Time use,mobility and expenditure: an innovative survey design for understanding individuals’ trade-off processes

A large amount of information is required to model the complex trade-off processes between travel activities, non-travel activities and budget assignment at the individual level. This paper describes the development of a new survey design, which incorporates components of travel surveys, time use surveys and consumer expenditure surveys in an integrated format, which is expected to deliver a richer data set allowing deeper insights into individuals’ activity and consumption patterns. The survey procedure and the incentives paid, which were necessary to obtain acceptable response rates, are also described. Results from two pilot studies using a trip-based and an activity-based diary format are presented. The paper examines to which extent the diaries have been capable of collecting the required data with high quality and response rates. The innovative “Mobility–Activity–Expenditure-Diary” is introduced and results of the main survey using this design are presented. Travel behaviour and non-travel activities were reported at high quality. Expenditures would require longer observation periods (and preferably not only telephone but also personal support in the survey process) to reduce unsystematic variations and to better capture individuals’ long term equilibrium.

     

Can using global positioning system (GPS) improve trip reporting?

In the United States, information about daily travel patterns is generally captured using self-reported information using a written diary and telephone retrieval (or mail-back of diary forms). Problems with these methods include lack of reporting for short trips, poor data quality on travel start and end times, total trip times and destination locations.This project combined a hand-held computer (Personal Digital Assistant or PDA) with a Global Positioning System (GPS) receiver to capture vehicle-based, daily travel information. The vehicle driver uses a menu to enter variables such as trip purpose and vehicle occupancy, but other data such as date, start time, end time, and vehicle position (latitude and longitude) are collected automatically at frequent intervals. The field test was conducted in Lexington, Kentucky in fall, 1996, with 100 households to use the equipment for six days. Respondents also completed a telephone survey for one day of travel (attempted for day 5).The field test was a test of equipment and willingness of the general public to participate, rather than to obtain a statistically valid travel behavior dataset for the Lexington area. One improvement to the hardware would be for the equipment to turn on automatically. There are limitations to the dataset and analyses that are discussed where appropriate. Although the dataset is small, this paper compares the results of the machine-recorded trips to self-reported trips captured by telephone interview.Self-reported distances are much longer than distances recorded by the PDA/GPS. A recalled distance of 10 miles was, on average, only 6.5 miles when the GPS points are matched to a positionally accurate base file. Similarly, recalled times generally exceed median measured values, but the differences are much smaller than for distances. Respondents reported that data entry of 1 min at the beginning of each trip over the six-day survey period was not burdensome.Recommendations for improving the hardware and software for conducting other travel surveys using GPS, and improving the utility of travel data collected using GPS are provided. One of the benefits of incorporating a GPS device into the survey process was the ability to collect information on route choice and travel speed. However, this paper does not address these topics.     

Error and uncertainty in travel surveys

Transportation study home interview surveys normally underestimate the amount of trip-making by residents of a study area, and the data has to be adjusted subsequently to compensate for this effect. One important reason for the shortfall appears to be the incomplete recall and reporting of travel information by respondents. The paper examines the influence of the survey instrument on recall by comparing reported travel behaviour in two surveys conducted in the same town, one using a conventional travel diary and the other an activity diary. The latter format appears to encourage a more complete response, giving rise to significantly higher reported trip rates and travel times. The paper discusses some research and policy implications of this finding and concludes with tentative suggestions for improved travel survey instruments.     

Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands

In the past few decades, travel patterns have become more complex and policy makers demand more detailed information. As a result, conventional data collection methods seem no longer adequate to satisfy all data needs. Travel researchers around the world are currently experimenting with different Global Positioning System (GPS)-based data collection methods. An overview of the literature shows the potential of these methods, especially when algorithms that include spatial data are used to derive trip characteristics from the GPS logs. This article presents an innovative method that combines GPS logs, Geographic Information System (GIS) technology and an interactive web-based validation application. In particular, this approach concentrates on the issue of deriving and validating trip purposes and travel modes, as well as allowing for reliable multi-day data collection. In 2007, this method was used in practice in a large-scale study conducted in the Netherlands. In total, 1104 respondents successfully participated in the one-week survey. The project demonstrated that GPS-based methods now provide reliable multi-day data. In comparison with data from the Dutch Travel Survey, travel mode and trip purpose shares were almost equal while more trips per tour were recorded, which indicates the ability of collecting trips that are missed by paper diary methods.     

