Introduction

Explanations of mobility change in transport studies are predominantly economic based. Dominant frameworks also, in general, tend to consider single socio-economic explanations. Differences in mobility amongst the population are frequently considered in terms of structural factors that commonly include age, income, location and gender. Current approaches towards examining mobility trends tend to consider the behaviour of population groups at single points in time, which is then repeated. For example, when considering the mobility of older people, the 65 years and over age group may be examined at 1995, 2000, 2005, etc. without considering wider socio-demographic composition of the age group which changes between different time points.

Mackett (2002) highlights the wider impact on transport planning in the U.K., as models are primarily based on commuting trips, with only 16% of trips in the U.K. for commuting (Department for Transport [DFT], 2015). As a result, there is a focus on individual trips, rather than contextualising travel decisions by lifestyles. As travel has become more complex, neither the models nor the data sources reflect this, which leads to simplistic transport policies (Mackett, 2002).

Analysis of mobility trends need not be limited only to the empirical level driven by individual decisions, but may also be shaped by deeper levels of meaning associated with social, cultural and economic structures. Furthermore, these deeper structures will themselves change over time, producing significant shifts in mobility behaviour as meanings are re-negotiated as well as being altered over time.

The framework presented in this paper adopts a dynamic multi-level approach that considers a combination of structural and cultural factors operating simultaneously to influence mobility outcomes. Fielding (1993, 2010) used a similar framework to schematise the economic processes in relation to migration. In this paper, the framework has been adapted to outline the multi-level forces involved in stimulating changes in mobility and consists of three different layers. These layers represent different processes that occur at varying structural levels and also operate over varying temporal scales. An examination of the simultaneous operation of these levels will assist in understanding future mobility outcomes.

The rest of the paper is structured as follows. The next section provides an overview of recent declining mobility trends and current explanations, with particular emphasis on birth cohorts. The proposed framework is introduced in the following section which includes presenting an outline of the period effects and varying levels of structural effects involved in mobility change. After an examination of these effects, the paper will consider an example of how these different factors have shaped current mobility trends for the so-called Baby Boomer cohort, considered here as born (1945–1964). The paper will conclude by presenting the implications for future research, in particular for studies considering “Generation Y” or “Millennials”, commonly defined as being born from around the 1980s onwards.

Recent trends of declining mobility

Although there has been an upward trend in mobility over the past three decades as incomes have risen and car ownership has increased, particularly amongst the poorest fifth of British households over the past decade, more recently there has been a reduction in the rate of growth in travel that began in the mid-1990s (Independent Transport Commission, 2010). Recently, studies have revealed that mobility has been declining in advanced economies including Germany, Australia, France, the U.K., the U.S.A. and Japan, over the past decade, leading to some commentators highlighting the emergence of “peak car”, whereby total car travel per capita has shown very low growth and even declined in some countries (Goodwin, 2012b; Metz, 2010). As this decline is occurring across different countries, there are some common features as possible explanations. To date many of the explanations around this declining mobility have been attributed to economic factors, such as changes in fuel prices, income, GDP and unemployment, particularly by governments (Goodwin, 2012a; Grimal, Collet, & Madre, 2013). As the trend occurred prior to the most recent economic recession in 2008, it is considered to be a real structural effect rather than a temporary cyclical effect (Goodwin, 2012a). It should also be recognised that the nature of the influence of economic factors on mobility has changed, with increasing attention being paid to lifestyle and attitudinal factors on mobility (Goodwin & Van Dender, 2013).

Demographic influences on “peak car” have been considered, with a particular focus on younger people. These include the reduction in the propensity to learn to drive and buy a car, in particular amongst younger men (DFT, 2010; Frändberg & Vilhelmson, 2011; Goodwin, 2012a; Kuhnimhof, Buehler, Wirtz, & Kalinowska, 2012; Noble, 2005a; Van Der Waard, Jorritsma, & Immers, 2013). Explanations for this include the increasing costs of motoring (DFT, 2010) and greater value placed on the use of purchasing new mobile technology (such as smartphones and tablets) and having internet access amongst younger people (Hopkins & Stephenson, 2014). Changing life stages and household living arrangements have also been found to have a great impact on the decline in youth driving licencing (Delbosc & Currie, 2013).

