Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

Using SenseCam to pursue “ground truth” for global positioning system travel surveys

Global Positioning System (GPS) surveys have been conducted for the past decade. Although GPS records were initially regarded as providing “ground truth” of travel, it has been found subsequently that they have some errors. SenseCam, a small passive digital camera, provides a chance to pursue ground truth by capturing images every 20 s on average. This paper discusses how SenseCam could help GPS data collection and shows potential benefits for both SenseCam and GPS research. This paper also investigates the performance of GPS devices in detail in terms of recording data by comparisons between GPS results and SenseCam images. The specific issue of missing GPS data is discussed and examined in this paper.     

Immobility in travel diary surveys

The share of immobile persons, i.e. persons not leaving their homes on a given reporting day, is both a central result and a prime quality indicator of a travel diary survey. The wide range of values for the share of immobiles reported in travel diary and time budget survey literature has motivated this in-depth analysis of the reasons for these disparities. This paper collates available evidence on the share of immobiles in travel diary surveys. The share of these non-travellers (UK), or no-trippers (US), varies greatly between otherwise similar surveys. After analysing both disaggregate and aggregate information, this paper concludes that the share of immobiles should be in the range of 8%–12% for the standard one-day, weekday-only travel diary. The analysis suggests that a substantial share of respondents refuse to participate in a soft way, i.e. by claiming not to have left the house. In its conclusions, the paper sketches new ways to reduce the share of such soft refusers during the interview and to identify them during the analysis.      

Changing the timing of activities in resolving Scheduling Conflicts

Following the growing interest in the characterisation and modelling of activity scheduling and re-scheduling behaviour, this paper reports the results of a study on the resolution of activity scheduling conflicts. Using empirical data collected through an Internet survey, the modification of the timing of pre-planned activities to accommodate a new activity in the schedule was analysed. Schedule adjustment was studied using a parametric hazard model. The results indicate that the characteristics of the activities involved are the most important factors influencing the process of schedule change. Several correlations among schedule modifications were found. Harry Timmermans is a Professor at the Eindhoven University of Technology. He is also Director of the Urban Planning Group and the European Institute of Retailing and Services Studies. Tomás Ruiz is a Lecture and researcher at the Technical University of Valencia (Spain). Prior to his employment with the University, Dr. Ruiz was a consultant at Estudios, Proyectos y Planificación S.A.     

Is the intention to use public transport for different travel purposes determined by different factors?

The objective of this study was to examine the psychological predictors of the intention to use public transport for three travel purposes: work or study, shopping, and leisure. An expanded version of the theory of planned behaviour (TPB) which contains overall image and past behaviour is used. Data were gathered through the survey of 392 residents living in the central parts of Kuala Lumpur in Malaysia. These data were analysed using the partial least squares technique. The results indicate that attitude and perceived behavioural control are significant predictors of the intention to use public transportation for various purposes. Further, they explain between 34.6% and 49.8% of the intention variance. By adding the overall image and past behaviour to the original predictors in the TPB, the explained variance, with regard to work or study, shopping, and leisure purposes, increased by 5.6%, 5.1%, and 6.8%, respectively.     

Towards a microeconomic framework for modelling the joint choice of activity–travel behaviour and ICT use

The rapid development of information and communication technologies (ICT) has been argued to affect time use patterns in a variety of ways, with consequent impacts on travel behaviour. While there exists a significant body of empirical studies documenting these effects, theoretical developments have lagged this empirical work and in particular, microeconomic time allocation models have not to date been fully extended to accommodate the implications of an increasingly digitised society. To address this gap, we present a modelling framework, grounded in time allocation theories and the goods–leisure framework, for joint modelling of the choice of mode of activity (physical versus tele-activity), travel mode and route, and ICT bundle. By providing the expression for a conditional indirect utility function, we use hypothetical scenarios to demonstrate how our framework can conceptualise various activity–travel decision situations. In our scenarios we assume a variety of situations such as the implications of severe weather, the introduction of autonomous vehicles, and the interaction between multiple decision makers. Moreover, our approach lays the microeconomic foundations for deriving subjective values of ICT qualities such as broadband speed or connection reliability. Finally, we also demonstrate the means by which our framework could be linked to various data collection protocols (stated preference exercises, diaries of social interactions, laboratory experiments) and modelling approaches (discrete choice modelling, hazard-based duration models).     

Should we abandon activity type analysis? Redefining activities by their salient attributes

This paper poses a challenge and begins a search. The challenge is to reconsider the usefulness of traditional activity types (“work”, “shopping”, etc.) in the understanding and modelling of travel behaviour. The search is for the more salient attributes of activities that may serve to better explain complex travel behaviours—such as activity scheduling and tour formation. In particular, this paper focuses on explicit measures of the spatial, temporal and interpersonal flexibility of activities, along with several traditional attributes (frequency, duration, involved persons, travel time, and location). Data from a recent in-depth week-long activity scheduling survey was used to define and compare these attributes. Results show that considerable variability in the attributes between and within traditional activity groups is evident. This casts considerable uncertainty on assumptions that statically assign levels of spatial, temporal, and interpersonal flexibility to any given activity type. A Principal Components Analysis further revealed eight new distinct clusters of activities that share like attributes. The relative role of each attribute in each component is examined, and subjective interpretations emerged (e.g., “Long and frequent”, “Space and time flexible” “Social networking”). The implications of these results for future model development and research are discussed. Future research should continue to expand the search for salient attributes and link them more directly to decision processes.     

Travel mode choice: affected by objective or subjective determinants?

This contribution presents theoretical considerations concerning the connections between life situation, lifestyle, choice of residential location and travel behaviour, as well as empirical results of structural equation models. The analyses are based on data resulting from a survey in seven study areas in the region of Cologne. The results indicate that lifestyles influence mode choice, although slightly, even when life situation is controlled for. The influence of life situation on mode choice exceeds the influence of lifestyle. The influence that lifestyle, and in part also life situation, has on mode choice is primarily mediated by specific location attitudes and location decisions that influence mode choice, respectively. Here objective spatial conditions as well as subjective location attitudes are important.

Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

The influence of built environment to the trends in commuting journeys in the Netherlands

In this paper we describe commuting trends in the Netherlands in the past decade and examine the influence of urban form and travel accessibility on commuting journeys over time on the basis of data from the Dutch National Travel Survey. Exploratory analysis is performed to identify changes in commuting participation, departure time, commuting time, commuting distance and the modal split. Regression analysis and choice models are used to examine the influence of the built environment on commuting parameters over time. The results indicate that urban form has consistently influenced the parameters of commuting journey in the Netherlands in the last 10 years. However, the trend of the influence is unique for each commuting model. Some influences have become less significant in the last decade and some have become stronger.