Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models

The use of GPS devices and smartphones has made feasible the collection of multi-day activity-travel diaries. In turn, the availability of multi-day travel diary data opens up new avenues for analyzing dynamics of individual travel behavior. This paper addresses the issue of day-to-day variability in activity-travel behavior. The study, which is the first of its kind in China, applies a unique combination of methods to analyze the degree of dissimilarity between travel days using multi-day GPS data. First, multi-dimensional sequence alignment is applied to measure the degree of dissimilarity in individual daily activity-travel sequences between pairs of travel days. Next, a series of panel effects regression models is used to estimate the effects of socio-demographics and days of the week. The models are estimated using multi-day activity-travel patterns imputed from GPS-enabled smartphone data collected in Shanghai, China. Results indicate that (1) days of the week have significant effects on day-to-day variability in activity-travel behavior with weekday activity-travel sequences being more similar and thereby different from weekend sequences; (2) the degree of dissimilarity in activity-travel sequences is strongly influenced by respondent socio-demographic profiles; (3) individuals having more control over and flexibility in their work schedule show greater intra-personal variability. Day-to-day variability in activity-travel behavior of this sample is similar to patterns observed in developed countries in some aspects but different in others. Strict international comparison study based on comparative data collection is required to further distinguish the sources of travel behavior differences between developing countries and developed countries. The paper ends with a discussion of the limitations of this study and the implications of the research findings for future research.     

Real-time tracking of activity scheduling/schedule execution within a unified data collection framework

One of the major foci in transport research is the identification of the temporal–spatial decision making structure embedded in activity scheduling and its linkage to actual activity execution. The latter part of the research in question has not been explored explicitly in real life situations due to the lack of effective data collection means. This paper presents a real-time activity scheduling, activity/travel survey system that incorporates the extraction of activity scheduling and the execution information within one unified data collection framework. These “revealed” data can be used for explicitly defining the mechanism of how people’s activity schedules dynamically adapt to social-demographic and temporal–spatial constraints and finally lead to the observed activity-travel patterns.     

Micro-simulation of daily activity-travel patterns for travel demand forecasting

The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns. This revised version was published online in June 2006 with corrections to the Cover Date.     

Effects of land-use transport scenarios on travel patterns: a multi-state supernetwork application

Understanding travellers’ response is essential to address policy questions arising from spatial and transport planning sectors. This paper demonstrates the usefulness of the multi-state supernetwork approach to investigate the effects of land-use transport scenarios on individuals’ travel patterns. In particular, it illustrates that multi-state supernetworks are capable of representing activity-travel patterns at a high level of detail, including the choice of mode, route, parking and activity location. Multi-faceted activity-travel preferences can be accommodated in supernetworks. Using a micro-simulation approach, the adaptation of individuals’ travel patterns to policies can be readily captured. The illustration concerns hypothetical land-use and transport scenarios for the city of Rotterdam (The Netherlands), focusing on accessibility changes, modal substitution and shift in the use of transport and location facilities.     

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Studies of urban travel behaviour typically focus on weekday activities and commuting. This is surprising given the rising contribution of discretionary activities to daily travel that has occurred during the last few decades. Moreover, current understanding of the relationship between travel behaviour and land use remains incomplete, with little research carried out to explore spatial properties of activity-travel behaviour during the off-peak and weekend time periods. Weekend behaviours, for example, influenced by the availability of time and the spatiotemporal distribution of “weekend” destinations, likely produce spatially and temporally distinct activity-travel patterns. Using data from the first wave of the Toronto Travel-Activity Panel Survey (TTAPS), this paper examines an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. The paper begins by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. Area-based measures of geographical extent and activity dispersion are then used to expose differences in weekday-to-weekend and day-to-day activity-travel patterns. Examination of unclassified activities carried out over a 1 week period reveals a level of spatial repetition that does not materialise across activities classified by type, travel mode, and planning strategy. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition is also found to be surprisingly similar across travel modes. The results also indicate weekday-to-weekend, and day-to-day fluctuations in spatial properties of individual activity-travel behaviour. These findings challenge the utility of the short-run survey as an instrument for capturing archetypal patterns of spatial behaviour. In addition, the presence of a weekday-to-weekend differential in spatial behaviour suggests that policies targeting weekday travel reduction could have little impact on travel associated with weekend activities.

Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area

Using four consecutive days of SITRAMP 2004 data from the Jakarta metropolitan area (JMA), Indonesia, this study examines the interactions between individuals’ activity-travel parameters, given the variability in their daily constraints, resources, land use and road network conditions. While there have been a significant number of studies into day-to-day variability in travel behaviour in developed countries, this issue is rarely examined in developing countries. The results show that some activity-travel parameter interactions are similar to those produced by travellers from developed countries, while others differ. Household and individual characteristics are the most significant variables influencing the interactions between activity-travel parameters. Different groups of travellers exhibit different trade-off mechanisms. Further analyses of the stability of activity-travel patterns across different days are also provided. Daily commuting time and regular work and study commitments heavily shape workers’ and students’ flexibility in arranging their travel time and out-of-home time budget, leading to more stable daily activity-travel patterns than non-workers.     