Other demographic forces changing travel car patterns have been highlighted, such as population growth and urbanisation, as well as an ageing population (Kuhnimhof, Zumkeller, & Chlond, 2013; Metz, 2013). Spatial distributions of demographic projections, particularly in urban areas, influencing “peak car” have also been considered (Goodwin & Van Dender, 2013; Headicar, 2013). Whilst the “peak car” phenomenon has been explained by a few common factors, there are other factors that contribute to changing mobility, particularly social and cultural factors on mobility that have been explored in studies on transport and society (Lyons, 2004), older people (Musselwhite & Haddad, 2010; Schwanen & Páez, 2010) and life-course changes (Goodwin, 1989; Scheiner, 2014; Scheiner & Holz-Rau, 2013).

In addition, understanding of mobility in transport studies is being augmented with the development of the “new mobilities paradigm” (Shaw & Hesse, 2010). The “new mobilities paradigm” has broadened the concept of mobility, incorporating the notion that mobility itself has become a significant activity rather than simply a means to travel from place to place (Pooley, 2009). The development of information communication technologies (ICT) has brought about opportunities for virtual mobility (Pooley, Turnbull, & Adams, 2005b). In the example of declining mobility, the growth in online activities, for example, social networking, online retail and mobile computing, is thought to explain a reduced need to travel (Goodwin, 2012a; Metz, 2010). However, the relationship between ICT and transport is complex, with evidence suggesting both substitution and stimulation of travel, as well as the enhancement of the travel experience (Lyons, 2009).

These studies and explorations into mobility highlight the need for the debates surrounding “peak car” to be more attentive to the changes of the economic and social contexts populations experience simultaneously. Ryder (1965) argued that cohort membership could be as important in determining behaviour as other social structural factors such as socio-economic group.

Birth cohort membership is likely to be an important consideration which has tended to be neglected in these explanations for declining mobility. This is particularly important to consider if the mobility decline is perceived as being a structural effect rather than a temporary, cyclical effect.

Currently missing from the “peak car” debate then is a consideration of the deeper-layered, multi-level combination of demographic forces and cultural underpinnings behind explanations for mobility and travel behaviour changes. Understanding the combination of different structural forces allows for reflection of the different socio-economic contexts that could assist in interpreting findings. By exploring the simultaneous interactions between different levels of socio-demographic factors, an explanation of the settings in which cohorts grow up can be added to the debate. Through establishing a framework, past and existing conditions that have influenced and are influencing the mobility of current cohorts can be identified to assist in the interpretation of results as well as contribute to predictions regarding future mobility outcomes.

Cohort groups and mobility

Broadening the socio-demographic and cultural perspective particularly in relation to cohorts is crucial when investigating changing mobility, given the different views, values and expectations between cohort groups that have developed as each birth cohort has grown up in different social, economic and historical environments (Ryder, 1965). Men and women in each birth cohort face a unique set of opportunities and constraints at different points over their life-course. As people age through the life-course, mobility changes due to different activities and circumstances associated with different life-course stages (Oppermann, 1995; Pooley, Turnbull, & Adams, 2005a; Ryley, 2006). Understanding the wider influences on these birth cohorts will assist in generating clues for future mobility.

In recent years, studies have begun to take into greater consideration the differences between birth cohorts and changing mobility trends (Frändberg & Vilhelmson, 2011; Scheiner & Holz-Rau, 2013; Thakuriah, Menchu, & Tang, 2010), particularly concerning the mobility of older people (Hakamies-Blomqvist & Henriksson, 1999; Hjorthol, Levin, & Sirén, 2010; Newbold, Scott, Spinney, Kanaroglou, & Páez, 2005).

