions made as scheduling proceeds from preplanning stages to execution. a) Pre-planned “Routine” Activity Decisions (i.e. routine sleep, breakfast, work and drop-off of children at school) Work
Work Shop
S P A C E
Cinema Child’s School Home
Drop-off Sleep
Sleep Eat
4
8
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TIME b) Continued decision making close to or during the same day of execution (i.e. pre-planning of meal preparation, eating at home, and visit to the cinema, plus modification in sleep) Work
Work Shop
S P A C E
Movie
Cinema Child’s School Home
Drop-off Sleep
Sleep Eat
4
8
Eat
12
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TIME c) Impulsive decisions made immediately before or during execution of activities, resulting in the final observed schedule (i.e. impulsive shopping addition, changes in meal preparation time, and movie duration) Work
Work
Grocery
Shop
S P A C E
Movie
Cinema Child’s School Home
Drop-off Sleep
Meal Prep Eat
Eat
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8
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TIME
16
Sleep
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Fri Thr Wed Tue Mon
Activity Day and Time
Sat
Sun
Figure 2 The scheduling process depicted sequentially over time: Example 1 - Straightforward scheduling behavior
1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000
Add Modify Delete Impulsive
1st 0 0Sun 0 0 1 1 Mon 1 1 1 2 2 Tue 2 2 2 3 Wed 3 3 3 4 Thr 4 4 5 5 Fri 5 5 6 6 6Sat 6 6 6 Sun 7 7
Scheduling Decisions in Sequence
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Figure 3 The scheduling process depicted sequentially over time: Example 2 - Semi-structured, but straight-forward scheduling behavior
Fri Thr Wed Tue Mon
Activity Day and Time
Sat
Sun
8 1800 1200 0600 0000 7 1800 1200 0600 0000 6 1800 1200 0600 0000 5 1800 1200 0600 0000 4 1800 1200 0600 0000 3 1800 1200 0600 0000 2 1800 1200 0600 0000 1
Add Modify Delete Impulsive
0 0 0 0 0 0 01st 0 0Sun 0 0 0 0 0 0 1 1 1Mon 1 1 1 1 2 Tue 2 2 2Wed 3 3 4 4Thr 4 4 5 5 5Fri 5 5 5 5 6 6 Sat 6 6 6 6 7Sun 77 7
Scheduling Decisions in Sequence
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Fri Thr Wed Tue Mon
Activity Day and Time
Sat
Sun
Figure 4 The scheduling process depicted sequentially over time: Example 3 - Highly structured, unordered and opportunistic scheduling
1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000 1800 1200 0600 0000
Add Modify Delete Impulsive 0 0 0 0 1st 0 0 Sun 0 0 0 0 0 1 1 1 1 1Mon 1 1 1 1 1 1 Tue 2 2 2 3 3Wed 3 3 3 Thr 4 4 5 5Fri 5 5 6 Sat 6 6 6 7Sun 7 7
Scheduling Decisions in Sequence