A computerized household activity scheduling survey

Household activity scheduling is widely regarded as the underlying mechanism through which people respond to emerging travel demand management policies. Despite this, very little fundamental research has been conducted into the underlying scheduling process to improve our understanding and ability forecast travel. The experimental survey approach presented in this paper attempts to fill this gap. At the core of the survey is a Computerized Household Activity Scheduling (CHASE) software program. The program is unique in that it runs for a week long period during which time all adult household members login daily to record their scheduling decisions as they occur over time. An up-front interview is used to define a household's activity agenda and mode availability. A sample of 41 households (66 adults and 14 children) was used to assess the performance of the survey. Analysis focuses on times to completion, daily scheduling steps, activity-travel patterns, and scheduling time horizons. Overall, the results show that the computer-based survey design was successful in gathering an array of information on the underlying process, while minimizing the burden on respondents. The survey was also capable of tracing traditionally observed activity-travel outcomes over a multi-day period with minimal fatigue effects. The paper concludes with a detailed discussion on future survey design, including issues of instrument bias, use of the Internet, and improved tracing of spatial behaviour. Future use of the survey methodology to enhance activity-travel diary surveys and stated responses experiments is also discussed. This revised version was published online in June 2006 with corrections to the Cover Date.     

Collecting a multi-dimensional three-weeks household time-use and activity diary in the Bandung Metropolitan Area,Indonesia

This paper describes a comprehensive panel data collection and analysis at household level, including detailed travel behaviour variables and comprehensive in-home and out-of-home activities, individual cognitive habits and affective behaviours, the rate of physical activity, as well as health related quality of life (QoL) information in the Bandung Metropolitan Area (BMA) of Indonesia. To our knowledge, this is the first attempt to collect an individual’s activity diary over an extended period as it captures the multi-tasking activities and multidisciplinary factors that underlie individual activity-travel patterns in a developing country. Preliminary analyses of the collected data indicate that different beliefs, anticipated emotions, support and attachment to motorised modes significantly correlate with different groups of occupation, gender, age, activity participation, multi-tasking activities, and physical health, but not with different social and mental health. This finding highlights the reason why implementing car reduction policies in Indonesia, without breaking or changing the individual’s habits and influencing his/her attitudes have not been fruitful. The results also show that endorsing more physical activities may result in a significant reduction in the individual’s motorised mode use, whilst individuals who demonstrate a tendency to use their spare time on social activities tend to have better social health conditions. Furthermore, undertaking multi-tasking out-of-home discretionary activities positively correlates with better physical health. All these highlight the importance of properly understanding and analysing the complex mechanisms that underlie these fundamental factors that shape individual daily activity-travel patterns in developing countries. This type of multidisciplinary approach is needed to design better transport policies that will not only promote better transport conditions, but also a healthier society with a better quality of life.     

Lifestyle classifications with and without activity-travel patterns

Trip-based approach and activity-based approach are two extremes in the use of activity related information when developing travel demand models. Creating lifestyle clusters for a population is a compromise between the two. On the one hand, it has taken into account travel-activity patterns in the development of the clusters. On the other hand, the clusters represent homogenous groups of individuals and simple activity-based travel demand models can be developed for each cluster. However, the development of such clusters requires knowledge of activity-travel patterns of individuals, which can only be obtained from a large-scale survey. It is still an open question how to create travel/activity-related lifestyle clusters using readily available socio-demographic data (such as census data) alone. This paper attempts to answer this question by proposing a procedure of lifestyle classification that moves from specific surveys to a general population. This paper first studies issues related to the development of homogeneous clusters using socio-economic, demographic and activity-travel data. The second part of the paper addresses the issue of data insufficiency and points out that in order to use the clusters developed for travel demand estimation, it is important to know how to allocate individuals in the population to the developed clusters. As a first attempt, this paper proposes to use a recently developed technique called, Support Vector Machine (SVM), to develop classification functions that based on readily available information only. The methodologies proposed are applied to a sub-urban area in Hong Kong. Six lifestyle clusters are first produced using factor analysis and cluster analysis. SVM is then used to develop classification functions that are based on fewer variables. Results show that the two sets of lifestyle clusters are similar and that the SVM outperforms other traditional classification methods.     

Impact of telecommuting on spatial and temporal patterns of household travel

A spatial and temporal analysis of travel diary data collected during the State of California Telecommuting Pilot Project is performed to determine the impacts of telecommuting on household travel behavior. The analysis is based on geocoded trip data where missing trips and trip attributes have been augmented to the extent possible. The results confirm the earlier finding that the Pilot Project telecommuters substantially reduced travel; on telecommuting days, the telecommuters made virtually no commute trips, reduced peak-period trips by 60%, total distance traveled by 75%, and freeway miles by 90%. The spatial analysis of the trip records has shown that the telecommuters chose non-work destinations that are closer to home; they exhibited contracted action spaces after the introduction of telecommuting. Importantly, this contraction took place on both telecommuting days and commuting days. The telecommuters distributed their trips, over the day and avoided peak-period travel on telecommuting days. Non-work trips, however, show similar patterns of temporal distribution on telecommuting days and commuting days. Non-work trips continued to be made during the lunch period and late afternoon and evening hours.     

