A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

An activity-based approach of investigating travel behaviour of older people

The paper presents the results of an investigation on daily activity-travel scheduling behaviour of older people by using an advanced econometric model and a household travel survey, collected in the National Capital Region (NCR) of Canada in 2011. The activity-travel scheduling model considers a dynamic time–space constrained scheduling process. The key contribution of the paper is to reveal daily activity-travel scheduling behaviour through a comprehensive econometric framework. The resulting empirical model reveals many behavioural details. These include the role that income plays in moderating out-of-home time expenditure choices of older people. Older people in the highest and lowest income categories tend to have lower variations in time expenditure choices than those in middle-income categories. Overall, the time expenditure choices become more stable with increasing age, indicating that longer activity durations and lower activity frequency become more prevalent with increasing age. Daily activity type and location choices reveal a clear random utility-maximizing rational behaviour of older people. It is clear that increasing spatial accessibility to various activity locations is a crucial factor in defining daily out-of-home activity participation of older people. It is also clear that the diversity of out-of-home activity type choices reduces with increasing age and older people are more sensitive to auto travel time than to transit or non-motorized travel time.     

Agent-based model for continuous activity planning with an open planning horizon

The paper proposes the microscopic travel demand model continuous target-based activity planning (C-TAP) that generates multi-week schedules by means of a continuous planning approach with an open planning horizon. C-TAP introduces behavioral targets to describe people’s motivation to perform activities, and it uses a planning heuristic to make on-the-fly decisions about upcoming activities. The planning heuristic bases its decisions on three aspects: a discomfort index derived from deviations from agents’ past performance with regard to their behavioral targets; the effectiveness of the immediate execution; and activity execution options available in the near future. The paper reports the results of a test scenario based on an existing 6-week continuous travel diary and validates C-TAP by comparing simulation results with observed behavioral patterns along several dimensions (weekday similarities, weekday execution probabilities of activities, transition probabilities between activities, duration distributions of activities, frequency distributions of activities, execution interval distributions of activities and weekly travel probability distributions). The results show that C-TAP has the capability to reproduce observed behavior and the flexibility to introduces new behavioral patterns.     

A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty

An understanding of the interaction between individuals’ activities and travel choice behaviour plays an important role in long-term transit service planning. In this paper, an activity-based network equilibrium model for scheduling daily activity-travel patterns (DATPs) in multi-modal transit networks under uncertainty is presented. In the proposed model, the DATP choice problem is transformed into a static traffic assignment problem by constructing a new super-network platform. With the use of the new super-network platform, individuals’ activity and travel choices such as time and space coordination, activity location, activity sequence and duration, and route/mode choices, can be simultaneously considered. In order to capture the stochastic characteristics of different activities, activity utilities are assumed in this study to be time-dependent and stochastic in relation to the activity types. A concept of DATP budget utility is proposed for modelling the uncertainty of activity utility. An efficient solution algorithm without prior enumeration of DATPs is developed for solving the DATP scheduling problem in multi-modal transit networks. Numerical examples are used to illustrate the application of the proposed model and the solution algorithm.     

Implications of congestion charging for departure time choice: Work and non-work schedule flexibility

The objective of this paper is to investigate the potential impacts of implementing variable congestion charging on the peak spreading of departure time choices, taking into account levels of scheduling flexibility of individuals. In particular, this study addresses non-work activities as well as socio-economic characteristics and their influence on scheduling flexibility for work trips. Departure time choice models were calibrated using data collected as part of a larger survey on the consequences of congestion charging on travel choices in the city of Edinburgh. The inclusion of variables related to work and non-work scheduling, as well as socio-economic variables have improved the performance of the models. This suggests that non-work activities, as well as work schedule flexibility have an impact on departure time choice for the journey to work. This means that even for those with flexible work schedules, but with other non-work commitments, the timing of their work trip may not be so flexible. Therefore, for the success of variable congestion charging schemes, other complimentary measures should be introduced in parallel. These include, for example, child care provision at work, opening hours of shops and leisure facilities.     