A multi-instrumented approach to observing the activity rescheduling decision process

Activity scheduling simulation models represent an emerging and proposing approach to forecasting travel demand. The most significant developmental challenge is the lack of empirical data on how people actually proceed through the scheduling and conflict resolution process. This paper develops a new methodology to collect data about the rescheduling decision process. The data collection involves six stages: preplanned schedule interview, coding of the preplanned schedule, second-by-second Global Positioning System tracking, internet-based prompted recall diary, detection of rescheduling decisions (via comparison of planned versus executed activities), and a final in-depth interview probing the how and why of rescheduling decisions. Each stage of the methodology is described in detail with example results drawn from a pilot study. Key discoveries include: elicitation of multiple preplanned schedule reporting methods (verbal, point-form, calendar); discovery that activity attributes (time, location, involved persons) are planned on significantly different time horizons and include partial elaboration; and provision of new insights into how and why rescheduling decisions are made. A method for automatically tracking rescheduling decisions was also discovered. Overall, the new methodology has potential to contribute to the development of more realistic models of the entire scheduling process, especially rescheduling and conflict resolution sub-models.     

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.     

Use of an activity diary survey to examine travel and activity reporting in a home interview survey: An example using data from Adelaide,Australia

Research presented in this paper uses results from an acitivity diary survey to comment on the quality of data collected in a home interview travel survey. The two surveys were conducted in Adelaide, Australia. Evidence is presented to suggest that although a period of slightly more than three years separated the two surveys, the samples were reasonably similar with respect to their socio-demographic composition and real mobility levels. There was, however, a much higher level of travel and out-of-home activity reporting in the activity diary when compared to the home interview survey. Differences in reporting rates are examined in detail and areas of deficiency with the home interview survey are identified. The paper concludes with a short discussion of the possible implications of these deficiencies when home interview survey data is used to investigate a range of urban transport issues.     

The design of environmentally aware travel diaries

Any policy addressing the concerns and trends associated with the impact of travel on the environment should be based on a solid understanding of the activities giving rise to them. While the measurement of the total environment loads by air or noise measurement stations is essential, it needs to be matched by the observation of the human behaviours creating them. This is especially true in the transport sector, which has been rightly or wrongly identified as having the potential to make a substantial contribution to the reduction of air and noise pollution. While the contribution of freight transport is of growing concern and importance, this paper focuses on the measurement of passenger transport throughout.

In the past transport planners have largely relied on the travel diary as their prime instrument to measure traveller behaviour. The travel diary is a survey instrument designed to record all movements during the course of one or more days including their relevant details. It is complemented by spearate household and personal forms for recording general information. In the following paper the term travel diary implies all three elements (the diary, the person form and the household form).

The remainder of the paper discusses to what extent and how the travel diary can be used to capture data for the assessment of policies directed at reducing the impact of transport on the social and natural environment. The requirements of a travel diary and the potential uses of new technologies in realising such a travel diary are then presented. A brief outlook on the possibilities of reasling such a diary concludes the paper.     

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.     

Household travel surveys: Where are we going?

In this paper, we commence by reviewing the recent history of household travel surveys. We note some of the problems that contemporary surveys are encountering throughout the world. We also review the data demands of current and emerging travel demand models, concluding that there are many new demands being placed on data, both in terms of the extent of the data required and the accuracy and completeness of the data. Noting that the standard method for conducting most household travel surveys is, and has been for some years, a diary, we briefly explore the evolution of the diary survey from the late 1970s to the present. In the next section of the paper, we explore a number of facets of potential future data collection. We include in this the use of GPS devices to measure travel, the potential of panel designs and some of the alternatives within panel designs, the development of continuous household travel surveys, especially in Australia, and the emerging capabilities in data fusion. Using some of these emerging methods for data collection and data simulation, we then propose a new paradigm for data collection that places the emphasis on a paid, national panel that is designed as a rotating, split panel, with the cross-sectional component conducted as a continuing survey. The basis of the panel data collection is proposed as GPS with demographic data, and the continuing national sample would also use GPS at its core. The potential to add in such specialised surveys as stated choice and process surveys is also noted as an advantage of the panel approach. We also explore briefly the notion that a special access panel or panels could be included as part of the design.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set

A major difficulty in the analysis of disaggregate activity-travel behavior in the past arises from the many interacting dimensions involved (e.g. location, timing, duration and sequencing of trips and activities). Often, the researcher is forced to decompose activity-travel patterns into their component dimensions and focus only on one or two dimensions at a time, or to treat them as a multidimensional whole using multivariate methods to derive generalized activity-travel patterns. This paper describes several GIS-based three-dimensional (3D) geovisualization methods for dealing with the spatial and temporal dimensions of human activity-travel patterns at the same time while avoiding the interpretative complexity of multivariate pattern generalization or recognition methods. These methods are operationalized using interactive 3D GIS techniques and a travel diary data set collected in the Portland (Oregon) metropolitan region. The study demonstrates several advantages in using these methods. First, significance of the temporal dimension and its interaction with the spatial dimension in structuring the daily space-time trajectories of individuals can be clearly revealed. Second, they are effective tools for the exploratory analysis of activity diary data that can lead to more focused analysis in later stages of a study. They can also help the formulation of more realistic computational or behavioral travel models.     