Modeling household activity–travel interactions as parallel constrained choices

The daily activity-travel patterns of individuals often include interactions with other household members, which we observe in the form of joint activity participation and shared rides. Explicit representation of joint activity patterns is a widespread deficiency in extant travel forecasting models and remains a relatively under-developed area of travel behavior research. In this paper, we identify several spatially defined tour patterns found in weekday household survey data that describe this form of interpersonal decision-making. Using pairs of household decision makers as our subjects, we develop a structural discrete choice model that predicts the separate, parallel choices of full-day tour patterns by both persons, subject to the higher level constraint imposed by their joint selection of one of several spatial interaction patterns, one of which may be no interaction. We apply this model to the household survey data, drawing inferences from the household and person attributes that prove to be significant predictors of pattern choices, such as commitment to work schedules, auto availability, commuting distance and the presence of children in the household. Parameterization of an importance function in the models shows that in making joint activity-travel decisions significantly greater emphasis is placed on the individual utilities of workers relative to non-workers and on the utilities of women in households with very young children. The model and methods are prototypes for tour-based travel forecasting systems that seek to represent the complex interaction between household members in an integrated model structure.     

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation during the post-school period have direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel pattern characteristics impact children’s after school activity engagement patterns.     

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.     

On the formulation of time-space prisms to model constraints on personal activity-travel engagement

The notion of time-space prisms has often been used in the context of describing activity-travel patterns of individuals. This paper presents a methodology for estimating the temporal vertices of time-space prisms using the stochastic frontier modeling technique. Observed trip starting and ending times are used as dependent variables and socio-economic characteristics and commute characteristics serve as independent variables. The models are found to offer plausible results indicating that temporal vertices of time-space prisms, though unobservable, can be estimated based on temporal characteristics of observed activity-travel patterns. Comparisons of stochastic frontier models of prism vertices and the distributions of prism vertices are presented using two activity data sets collected in the United States – San Francisco and Miami. Differences and similarities in temporal vertex locations are highlighted in the paper.     

Multi-day activity-travel pattern sampling based on single-day data

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.     

An integrated framework to jointly model decisions of activity time allocation and work-related travel

Five activity-travel choice dimensions, including three activity time allocation decisions and two work-related travel choices, are jointly modeled using the structural equation model in order to accommodate the complex interactions among them. Via a two-step estimation approach, the behavioral pattern underlying activity-travel decisions is explicitly revealed. For example, it demonstrates the priority with respect to subsistence activity, maintenance activity, and recreation activity due to a limited time budget; and bus commuting behavior positively influences the time allocated to the maintenance activity. In addition, two attitudinal factors are constructed and confirmed to have important effects on the five behavioral dimensions, which contribute to reveal the decision-making process from the perspective of psychology. This comprehensive framework is expected to provide important implications for mobility management and urban planning.     

A dynamic analysis of person and household activity and travel patterns using data from the first two waves in the Puget Sound Transportation Panel

Daily activity participation and travel patterns are examined using data from the Puget Sound Transportation Panel (PSTP), which contains two-pairs of daily travel diary information (wave 1 in 1989 and wave 2 in 1990). Summary data of the travel diaries at the person and household levels are obtained using cluster analysis. At the person level, four clusters are found reasonable for both activity and travel. The four-cluster solutions indicate substantial day-to-day variation in activity participation and similarity in travel behavior over time. Temporal changes are analyzed using contingency table methods and log-linear models. The analysis reveals that activity participation and mobility present many regularities over time. Transitions within each wave show strong dependence between two days for both activity and travel, with higher dependency for the travel patterns than for the activity patterns. These are consistent when the analysis focuses on a longer period such as a year. Temporal dependencies appear to be stronger in the household-based than the person-based analysis. A hierarchical structure is also found in the relationship between activity and travel clusters. The link between activity and travel is much stronger within a day, weaker from one day to another, and the least strong from one year to the next. Important irregularities, however, are found and may be due to scheduling time-frames adopted by the respondents that are only partially captured in the data used.     

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.     

Regimes in social–cultural events-driven activity sequences: Modelling approach and empirical application

In this study we propose and apply a Bayesian-network model to predict and analyse the factors that influence activity-travel sequences that are triggered by social–cultural events. The study is motivated by the intention to examine the wider context in which activity-travel decisions are made and to model such decisions under longitudinal time horizons. We assume that social events trigger a series of interrelated activities and corresponding trips. Data about events and related activities are collected using a month-diary and involving a large sample of households in the Eindhoven region, The Netherlands. A learning algorithm is applied to derive a Bayesian-network model from the event diary. The results show that indeed many travel choices are influenced by particular events, that these influences vary by socio-demographic variables and that the learned Bayesian-network model is able to represent these interdependencies among all these variables. We demonstrate how the model can be used to predict event-driven activity-travel sequences in a micro-simulation.