By focusing on age groups, certain assumptions about different groups have been made. Travel behaviour of older groups has derived from the behaviours of pre-boomer cohorts.

In addition, historically the 20–29 age group was highly mobile (Pooley et al., 2005b); however, more recently Frändberg and Vilhelmson (2011) have found that mobility amongst younger age groups has declined in Sweden. There are also shifts occurring in relation to the gender mobility gap; studies have found that gender differences in mobility have been converging at the aggregate level, particularly amongst older people (Frändberg & Vilhelmson, 2011; McQuaid & Chen, 2012; Noble, 2005b; Rosenbloom, 2006). Tilley and Houston (2016) find that women of the “Generation Y cohort” (b.1976–1985) had higher levels of mobility compared to their male counterparts. This particular finding has not been observed previously, indicating that aggregate mobility trends change in line with each successive cohort. Therefore, cohort effects are useful in explaining mobility trends. With each successive cohort there is likely to be greater variation in their mobility in line with wider socio-demographic and other structural factors. Cohort effects are those attributable to differences in historical, social, economic, cultural and technological contexts, in which different cohorts have grown up (Rafferty & King-Hele, 2010); in essence, these characterise cohort groups. Cohort effects are explained by illustrating the influential contextual factors in the model presented within this paper.

Multi-level perspectives

Recognising the significance of the contributions of wider social theory in mobility studies (Bailey, 2005), studying travel behaviour involves more than simply observing the empirical processes captured in cross-sectional spatial datasets (in Gregory’s [1978] terms, mobility as part of the spatial patterns of social exchange). There is greater value in seeking explanations that are shaped by spatial structures and schema over space–time.

Fielding (2012), for example, has taken this argument about the significance of different levels of socio-spatial structures and applied it to wider mobility research in relation to his search to explain temporal trends in internal migration in the U.K. between 1950 and 2010.

Fielding (2010) used this framework to schematise the economic processes in relation to mass migration, that is, large net streams crossing national borders resulting in significant population redistribution. With the framework, Fielding demonstrates that there are deeper structural explanations to the global economic crisis rather than it being a cyclical downturn (King, 2012). In relation to the “peak car” phenomena, as the decline in mobility occurs after the recession, this is considered to be a real structural rather than a temporary cyclical effect (Goodwin, 2012a). Therefore, drawing on Fielding’s work helps to illustrate the structural explanations behind “peak car” and consider how mobility is influenced at these deeper levels.

Whitmarsh (2012) also argues that the multi-level perspective (MLP) framework is a useful analytical framework for understanding transitions, as it highlights dynamics and complexity of both incremental and radical innovation and is an approach that moves beyond economic focused models. Geels (2012) uses this framework to explain transport transitions, noting that it recognises there are no single “causes” or drivers in transitions; rather there are processes on multiple dimensions and at different levels. However, there are ways in which this can be improved. As Whitmarsh (2012) highlights, the applications of MLP in transport are limited in understanding societal systems as they can be treated as exogenous, with lifestyles (Shove & Walker, 2007) and culture (Haxeltine et al., 2008) also considered in this way.

In the MLP framework, Geels (2012) describes the socio-technical landscape, which is in broader terms, as it includes a variety of different things, such as urban layouts, societal values and beliefs and macroeconomic trends; therefore, the macro-level is less clearly conceptualised (Whitmarsh, 2012). The framework by Fielding (2010) is useful to adapt as it provides a broader overview of the socio-economic context, and thus begins to unpack this “landscape”.

Changing mobility of cohort groups

This paper introduces a conceptual model to explore the relationship between birth cohorts and mobility, over time and through space. The proposed framework enables existing research and analysis into changing travel trends, such as those related to “peak car”, to be used in a more holistic sense.

Empirical analysis using data from the U.K. National Travel Survey from 1995 to 2008 in Tilley and Houston (2016) reveals that birth cohorts have changed their travel behaviour over time, not all as a result of age effects. By adopting a cohort approach, older people were found to travel further than previous generations, whilst younger men were found to have much lower mobility than previous generations.

