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Purpose — The Department for Transport's 2011 GPS National Travel Survey (NTS) pilot study investigated whether personal GPS devices and automated data processing could be used in place of the 7-day paper diary. Using GPS technology could reduce the relatively high burden that the diary places upon respondents, reduce costs and improve data quality.

Design/methodology/approach — Data was collected from c.900 respondents. Practical changes were made to the existing methodology where necessary, including the collection of information to support data processing. Processing was undertaken using the University of Eindhoven's Trace Annotator. Results from the GPS pilot were then compared to those from the main NTS diaries for the same period.

Findings — There were no insurmountable problems using GPS devices to collect data; however, the processed GPS data did not resemble the diary outputs, making GPS unsuitable for the NTS. The GPS data produced fewer and longer trips than the diary data. The purpose of a quarter of the GPS trips was unclear, and a disproportionate share started and ended at home.

Research limitations — Further work to manually inspect trips identified via validation as unfeasible and subsequently refine the processing algorithms would have been desirable had time permitted. GPS data processing may have been hindered by missing GPS data, particularly in the case of rail travel.

Originality/value — This research used an accelerometer-equipped GPS device to better predict the method of travel. It also combined addresses that respondents reported having visited during the travel week with GIS data to code the purpose of trips without using a post-processing prompted-recall survey.

There is extensive evidence that decision-makers, faced with increasing information load, may simplify their choice by reducing the amount of information to process. One simplification, commonly referred to as attribute non-attendance (ANA), is a reduction of the number of attributes of the choice alternatives. Several previous studies have identified relationships between varying information load and ANA using self-reported measures of ANA. This chapter revisits this link, motivated by recognition in the literature that such self-reported measures are vulnerable to reporting error.

This chapter employs a recently developed modelling approach that has been shown to effectively infer ANA, the random parameters attribute non-attendance (RPANA) model. The empirical setting systematically varies the information load across respondents, on a number of dimensions.

Confirming earlier findings, ANA is accentuated by an increase in the number of attribute levels, and a decrease in the number of alternatives. Additionally, specific attributes are more likely to not be attended to as the total number of attributes increases. Willingness to pay (WTP) under inferred ANA differs notably from when ANA is self-reported. Additionally accounting for varying information load, when inferring ANA, has little impact on the WTP distribution of those that do attend. However, due to varying rates of non-attendance, the overall WTP distribution varies to a large extent.

This is the first examination of the impact of varying information load on inferred ANA that is identified with the RPANA model. The value lies in the confirmation of earlier findings despite the evolution of methodologies in the interim.

This chapter proposes a new mixture model which allows for heterogeneity in sensitivities and decision rules across decision makers and attributes.

A new mixture model is put forward in which the different latent classes make use of different decision rules, where the use of generalised random regret minimisation kernel allows for within class heterogeneity in the decision rules applied across attributes.

Our theoretical developments are supported by the findings of an empirical application using data from a typical stated choice survey.

Existing work has looked at heterogeneity in decision rules and sensitivities across respondents. Other work has focused on the possibility that different decision rules apply to different attributes. This chapter puts forward a model that combines these two directions of research and does so in a way that lets the optimal specification be driven by the data rather than being imposed by the analyst.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The 1990s have seen a major expansion in both the interest in retailing as an academic research subject and in the availability of European retail academic journals. Using a bibliometric approach, this paper investigates the development of published academic retail research in these journals. It identifies different emphases within the retail journals and retail research. Overall, however, retailing is identified increasingly as a synthetic rather than an interdisciplinary subject. A gulf between the direction that European scholars and US scholars are following is considered.