Periodic Reporting for period 1 - URGENT (Choice, necessity or chance? Understanding behaviouR chanGE iN Transport)
Periodo di rendicontazione: 2022-10-01 al 2025-03-31
The missing contribution of the transport sector to the reduction of greenhouse gas emissions raises questions about our highly mobile, carbon-intense, and car-dependent lifestyles. To reach sustainability goals, significant transformations in transport behaviours and systems are urgently required. The URGENT project aims to provide the knowledge base for more effective intervention strategies to achieve such transformations. To this end, the project examines the individual and contextual circumstances under which people change their mobility behaviour and the mental mechanisms involved in this change process.
The project relies on a holistic, interdisciplinary approach and utilises a unique longitudinal dataset: Over a three-year period, data will be collected via a large biannual survey, complemented by qualitative interviews and a mobility diary app for a sub-sample of participants. With this threefold approach, URGENT examines how people adapt their mobility behaviours in anticipation of and in response to:
· residential relocation;
· retirement;
· crash involvement;
· electric vehicle adoption.
A special focus lies on the different effects of sudden versus planned/anticipated and desired versus undesired life events. URGENT thus pursues the question of whether and to what extent travel behaviour change is subject to choice, necessity, or chance.
Applying causal machine learning methods, the project further aims to uncover which personal, social, technical, or spatial factors are the most relevant initiators of behaviour change and will specify the causal relations between the involved factors. URGENT will additionally examine rebound effects of changed mobility behaviour and reveal under which conditions, and to what extent, behaviour change in one area (e.g. commuting) positively or negatively spills over to other areas (e.g. air travel, food consumption).
URGENT’s analytical strategy cross-fertilizes concepts from psychology (behaviour change models), human geography (mobility biographies approach), sociology (mobility cultures), and machine learning (causal discovery and causal inference). The project aims to increase the understanding of behaviour change in transport, and also bears the potential to lead to a breakthrough in studying causality in transport research at large.
The project relies on a holistic, interdisciplinary approach and utilises a unique longitudinal dataset: Over a three-year period, data will be collected via a large biannual survey, complemented by qualitative interviews and a mobility diary app for a sub-sample of participants. With this threefold approach, URGENT examines how people adapt their mobility behaviours in anticipation of and in response to:
· residential relocation;
· retirement;
· crash involvement;
· electric vehicle adoption.
A special focus lies on the different effects of sudden versus planned/anticipated and desired versus undesired life events. URGENT thus pursues the question of whether and to what extent travel behaviour change is subject to choice, necessity, or chance.
Applying causal machine learning methods, the project further aims to uncover which personal, social, technical, or spatial factors are the most relevant initiators of behaviour change and will specify the causal relations between the involved factors. URGENT will additionally examine rebound effects of changed mobility behaviour and reveal under which conditions, and to what extent, behaviour change in one area (e.g. commuting) positively or negatively spills over to other areas (e.g. air travel, food consumption).
URGENT’s analytical strategy cross-fertilizes concepts from psychology (behaviour change models), human geography (mobility biographies approach), sociology (mobility cultures), and machine learning (causal discovery and causal inference). The project aims to increase the understanding of behaviour change in transport, and also bears the potential to lead to a breakthrough in studying causality in transport research at large.
At the end of 2023 the biannual panel survey was initiated. When completed, it will consist of seven waves of data collection. The survey gathers information on participants’ mobility behaviour, related psychological factors (attitudes, norms, values, perceived barriers and control), demographics, life events, and satisfaction with travel, residential location, and life more generally. Around 6000 participants have completed the first three waves of the panel survey.
A sub-sample of participants who expected to experience one of three selected life events (i.e. residential relocation, retirement, or EV adoption) additionally participated in a qualitative interview before the event and in another interview about 3-9 months after the event. These interviews focus on anticipated changes in mobility behaviour (interview 1) as well as actual behavioural changes and changed attitudes and satisfaction following the life event (interview 2). The analysis of the interviews will particularly explore what role desire for change and preparation play in the process of behaviour change and how often behaviour change is an intended process versus something that merely happens by chance. To explore the nature and consequences of unintended or random changes, interviews with people who have been involved in a traffic crash within the first 1.5 years of the data collection will additionally be conducted.
To capture changes in mobility behaviour in more detail, case study participants have used a mobility app that automatically collects travel data over a period of nine months, including the time before and after the life event. The app has also requested regular attitudinal information on the main transport modes, thereby providing detailed information on how travel attitudes and behaviour change after a life event.
A sub-sample of participants who expected to experience one of three selected life events (i.e. residential relocation, retirement, or EV adoption) additionally participated in a qualitative interview before the event and in another interview about 3-9 months after the event. These interviews focus on anticipated changes in mobility behaviour (interview 1) as well as actual behavioural changes and changed attitudes and satisfaction following the life event (interview 2). The analysis of the interviews will particularly explore what role desire for change and preparation play in the process of behaviour change and how often behaviour change is an intended process versus something that merely happens by chance. To explore the nature and consequences of unintended or random changes, interviews with people who have been involved in a traffic crash within the first 1.5 years of the data collection will additionally be conducted.
To capture changes in mobility behaviour in more detail, case study participants have used a mobility app that automatically collects travel data over a period of nine months, including the time before and after the life event. The app has also requested regular attitudinal information on the main transport modes, thereby providing detailed information on how travel attitudes and behaviour change after a life event.
Expected impacts of the project include a more comprehensive understanding of human behaviour, particularly how, when, and why people change their travel behaviour. This knowledge will inform and improve existing psychological decision models and can be used to develop more effective intervention strategies to reduce carbon emissions and car dependency.
By examining potential rebound and spillover effects of changed travel behaviour and technology adoption, the project can help identify ways to minimize rebound effects and promote actions to increase the likelihood of positive spillover from sustainable travel choices to other consumption areas.
The application of causal machine learning methods on the rich longitudinal dataset, combined with domain expertise in psychology, transport geography, and sociology, is expected to lead to technological innovations in data analysis and modelling on the one hand and to a more integrated and holistic understanding of transport behaviour on the other hand.
By examining potential rebound and spillover effects of changed travel behaviour and technology adoption, the project can help identify ways to minimize rebound effects and promote actions to increase the likelihood of positive spillover from sustainable travel choices to other consumption areas.
The application of causal machine learning methods on the rich longitudinal dataset, combined with domain expertise in psychology, transport geography, and sociology, is expected to lead to technological innovations in data analysis and modelling on the one hand and to a more integrated and holistic understanding of transport behaviour on the other hand.