Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

The importance of the multi-purpose journey to work in urban travel behavior

This study examines the journey to work as a multiple-purpose trip (home-to-home circuit). Using disaggregate travel diary data collected over 35 consecutive days, the study shows the importance of the multi-purpose work trip in the overall travel pattern of the urban household. A large proportion of many households' total travel is undertaken in conjunction with the journey to and from work. The paper also examines the nature of these work-induced travel linkages and finds that many types of urban establishments depend heavily upon stops made in connection with the work trip. In fact, there is a group of urban functions that have stronger travel links with the workplace than with the home or with any other type of urban establishment. The study examines the implications of the multi-purpose journey to work for policies regarding mode use and the viability of centrally-located urban functions.     

Methodologies for exploring the link between urban form and travel behavior

Communities are increasingly looking to urban design and the concept of the New Urbanism as an effective strategy for reducing automobile dependence in suburban areas. While the available empirical evidence suggests that automobile travel is lower in traditional-style neighborhoods, it provides limited insights as to how and why, largely because the research methodologies used have been insufficent for the task. Most of the studies addressing this question fall into three categories: simulation studies, aggregate analyses, and disaggregate analyses. Two other approaches offer greater promise for understanding the relationship between urban form and travel behavior: choice models and activity-based analyses. This paper reviews alternative approaches for exploring the link between urban form and travel behavior, outlines issues and complexities that this research must address, and, finally, suggests that the focus of this research should shift from the search for strategies to change behavior to a search for strategies to provide choices.     

Systematic variability in repetitious travel

The focus of this paper is the degree to which day-to-day variability in the individual's travel pattern has a systematic, or nonrandom, component. We first review the different sources of variability in travel, emphasizing the difference between between-individual and within-individual variation and the implications of this difference for travel analysis. After discussing the impact of measurement (i.e. the way in which travel behavior is measured) on the study of repetition and variability, we use the Uppsala data to examine the level of systematic variability in an individual's longitudinal travel record. The analysis focuses on two questions:

- How well does observation over one week capture longer-term (five-week) travel behavior; in other words, is behavior highly repetitive from week to week?

- How systematic is within-individual variability; in other words, are certain stops distributed over the five-week record in a nonrandom, that is either regular or clustered, fashion?

Using measures of travel that include more than one stop attribute (e.g. activity, mode, time of day, and location), we found that:

- A seven-day record of travel does not capture most of the separate behaviors exhibited by the individual over a five-week period, but it does capture, for most people, a good sampling of the person's different typical daily travel patterns.

- Whereas a considerable portion of intraindividual variability is systematic (nonrandom), clustering is a more important source of nonrandom variation than is regularity.

The results suggest that behavior does not follow a weekly cycle closely enough for a one-week travel record to measure the longer-term frequency with which the individual makes certain stops or to assess the level of day-to-day variation present in the individual's record. Because these results are likely to reflect the particular measures of behavior we used, one conclusion of this study is the need for other studies that replicate the aims of this one but use a variety of other travel measures. Only through such additional work can we truly assess the sensitivity of our findings to measurement techniques.     

An assignment principle for urban networks based on travel budgets

A new assignment principle for traveler behavior in an urban network is described which is based on empirical findings in the theory of travel budgets. It characterizes the distribution of travelers, demand, and modal split. It treats all travel decisions (whether to travel, where to go, how to get there) and the important costs (time and money) in a single, unified way. A numerical technique is proposed and it is applied to several examples to illustrate qualitative features.     

Additive measures of travel time variability

This paper derives a measure of travel time variability for travellers equipped with scheduling preferences defined in terms of time-varying utility rates, and who choose departure time optimally. The corresponding value of travel time variability is a constant that depends only on preference parameters. The measure is unique in being additive with respect to independent parts of a trip. It has the variance of travel time as a special case. Extension is provided to the case of travellers who use a scheduled service with fixed headway.     

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

Transportation - In the face of a continuous increase in the number of tourists in the Alps, the associated traffic volumes, and the resulting negative externalities, there is an urgent need to...     

Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.This paper investigates simple economic models of bus demand, examines alternative variables that can be used and discusses some alternative model forms. It demonstrates the results of a model using data from twelve urban bus operators in Britain and compares the results with those from other types of study. The model utilises fare and service quality elasticities to explain the decline in passengers on urban bus services, and derives an average elasticity with respect to fare changes of –0.31 and with respect to service quality changes of +0.62. It is estimated that fare rises accounted for 13% of the 43% decline in passengers over the last fifteen years, vehicle mileage reductions for 14.3% and that only 15.7% was due to such factors as rising car ownership which are often given as the cause of declining bus patronage.The results, by showing that passengers are far more sensitive to changes in service than they are to fare rises, are a useful guide to the broader public transport policy issues, and the paper concludes that the model does provide a useful method of forecasting public transport demand at a strategic level. Further work is needed, however, to establish more accurate forecasts for different types of passenger and studies are now being undertaken to establish these and to construct an operational forecasting model that can be applied with only limited data requirements     

The determinants of long distance travel in Great Britain

This study analyses of the determinants of long distance travel in Great Britain using data from the 1995-2006 National Travel Surveys (NTSs). The main objective is to determine the effects of socio-economic, demographic and geographic factors on long distance travel. The estimated models express the distance travelled for long distance journeys as a function of income, gender, age, employment status, household characteristics, area of residence, size of municipality, type of residence and length of time living in the area. A time trend is also included to capture common changes in long distance travel over time not included in the explanatory variables. Separate models are estimated for total travel, travel by each of four modes (car, rail, coach and air), travel by five purposes (business, commuting, leisure, holiday and visiting friends and relatives (VFRs)) and two journey lengths (<150 miles and 150+ miles one way), as well as the 35 mode-purpose-distance combinations.The results show that long distance travel is strongly related to income: air is most income-elastic, followed by rail, car and finally coach. This is the case for most journey purposes and distance bands. Notable is the substantial difference in income elasticities for rail for business/commuting as opposed to holiday/leisure/VFR. In addition, the income elasticity for coach travel is very low, and zero for the majority of purpose-distance bands, suggesting coach travel to be an inferior mode in comparison to car, rail and air. Regarding journey distance, we find that longer distance journeys are more income elastic than shorter journeys.For total long distance travel, the study indicates that women travel less than men, the elderly less than younger people, the employed and students more than others, those in one adult households more than those in larger households and those in households with children less than those without. Long distance travel is also lowest for individuals living in London and greatest for those in the South West, and increases as the size of the municipality declines.     

Direct analysis of the perceived importance of attributes of reliability of travel modes in urban travel

Reliability of travel modes was found to be the most important characteristic of transportation systems in several attitudinal investigations of individual travel behavior. This paper represents the first part of a research effort aimed at gaining a better understanding of the characteristics of reliability of transportation modes in urban travel. In this research, reliability characteristics are identified; their importance relative to each other is assessed, and an insight into possible structure of an objective reliability index is discussed. The research is based on perceived values of reliability, which were identified through a large attitudinal survey conducted in the Chicago metropolitan area.     

Analysis of travel activity determinants using robust statistics

This study investigates travel behavior determinants based on a multiday travel survey conducted in the region of Ghent, Belgium. Due to the limited data reliability of the data sample and the influence of outliers exerted on classical principal component analysis, robust principal component analysis (ROBPCA) is employed in order to reveal the explanatory variables responsible for most of the variability. Interpretation of the results is eased by utilizing ROSPCA. The application of ROSPCA reveals six distinct principal components where each is determined by a few variables. Among others, our results suggest a key role of variable categories such as journey purpose-related impedance and journey inherent constraints. Surprisingly, the variables associated with journey timing turn out to be less important. Finally, our findings reveal the critical role of outliers in travel behavior analysis. This suggests that a systematic understanding of how outliers contribute to observed mobility behavior patterns, as derived from travel surveys, is needed. In this regard, the proposed methods serve for processing raw data typically used in activity-based modelling.     

Is telecommuting cheaper than travel? An examination of interaction costs in a business setting

This paper presents an analysis of telecommunications and travel costs for typical business meetings. It is contribution to the debate on the substitutability of telecommunications for travel. An underlying assumption which supports the substitution hypothesis is that the cost of traversing distance through the use of telecommunication is lower than the cost of travelling.This paper addresses the relative cost of telecommunications and travel in conducting interactions. Three factors are assumed to determine these costs: distance, duration of interaction and number of participants. The analysis assumes that cost alone affects choice, and ignores other communication qualities.The relationship between telecommunication and travel costs was tested quantitatively through a case study of typical business meetings in the U.S., based on data from 1988. The results show that travel costs are lower than telecommunication costs for shorter distances, and that the relationship between telecommunications and travel costs differs substantially as a function of number of participants and meeting duration. Because of ongoing rapid changes in the costs of both of these interaction modes, the complex competition between them will continue. The implications of the findings for location decisions and policy-making are discussed.     

