Editorial: Triggers of behavioural change in an evolving world

This special issue collects six papers that were presented at the 2015 IATBR conference, which took place in Windsor, UK. It also includes a resource paper from one of the conference workshops. All regular papers were selected by the guest editors and subsequently peerreviewed in line with the European Journal of Transport and Infrastructure Research standards. Ultimately, 60% of submitted papers were accepted for publication

Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel

Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation

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

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 2-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

Modelling the loss and retention of contacts in social networks: The role of dyad-level heterogeneity and tie strength

Social networks have attracted attention in different fields of research in recent years and choice modellers have engaged with their analysis by looking at the role that social networks play in shaping decisions across a variety of contexts. The incorporation of the social dimension in choice models creates the need for understanding how social networks evolve over time and in particular which social contacts (alters) are retained over time by an individual (ego). Existing work fails to capture the full extent of ego-level and ego-alter level heterogeneity in these processes. We propose the use of a hybrid model framework which is based on the notion of latent strength of relationship. The resulting model allows for heterogeneity in the latent strength both across individuals and across their different relationships. In addition, we allow for heterogeneity not linked to the latent strength concept. We demonstrate the benefits of the approach using data from Chile, showing the presence of extensive variations in retention of social contacts and in strength of relationship both at the ego and ego-alter level, only some of which can be linked to observed characteristics

Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data

An understanding of activity choices and duration is a key requirement for better policy making, in transport and beyond. Previous studies have failed to make the important link with individuals' social context. In this paper, the Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model is applied to the choice of activity type and duration over the course of two days, using data from the Chilean city of Concepcion. In common with other studies, heterogeneity across decision makers is accommodated in the model by analysing the impact of different sociodemographic, mobility and residential location variables on both the activity choice and the time allocation decision. In addition, different social network and social capital measures are found to be signi cantly correlated with the choice and duration of different activities, and we show how these relationships seem to differ from the effects of socio-demographic variables. Finally, we perform a forecasting exercise using the MDCNEV model, highlighting the differences in substitution patterns from a standard MDCEV model

Accommodating correlation across days in multiple discrete-continuous models for time use

The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses how the theoretical accommodation of these effects is not straightforward, especially with budget constraints at the day and multi-day level. We rely on additive utility functions where we accommodate correlation between activities at the within-day and between-day level using a mixed MDCEV model, with multivariate random distributions. We illustrate our approach using a well-known time use datasets, confirming our theoretical points and highlighting the benefits of allowing for correlation across days in terms of model fit and behavioural insights

Mode choice with latent availability and consideration: Theory and a case study

Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice

Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data

A multi-country panel study of behaviour, perceptions and expectations during different stages of the COVID-19 pandemic

It is widely accepted that the COVID-19 pandemic has dramatically changed travel patterns since 2020, largely due to restrictions on people's movement and work-from home practices. A large number of studies have been conducted to understand such changes from a trip maker's perspective, using different types of mobility data collected across the world. This study uses survey panel data on travel behaviour and activity participation collected between May 2020 and November 2020 in the United Kingdom, Australia, Colombia and South Africa using a consistent survey approach. We identify a role for three key underlying latent constructs, namely 1) concerns about COVID-19, 2) approval of government interventions and 3) scepticism towards COVID-19 measures. Using a hybrid choice model, we study the role of these constructs in explaining stated travel choices in two hypothetical post-pandemic scenarios. The model results show significantly different perceptions towards COVID-19 concerns and government handling of the COVID-19 pandemic (including restrictions) across countries. The model estimates show a clear influence for the latent constructs in explaining stated behaviour in the hypothetical post-pandemic scenarios across the four countries, where this is also impacted by lockdown stringency levels as well as socio-demographics

Travel, social networks and time use: modeling complex real-life behavior

This chapter encompasses the different themes and contributions presented in the Ph.D. thesis that was awarded the 2017 Eric Pas dissertation Prize at the 15th International Conference on Travel Behavior Research. Four main topics are discussed: performing choice modeling with semi-ubiquitous data, modeling decisions related to social networks, estimating and forecasting discrete-continuous choices and revealed preferences data collection. Results on how the incorporation of availability and consideration in mode choice models using GPS data can help obtaining meaningful results are discussed. The advanced models applied to different decisions related to social networks allow to reach conclusions that contribute to the current debate on the substitution of travel for social purposes with ICT. Most of the modeling of discrete-continuous choices is focused on the analysis of time use and on finding methods to allow for correlation across different activities and days of the week, especially in forecasting. These contributions exclusively make use of revealed preference datasets with repeated observations. The experience gained by the work in the thesis inspires the development of a unified data collection, gathering information about time use, social network and short-term and long-term travel behavior. The data collection protocol is not unique to travel behavior research and can be used in different fields