Handbook of Choice Modelling
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Handbook of Choice Modelling

Edited by Stephane Hess and Andrew Daly

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.
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Chapter 6: Self-tracing and reporting: state of the art in the capture of revealed behaviour

Nadine Rieser-Schüssler and Kay W. Axhausen


The measurement of travel behaviour is based on the traces, which travellers leave willingly or unwillingly. The chapter discusses and describes the range of these traces. They range from the participation in travel diary surveys to the technical records of mobile phone providers: some of them are recent, such as tracing by smart phones, some are driven by the curiosity of the travellers themselves, such as the dollar bill tracing website www.wheresgeorg.com, some by national accounting or policy making, such as the various national travel diary studies. Each of the available forms has well-known biases during the various phases of data collection and processing, which we try to highlight and discuss. It is necessary to point out, that the object of the data collection varies between the approaches. Traditional transport planning, national statistics driven approaches are interested in identifiable movements, for example stages, trips or journeys, which can be described with an origin, a destination, a purpose and a (main-)mode and associated with the person undertaking this movement. This movement has social meaning as it has been undertaken to satisfy some need or task of the person reporting it. New tracing technologies, such as the interaction records of mobile phones with their localized infrastructure or the geo-location stamps of Twitter, provide movement information as a byproduct, but at random intervals and without socially meaningful information about the movement when tracing for short periods (see Table 6.1).

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