Competition and Ownership in Land Passenger Transport: The Sixth International Conference,Cape Town,South Africa,September 1999 - Part 1

The ability of conventional South African travel analysis practices to analyse adequately the travel needs of the poor is examined. The origins and nature of conventional practices are described, and it is observed that typically their scope has been limited to motorized modes, commutes and peaks. The paper reports on the findings of an activity diary survey administered in Cape Town that extended the conventional scope of analysis. An activity‐based survey method was selected because it typically yields higher rates of trip recall than other methods and is therefore relatively well suited to investigating travel behaviour in its fuller complexity. Selected findings of the survey are presented to demonstrate that travel occurring by non‐motorized modes, for non‐work purposes and during off‐peak periods, is considerable. It is argued that the conventional limitation in analytical scope can create serious misconceptions of the true nature of travel behaviour, particularly of low‐income households. By restricting the focus of analysis to motorized, work and peak period trip‐making, there is a risk of a routine bias being introduced in the way the urban passenger transport problem is understood, and in the nature of the interventions that are implemented as a result.     

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.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.     

Defining and understanding trip chaining behaviour

Trip chaining is a phenomenon that we know exists but rarely investigate. This could be attributed to either the difficulty in defining trip chains, extracting such information from travel diary surveys, the difficulty in analysing all the possible trip chain types, or all of the above. Household travel diary surveys provide a wealth of information on the travel patterns of individuals and households. Since such surveys collect all information related to travel undertaken, in theory it should be possible to extract trip-chaining characteristics of travel from them. Due to the difficulty in establishing and analysing all of the possible trip chain types, the majority of research on trip chaining has appeared to focus on work travel only. However, work related travel in many cities does not represent the majority of activities undertaken and, for some age groups, does not represent any travel at all. This paper begins by reviewing existing research in the field of trip chaining. In particular, investigations into the definitions of trip chaining, the defined typologies of trip chains and the research questions that have been addressed are explored. This review of previous research into trip chaining facilitates the following tasks: the identification of the most useful questions to be addressed by this research; defining trip chaining and associated typologies and defining data structures to extract trip chaining information from the household travel surveys conducted in metropolitan Adelaide, South Australia. The definition and typology developed in our research was then used to extract trip-chaining information from the household travel diary survey (MAHTS99) conducted in Adelaide in 1999. The extracted trip chaining information was then used to investigate trip-chaining behaviour by households. The paper reports the results of this analysis and concludes with a summary of the findings and recommendations for further investigations.     

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa,Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.     

Habitual travel behaviour: Evidence from a six-week travel diary

This paper introduces different methods to measure similarity of travel behaviour addressing the question of how repetitious travel behaviour actually is. It compares empirical results of the different methods based on the data from a six-week travel diary. In general, the results show that the day-to-day behaviour is more variable if measured with trip-based methods instead of methods based on time budgets. Furthermore, it is confirmed that the similarity declines if the method captures more of the complexity of the travel pattern. It is also shown that travel behaviour is neither totally repetitious nor totally variable. Even for the whole observation period, it is demonstrated that two days always have some common elements. Additionally, it is found that the different methods yield the same pattern of variability for different types of day. Travel behaviour is clearly more stable on work days. Similar results for all methods are also obtained concerning the question of how long the minimum period of observation should be. All measures show that the period should not be less than two weeks if one aims at measuring variability.     

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.     

Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index

Using Herfindahl–Hirschman Index and the Mobidrive and Thurgau six-week travel diary datasets this paper examines the degree of repetition of individuals’ choices of their daily activity–travel–location combinations. The results show that the repetitiveness of individual activity–travel–mode–location combinations is highly influenced by the individuals’ out-of-home commitments, the intra-household conditions and the availability and the accessibility of the activity locations. Different types of activity have different pattern of repetition. The level of repetition of individual’s daily activity–travel pattern is less correlated to travel mode choice, but more to the individuals’ commitments and obligations. The repetitiveness of mode choices is more related to the conditions or the accessibilities of the activity location, but not directly to the activity itself.