Traveler delay costs and value of time with trip chains, flexible activity scheduling and information

The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper we extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. We analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. We show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. We investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.     

The validity of using activity type to structure tour-based scheduling models

A unique set of activity scheduling data is utilized in this paper to provide much needed empirical analysis of the sequence in which activities are planned in everyday life. This is used to assess the validity of the assumption that activities are planned in accordance to a fixed hierarchy of activity types: mandatory activities first (work/school), followed by joint maintenance, joint discretionary, allocated maintenance, and individual discretionary activities. Such an assumption is typical of current generation activity and tour-based travel demand models. However, the empirical results clearly do not support such assumptions. For instance, fewer than 50% of mandatory activities were actually planned first in related out-of-home tours; remaining activity types also did not take any particular precedence in the planning sequence. Given this, a search was made for the more salient attributes of activities (beyond activity type) that would better predict how they are planned within tours. Several ordered response choice models for different tour sizes were developed for this purpose, predicting the choice order of the 1st, 2nd, 3rd, etc. planned activity in the tour as a function of activity type, activity characteristics (duration, frequency, travel time, and involved persons), and individual characteristics. Activity duration played the most significant role in the models compared to any other single variable, wherein longer duration activities tended to be planned much earlier in tours. This strongly suggests that the amount of time-use, rather than the nature of the event as indicated by activity type, is a primary driver of within-tour planning order and offers potential for a much improved and valid fit.     

Modeling the choice continuum: an integrated model of residential location,auto ownership,bicycle ownership,and commute tour mode choice decisions

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.     

A coupled multi-agent microsimulation of social interactions and transportation behavior

Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior.     

An activity-based approach for scheduling multimodal transit services

This paper proposes a new activity-based transit assignment model for investigating the scheduling (or timetabling) problem of transit services in multi-modal transit networks. The proposed model can be used to generate the short-term and long-term timetables of multimodal transit lines for transit operations and service planning purposes. The interaction between transit timetables and passenger activity-travel scheduling behaviors is captured by the proposed model, as the activity and travel choices of transit passengers are considered explicitly in terms of departure time choice, activity/trip chain choices, activity duration choice, transit line and mode choices. A heuristic solution algorithm which combines the Hooke–Jeeves method and an iterative supply–demand equilibrium approach is developed to solve the proposed model. Two numerical examples are presented to illustrate the differences between the activity-based approach and the traditional trip-based method, together with comparison on the effects of optimal timetables with even and uneven headways. It is shown that the passenger travel scheduling pattern derived from the activity-based approach is significantly different from that obtained by the trip-based method, and that a demand-sensitive (with uneven headway) timetable is more efficient than an even-headway timetable.     

Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

A model of complex travel behavior: Part I—Theoretical development

This paper presents a policy sensitive approach to modeling travel behavior based on activity pattern analysis. A theoretical model of complex travel behavior is formulated on a recognition of a wide range of interdependencies associated with an individual's travel decisions in a constrained environment. Travel is viewed as input to a more basic process involving activity decisions. A fundamental tenet of this approach is that travel decisions are driven by the collection of activities that form an agenda for participation; the utility of any specific travel decision can be determined only within the context of the entire agenda. Based on the theoretical model of complex travel behavior, an operational system of models, STARCHILD (Simulation of Travel/Activity Responses to Complex Household Interactive Logistic Decisions), has been developed to examine the formation of household travel/activity patterns, and is presented in a companion paper (Recker et al., 1986).     

The cost of travel time variability: Three measures with properties

This paper explores the relationships between three types of measures of the cost of travel time variability: measures based on scheduling preferences and implicit departure time choice, Bernoulli type measures based on a univariate function of travel time, and mean-dispersion measures. We characterise measures that are both scheduling measures and mean-dispersion measures and measures that are both Bernoulli and mean-dispersion. There are no measures that are both scheduling and Bernoulli. We consider the impact of requiring that measures are additive or homogeneous, proving also a new strong result on the utility rates in an additive scheduling measure. These insights are useful for selecting cost measures to use in applications.     

