We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

Abstract

Recent work in transport research has increasingly tried to broaden out beyond traditional areas such as mode choice or car ownership and has tried to position travel decisions within the broader life context. However, while important progress has been made in terms of how to capture these additional dimensions, both in terms of detailed tracking of movements and in-depth data collection of long term decisions or social network influences, surveys have tended to look at only a handful (or often one) of these issues in isolation, especially at the data collection end. Making these links is the key aim of the data collection described in this paper. We conducted a comprehensive survey capturing respondents' travel, energy and residential choices, their social environment, life history and short-term travel patterns. The survey is composed of a detailed background questionnaire, a life-course calendar and a name generator and name interpreter. Participants were also required to use a smartphone tracking app for two-weeks. We believe that this is an unprecedented effort that joins complexity of the survey design, amount of information collected and sample size. The present paper gives a detailed overview of the different survey components and provides initial insights into the resulting data. We share lessons that we have learned and explain how our decisions in terms of specification were shaped by experiences from other data collections.

Clustering Life Course to Understand the Heterogeneous Effects of Life Events, Gender, and Generation on Habitual Travel Modes

“…Overall, the response rate of 1.7%, although low, is consistent with other implementations using similar unsolicited mailings to recruit and similar incentive payment levels [39]. The survey achieved a gender balance [36], but those who answered the survey were disproportionately highly educated and higher income, even within the San Francisco Bay Area, a phenomenon that is commonly found in previous retrospective survey-based mobility biography studies in other countries [9], [40]. The results should therefore be interpreted with this level of representation in mind.…”

“…The life history calendar portion of the WholeTraveler survey asked respondents to indicate when certain key life events occurred and what other factors pertained to their lives on an annual basis starting at age 20 and up to age 50. Retrospective surveys have been shown to cover relatively long periods of life course reasonably accurately when properly designed [12], [40]. We followed the recommendation in [12] on the survey structure, administration, sampling, and range of life course and mobility events considered.…”

Modelling multiple occurrences of activities during a day: an extension of the MDCEV model

“…The Leeds dataset was collected in 2017 as part of the ERC-funded project ‘DECISIONS’. Time use was only one of the aspects on which the data collection effort was focused, see Calastri and Hess ( 2020 ) for more details. The study participants first completed a background survey providing data on their socio-demographics, commuting behaviour, and attitudes.…”

Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI

“…He et al used mobile data, bus cards, and other social signals to collect transportation demand, forecast transportation congestion and found the root reasons, and then provided the best transportation path and other guidance [21]. Calastri et al captured social network structures, lifetime events, short-term travel, and activity planning from multi-source data collections [22]. Vinel et al addressed tight coupling between the performance of vehicle-to-vehicle communications and the performance of cooperative ITSs' safety applications [23].…”