An examination of mobility by age group revealed that the mobility of those aged under 40 declined, with the most significant decline amongst younger men under 30, so that women of the same age now travel further. However, those aged 40 and over increased their weekly mobility. In particular, the weekly mobility of women aged 60–69 increased, as well as that of older men aged 70 and over.

Exploring the mobility of different cohort groups, when comparing men and women, older men belonging to the Boomer cohorts (b.1946–1965) had significantly higher weekly mobility compared to women, with the Boomer cohorts having the highest mobility of all cohort groups, with some of the oldest cohorts having a decline in mobility over time.

The mobility of women belonging to the younger birth cohorts in the study, Generations X (b.1966–1975) and Y cohort (b.1976–1985), had significantly increasing mobility over time. Indeed, Generation Y women had significantly higher weekly mobility in 2006–2008 compared to men of the same cohort. This finding has not been observed previously, indicating that mobility changes with each successive cohort.

These findings indicate that cohort membership is important to understand mobility patterns. With each successive birth cohort ages, there is likely to be greater variation in mobility, given the different socio-economic contexts they experience. The framework proposed in this paper then adopts a longitudinal demographic lens to offer a more dynamic understanding of how mobility changes and trends might be understood at different socio-spatial scales of investigation.

The framework assists in unpacking the lifestyle changes of different birth cohorts, and could help with employing cohort analysis in transport studies. The framework is an assessment of different processes at work influencing mobility. Whilst a time span of over 55 years is provided, it can be used at different temporal scales, depending on the timescale of the investigation. The framework also coincides with the beginning of the U.K. National Travel Survey, a repeated cross-sectional household survey conducted by the U.K. government, which collects information on travel patterns, and was first conducted in 1965–1966. It ran periodically though the 1970s and 1980s and has been running continuously since 1988. The framework then is suitable for exploring investigations covering this long timescale. For example, recent decline in mobility has predominately found to be explained by the fall in mobility of younger people. However, older people of the Boomer generation are becoming more mobile, yet assumptions about older people tend to be derived from pre-Boomer cohorts. There would appear to be different effects for cohort groups, which the framework can assist in identifying.

A dynamic framework for understanding the multi-level forces stimulating mobility change for birth cohorts

 Figure 1, adapted from Fielding (1993, 2010), outlines the wider multi-level forces that may influence changes in mobility patterns over time. It identifies three different layers of effects, at varying levels, over the past 55 years that are contributing to changing mobility trends.

Multi-level forces and differential effects affecting birth cohorts that stimulate mobility change^*

All authors

Sara Tilley

https://doi.org/10.1080/01441647.2016.1246487

Published online:

26 October 2016

Figure 1. A dynamic schema of the multi-level forces stimulating mobility change.

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The processes occurring in each layer can be summarised as follows:

1. Period effects (this encapsulated the top layer of macroeconomic processes, which tend to be short term in nature);

2. Mid-structural effects (this is the middle layer which summarises structural changes operating at a moderate rate of change) and

3. Deep structure effects (this is the bottom layer of cultural changes occurring at an almost imperceptible rate of change).

The simultaneous operation of these three layers has varied impacts on the mobility of birth cohorts as they age, which in turn influence mobility outcomes at the population level. Examining how these three layers interact with each other can assist in understanding how mobility changes over time.

It should be remembered that the framework attempts to encompass the complexity of interrelated contextual factors involved in changing mobility trends. The defining feature of the three levels is the temporal scale over which they operate. The layers have been ordered according to timescale, with the faster processes at the top of the model, with slower processes towards the bottom. Time is important here to illustrate the changing nature of contexts to be considered in line with mobility trends. Also important to note is that some levels may operate over different spatial scales.