The significance and measurement of variability in travel behaviour

It is argued that an understanding of variability is central to the modelling of travel behaviour and the assessment of policy impacts, and is not the peripheral issue that it has often been considered. Drawing on recent studies in the UK and Australia, in conjunction with a review of the literature, the paper first examines the policy and analytical rationale for using multi-day data, then illustrates different ways of measuring variability, and finally discusses issues relating to the collection of suitable data for such analyses. In a policy context, there is a growing need for multi-day data to examine issues that affect general rather than one-day behaviour (e.g. to assess the distribution of user charges for road pricing, or patterns of public transport usage); while analytically, multi-day data is needed to improve our ability to identify the mechanisms behind travel behaviour and to derive better empirical relationships. Three measures of variability are presented: a graphical form showing daily differences in behaviour at the individual level; an aggregate, similarity index; and a hybrid graphical/numerical measure, which provides new insights into variability in daily patterns of behaviour. The paper raises a number of issues for debate, probably the most crucial of which is: variability in what? The way in which behaviour is measured crucially affects our conception of stability and variability.     

Classification issues in the analysis of complex travel behavior

Researchers have devoted considerable effort to identifying homogeneous travel-behavior groups, each of which also has distinctive sociodemographic characteristics. Interest in these efforts has been fueled by both theoretical and applied concerns. From a theoretical perspective, if such behaviorally and sociodemographically homogeneous groups cari be identified, we would have an improved understanding of the determinants of travel. From an applied perspective, because spatial choice models assume behavioral homogeneity within each sociodemographically defined subgroup, one would like to be able to identify groups that are homogeneous with respect to both behavior and sociodemographics. Most previous efforts to define such groups have classified individuals on the basis of one-day travel records. In this paper we review these efforts, note the problems inherent in using one-day travel records for identifying homogeneous travel-behavior groups, and use standard grouping procedures to classify individuals on the basis of behavior observed over a longer time period (five weeks). Using multi-day travel data means that the travel measures employed in classifying individuals are different from and more complex than those used with one-day data. We identify five travel-behavior groups, each of which has distinctive socio-demographic characteristics. Considerable intra-group variability remains, however, even though the groups are classified on the basis of longer-term behavior. The paper concludes with an examination of the implications of day-to-day variability in individual travel for classification procedures.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

Wanting to travel,more or less: Exploring the determinants of the deficit and surfeit of personal travel

This study investigates the determinants of peoples desire to increase or decrease the amount of travel they do. We use data from 1,357 working commuters, residents of three different neighborhoods in the San Francisco Bay Area, California. The dependent variables are indicators of Relative Desired Mobility for ten categories of travel (short- and long-distance overall and by several mode- and purpose-specific categories). These variables are measured on a five-point ordinal scale ranging from much less to much more, through which the respondents indicated the amount of travel they want to do (in the category in question) compared to what they are doing now. Censored ordered probit models were developed for these variables, with explanatory variables including general travel attitudes, specific liking for travel in each of the same separate categories, objective and subjective measures of the amount currently traveled in each category, and personality, lifestyle, and socio-demographic characteristics. The results support the hypotheses that the liking for travel has a strong positive impact, and subjective qualitative assessments of mobility have a strong negative impact, on the desire to increase ones travel. Finally, a number of general types of effects on Relative Desired Mobility were identified, among them complementarity and substitution effects. The results of this study can provide policy makers and researchers with new and valuable insight into key principles that affect individual travel demand.     

Nested logit models for mixed-mode travel in urban corridors

The nested logit (NL) model is a generalisation of the well-known multinomial logit (MNL) model which copes with its “independence from irrelevant alternatives” problem, at the expense of more difficult calibration and use. Mixed-mode movements (i.e. park-and-ride) are by nature not independent of competing single-mode options and have, therefore, traditionally been inadequately modelled in most empirical applications. This paper reports on the specification, estimation, testing and comparison of MNL and NL models using disaggregate data of work trips in an urban corridor, where choice was among several alternatives including mixed-mode options. It was found that the more general NL model was more adequate, not only in theory but in practice. The paper concludes by comparing the disaggregate NL model with previously calibrated aggregate NL models for the same corridor using a different data set.     

The influence of travel time variability on the value of time

Current benefits from travel time savings have only been related to the benefits from reducing mean travel time. Some previous attempts of including variability in the generalised cost function have mainly assumed commuters with fixed arrival time. This paper presents a comprehensive framework for valuing travel time variability that allows for any journey purpose and arrival time constraint. The proposed model is based on the expected utility approach and the mean-standard deviation approach. Stated Preference methods are considered the best technique for providing the data for calibrating the models. The values of time derived from the models are highly influenced by the value of travel time variability and it strongly depends on the probability distribution function travellers are faced with.