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Purpose — Automated fare collection systems implemented in public transportation systems in the last decade have provided a massive, continuous and low-cost source of reliable travel information. A direct and useful application of these data is the estimation of highly representative, although not bias-free, origin-destination (OD) matrices.

Methodology/approach — We discuss several issues with current OD matrix estimation methodologies, such as fare evasion and group travel, and their derived biases, specifically focusing on the Santiago (Chile) case. We also propose and apply two methods of validation: endogenous and exogenous validation. We elaborate on some methodological improvements that could be implemented to upgrade the activity estimation mechanics.

Findings — Several sources of bias in the estimation of OD matrix estimation from passive data are pointed and some solutions proposed. We apply improvements to existing methodologies and increase the success rate of trip estimations.

Practical implications — The reliable estimation of public transport OD matrices from passive data results in a valuable planning tool for both transit authorities and operators, much more representative and with less errors and biases that conventional data collecting techniques.

Originality/value of paper — This paper is one of the first works to deal with the subject.

This paper aims to show the need for tourism researchers to identify clearly units of observation and measurement.

Using examples focusing on international tourism, discussion shows how terms commonly used in tourism research can be vague, ambiguous or invalid for formulating theory or analysis or for generating and presenting research results.

A tourism research best practice is needed regarding identifying populations and observation and measurement units so ambiguous or invalid use of terms like person, visits, visitor and travelers does not occur and specific terms, for example, person‐visit, person‐visit‐day and party‐visit are used to communicate clearly.

The paper clarifies the necessity of using terms like person‐visit, person‐visit days and nuclear‐family party visit to give research clear meaning and, in some cases, to avoid propagating questionable or invalid analysis.

In the recent past, mobile technologies that track the movement of people, freight and vehicles have evolved rapidly. The major categories of such technologies are reviewed and a number of attributes for classification are proposed. The willingness of people to engage in such technologically based surveys and the reported biases in the make-up of the sample obtained are reviewed. Lessons are drawn about the nature of the samples that can be achieved and the representativeness of such samples is discussed. Data processing is addressed, particularly in terms of the processing requirements for logged data, where additional travel characteristics required for travel analysis may need to be imputed. Another issue explored is the reliability of data entered by respondents in interactive devices and concerns that may arise in processing data collected in real time for prompting or interrogating respondents. Differences, in relation to the data user, between data from mobile devices and data from conventional self-report surveys are discussed. Potentials that may exist for changes in modelling from using such data are explored. Conclusions are drawn about the usefulness and limitations of mobile technologies to collect and process data. The extent to which such mobile technologies may be used in future, either to supplement or replace conventional methods of data collection, is discussed along with the readiness of the technology for today and the advances that may be expected in the short and medium term from this form of technology.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

Companies are adopting innovative methods for responsiveness and efficiency in the public transport sector. The implementation of air-taxi services (ATS) in the transport sector is a move in this direction. Air taxis have a two-pronged advantage as they can reduce travel times by avoiding traffic congestion and have the potential to reduce carbon footprint compared to traditional modes of public transportation. Many companies worldwide are developing and testing ATS for practical applications. However, many factors may play a significant role in adopting ATS in the transport sector. This paper attempts to unearth such critical success factors (CSFs) and establish the interrelationships between these factors.

Fifteen CSFs were identified by systematically reviewing the literature and taking experts' input. An integrated multi-criteria decision-making (MCDM) technique, Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (DEMATEL-ANP [DANP]) was used to envisage the causal relationships between the identified CSF.

The results reveal that Govt Regulations (GOR), Skilled Workforce (SKF) and Conductive Research Environment (CRE) are the most influential factors that impact the adoption of ATS in the transport sector.

The research implications of these findings will help practitioners and policymakers effectively implement ATS in the public transportation sector.

This is the first kind of study that identifies and explores the different CSFs for ATS implementation in public transportation. The CSFs are evaluated with the help of a framework built with inputs from logistics experts. The study recognizes the CSFs for ATS implementation and provides a foundation for future research and smooth adoption of ATS.