On the structure of weekly activity/travel patterns

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term “structure” refers to the sequence by which various activities enter one’s daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey

According to US Census Bureau, the number of individuals in the age group above 65 years is expected to increase by more than 100% from the year 2000 to 2030. It is anticipated that increasing elderly population will put unforeseen demands on the transportation infrastructure due to the atypical mobility and travel needs of the elderly. Consequently, transportation professionals have attempted to understand the travel behavior of the elderly including the trip frequency, trip distance and mode choice decisions. Majority of the research on elderly travel behavior have focused on the mobility outcomes with limited research into understanding the tradeoffs made by this population segment in terms of their in-home and out-of-home activity engagement choices. The goal of the current research is to contribute to this line of inquiry by simultaneously exploring the daily activity engagement choices of the elderly Americans including their in-home and out-of-home activity participation (what activities to pursue) and time alloocation (duration of each activity) decisions while accounting for the temporal constraints. Further, the study attempts to explore the relationship between physical and subjective well-being and daily activity engagement decisions of the elderly; where subjective well-being is derived from reported needs satisfaction with life and different domains of it. To this end, data from the Disabilities and Use of Time survey of Panel Study of Income Dynamics was used to estimate a panel version of MDCEV model. In addition to person- and household-level demographic variables, activity participation and time use choices of elderly were found to vary across different levels of reported physical and subjective well-being measures. The model estimation results were plausible and provide interesting insights into the activity engagement choices of the elderly with implications for transportation policy development. Among other socio-demographic variables, living arrangements (living with family versus in elderly homes) were found to have significant influence on how people participate into different in-home versus out-of-home activities. For example, elderly living in the elderly home were found to participate more into out-of-home activities compared to people living with families. Elderly with disabilities were found to compensate lower participation into out-of-home activities with more participation into in-home activities. Considerable heterogeneity was observed in time engagement behavior of the elderly across reported levels of satisfaction with finance, job and cognitive needs. For example, elderly expressing high satisfaction with job was found to spend less time in in-home social activities. Elderly reporting higher satisfaction with finance were found to spend more time into OH social and shopping activities.     

A conceptual framework of individual activity program generation

The research in this paper attempts to better understand the process by which activities are generated at an individual level. Activity-based travel analyses have gained popularity in recent years because they recognize the complexity of activity behavior and view travel as a derivative of this behavior. Most activity-based studies have focused on the spatial and temporal linkage of trips; that is, the scheduling of activities. They consider the agenda of activities for participation, and associated attributes of the activity participation (such as mode to activity and location of activity performance), as predetermined. This paper develops a comprehensive conceptual framework of the relatively unexplored area of activity agenda generation. Such a framework will be valuable in empirical modeling of activity generation behavior. A subsequent paper focuses on translating a part of this conceptual framework into an empirical model.     

How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips

In departure time studies it is crucial to ascertain whether or not individuals are flexible in their choices. Previous studies have found that individuals with flexible work times have a lower value of time for late arrivals. Flexibility is usually measured in terms of flexible work start time or in terms of constraints in arrival time at work. Although used for the same purpose, these two questions can convey different types of information. Moreover, constraints in departure time are often related not only to the main work activity, but to all daily activities. The objective of this paper is to investigate the effect of constraints in work and in other daily trips/activities on the willingness to shift departure time and the willingness to pay for reducing travel time and travel delay. We set up a survey to collect detailed data on the full 24-hour out-of-home activities and on the constraints for each of these activities. We then built a stated preference experiment to infer preferences on departure time choice, and estimated a mixed logit model, based on the scheduling model, to account for the effects of daily activity schedules and their constraints. Our results show that measuring flexibility in terms of work start time or constraints at work does not provide exactly the same information. Since one-third of the workers with flexible working hours in the survey indicated that they have restrictions on late work-arrival times, their willingness to pay will be overestimated (almost doubled) if flexibility information is asked only in terms of fixed/flexible working hours. This clearly leads to different conclusion in terms of demand sensitivity to reschedule to a later departure time. We also found that having other activities and constraints during the day increases the individuals’ willingness to pay to avoid being late at work, where the presence of constraints on daily activities other than work is particularly relevant for individuals with no constraints at work. The important impact of these findings is that if we neglect the presence of constraints, as is common practise in transport models, it will generally lead to biased value-of-time estimates. Results clearly show that the shift in the departure time, especially towards a late departure time, is strongly overestimated (the predicted shift is more than double) when the effect of non-work activities and their constraints is not accounted for.     