Cohort trajectories: mobility of the Boomer cohort

This section considers the interactions between period and structural effects and the resulting influence on mobility for the Boomer cohort. In the framework, transmission occurs between the three layers identified in Figure 1 at a point in time. These cyclical effects can lead to inertia on the mobility of birth cohorts and become structural. These may have positive or negative repercussions on the mobility of particular birth cohorts as they age. These are illustrative of cohort effects.

Over the coming decades as the Boomer cohort ages, it is large in terms of its number of members. With improved health and medical care, subsequent cohorts are likely to remain active for longer, thus increasing life expectancies (Christensen, Doblhammer, Rau, & Vaupel, 2009; Tinker, 2002; UN, 2010). A combination of healthier, much older people will lead to a greater proportion of older people in the population, who will also likely be able to continue driving for much longer.

However, particularly in policy, older people are assumed to have lower mobility and require assistance, such as the concessionary travel pass which removes the cost of public transport. The changing mobility trends of the Boomers could be interpreted more widely to consider other factors, rather than just increasing driving licencing and underpinning this with wider considerations. Table 1 presents a summary of the different multi-level forces affecting the mobility of the Boomer cohort (b. 1945–1964).

Future implications for mobility and conclusions

Dominant frameworks to explain mobility change tend to focus on singular explanations, rather than a consideration of a wide combination of interlinked factors. Population projections and characteristics are useful for understanding how and why cohort groups travel and the changes that occur over time. As cohorts age, their characteristics, needs, desires and expectations will evolve over time. As such, when they reach a particular age, their mobility patterns are likely to differ from earlier cohorts and the assumptions about particular age groups may no longer hold.

The framework put forward in this paper adopts a multi-level approach that can assist in identifying the contexts that birth cohorts experience at particular points in time, and therefore assist in the interpretation of changing mobility trends. Not only can the framework help identify what is occurring at a current time point, it can also determine what has happened in the past to contribute to particular mobility outcomes.

Recently, there has been growing interest in the travel behaviour of the millennial generation (also known as Generation Y, generally considered to be born from around the 1980s onwards) in comparison to those of the preceding generations. In relation to the “peak car” debate, many have observed the notable reduction of younger people, particularly younger men, obtaining a driving licence across different countries (Delbosc & Currie, 2013; Kuhnimhof, Armoogum, et al., 2012). Exploring this issue in relation to time use, Garikapati, Pendyala, Morris, Mokhtarian, and McDonald (2016) find that there are differences between younger and older millennials, with younger ones spending significantly more time in the home. By contrast, older millennials have similar activity-time use patterns to their prior generation counterparts as they age, with the exception of time spent as a car driver. Studies have also found that millennials tend to have lower rates of car ownership, make fewer trips as well as travel fewer miles (McDonald, 2015; Polzin, Chu, & Godfrey, 2014). Tilley and Houston (2016) find that the mobility of younger men has fallen so that it is lower than that of women of the same age and generation.

Although there has been a decline in car use and mobility amongst younger people, which has also been associated with the “peak car” phenomenon, there are questions to whether current generations of younger people will continue to forego car use or whether they are simply delaying traditional life stages of previous generations (e.g. marriage, childbirth, etc.) and will eventually acquire a car-dependent lifestyle (Brown, Blumenberg, Taylor, Ralph, & Turley Voulgaris, 2016; Delbosc, 2016).

Some studies suggest that millennials are delaying the inevitable life stages (Garikapati et al., 2016) due to lag economic effects and higher levels of car ownership amongst the oldest of the millennial cohort (Klein & Smart, 2017). Increases have also been observed in car trips rendering calls for “planners to temper their enthusiasm about ‘peak car’” (Newbold & Scott, 2017), suggesting that millennials will eventually adopt the car as part of their everyday mobility as seen with previous generations. The framework put forward in this paper then can add to this debate and predications about the future mobility of the millennial cohort/Generation Y by drawing on a wider combination of factors.