Modeling commuting mode choice jointly with work start time and work duration

This paper presents a joint trivariate discrete-continuous-continuous model for commuters’ mode choice, work start time and work duration. The model is designed to capture correlations among random components influencing these decisions. For empirical investigation, the model is estimated using a data set collected in the Greater Toronto Area (GTA) in 2001. Considering the fact that work duration involves medium- to long-term decision making compared to short-term activity scheduling decisions, work duration is considered endogenous to work start time decisions. The empirical model reveals many behavioral details of commuters’ mode choice, work start time and duration decisions. The primary objective of the model is to predict workers’ work schedules according to mode choice, which is considered a skeletal activity schedule in activity-based travel demand models. However, the empirical model reveals many behavioral details of workers’ mode choices and work scheduling. Independent application of the model for travel demand management policy evaluations is also promising, as it provides better value in terms of travel time estimates.     

Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling

Activity-travel scheduling is at the core of many activity-based models that predict short-term effects of travel information systems and travel demand management. Multi-state supernetworks have been advanced to represent in an integral fashion the multi-dimensional nature of activity-travel scheduling processes. To date, however, the treatment of time in the supernetworks has been rather limited. This paper attempts to (i) dramatically improve the temporal dimension in multi-state supernetworks by embedding space–time constraints into location selection models, not only operating between consecutive pairs of locations, but also at the overall schedule at large, and (ii) systematically incorporate time in the disutility profiles of activity participation and parking. These two improvements make the multi-state supernetworks fully time-dependent, allowing modeling choice of mode, route, parking and activity locations in a unified and time-dependent manner and more accurately capturing interdependences of the activity-travel trip chaining. To account for this generalized representation, refined behavioral assumptions and dominance relationships are proposed based on an earlier proposed bicriteria label-correcting algorithm to find the optimal activity-travel pattern. Examples are shown to demonstrate the feasibility of this new approach and its potential applicability to large scale agent-based simulation systems.     

Selfishness and altruism in the distribution of travel time and income

Most economic models assume that individuals act out their preferences based on self-interest alone. However, there have also been other paradigms in economics that aim to capture aspects of behavior that include fairness, reciprocity, and altruism. In this study we empirically examine preferences of travel time and income distributions with and without the respondent knowing their own position in each distribution. The data comes from a Stated Preference experiment where subjects were presented paired alternative distributions of travel time and income. The alternatives require a tradeoff between distributional concerns and the respondent’s own position. Choices also do not penalize or reward any particular choice. Overall, choices show individuals are willing forgo alternatives where they would be individually well off in the interest of distributional concerns in both the travel time and income cases. Exclusively self-interested choices are seen more in the income questions, where nearly 25 % of respondents express such preferences, than in the travel time case, where only 5 % of respondents make such choices. The results also suggest that respondents prioritize their own position differently relative to regional distributions of travel time and income. Estimated choice models show that when it comes to travel time, individuals are more concerned with societal average travel time followed by the standard deviation in the region and finally their own travel time, while in the case of income they are more concerned with their own income, followed by a desire for more variability, and finally increasing the minimum income in their region. When individuals do not know their fate after a policy change that affects regional travel time, their choices appear to be mainly motivated by risk averse behavior and aim to reduce variability in outcomes. On the other hand, in the income context, the expected value appears to drive choices. In all cases, population-wide tastes are also estimated and reported.