The most recent recession, whilst having had varying impacts on the whole population, has almost certainly had greater adverse effects on current cohorts of younger people. Youth unemployment has increased dramatically, not only in Britain but in other EU countries as younger people leaving education have been unable to gain entry to the labour market. The lasting impact of unemployment has raised concerns about these cohorts being a “lost generation” (Eurofound, 2012); perhaps similar to the “silent generation” who grew up during the Great Depression (Strauss & Howe, 1991 in Lehto, Jang, Achana, & O’leary, 2008).

Therefore, there may be potentially damaging ramifications on the mobility of these current younger cohorts when they are older as a result of being unable to drive. In a car-centric society, being unable to drive also affects the ability to access employment opportunities. These cohorts may be more reliant on public transport services, but due to reductions in bus subsidies there has been a contraction in services in some areas, further reducing mobility options for this cohort (in addition to current older people who may be reliant on these services). The lack of adequate resources for mobility may lead to issues of social exclusion (Lucas, 2012).

However, other structural forces may lead to younger cohorts compensating for or offsetting lower mobility. During the lifetime of younger cohorts, there have been rapid developments in electronic and telecommunications technology (Foot & Stoffman, 2000; McIntosh-Elkins, Mcritchie, & Scoones, 2007). These younger cohorts differ from current and former cohorts and have been termed “digital natives” (Lyons, 2015; Van Wee, 2015) as they have grown up alongside these developments and are familiar with ICT applications and the internet, much like the Boomer cohort grew up with developments in the car (Coughlin, 2009). There is some evidence to suggest that younger cohorts value technology, particularly the smartphone and good internet access, as opposed to car ownership (Weissman, 2012).

The use of such technology amongst younger cohorts may be an alternative to travel, or desire to use it whilst travelling as it produces more “valued” travelling time (Jain & Lyons, 2008; Lyons, Jain, & Holley, 2007; Lyons & Urry, 2005). Smartphones in particular have been argued to provide freedom in terms of accessing information, family, friends and other contacts via social media (Lyons, 2015; McDonald, 2015). The development of smartphone apps and availability of real-time travel information including via Twitter can encourage change to behaviour and provide different opportunities for travel. Smartphones also provide different opportunities for travel including ridesharing services such as Uber and Lyft, which are more commonly used amongst younger people (McDonald, 2015).

As this technology has become more important amongst younger people, it may override the desire for higher mobility sought by former cohorts, thus producing a different culture of mobility and changing the meaning of mobility and space for these cohorts. Mobility decisions may be integrated more fully into residential migration decisions, particularly if these cohorts are unable to drive. Evidence of this may already be occurring, reflected in the changing desires for retirement locations towards city living where public transport services tend to be of a higher quality (McIlwain, 2012).

Demey et al. (2011) note that amongst younger people aged around 20–44, there has been a decrease in the proportion of those living with a partner and having dependent children, yet an increase in the proportion of those living with a partner and without children. Therefore, the suggestion that millennials are delaying life stages should be treated with caution. In addition, there may be larger shifts in technology, demography and socio-cultural characteristics of the millennials, which will not lead to the same levels of mobility seen amongst previous generations. Indeed, Delbosc (2016) argues that policy may be able to continue the lower use of cars by the current younger generation and prevent mobility based on the car.

The model adapted from Fielding (1993, 2010) demonstrates how effects are not cyclical and how they may become structural for the millennial cohort, particularly in the “peak car” debate. This framework then highlights gaps and ideas for not only the interpretation of results, but where further analysis is required. In relation to the “peak car” debate, there is a need to identify the issues for younger people more widely to consider attitudes for example, although this alone may not be sufficient and may need to be combined with other influences.

Whilst a full consideration of the combination of wider demographic and social changes that may be having great influence on mobility trends has been attempted to be captured within the framework, it may not be suitable for all contexts. The empirical analysis upon which this framework has been drawn was based on a British context, although it could be transferred to studies largely in the Global North. It also tends to operate and generalise at a national level and it should be recognised that there will be regional variations within this. However, the framework itself is designed to be fluid and therefore could be adapted to suit different situations.

Changing attitudes and lifestyles may be captured through surveys alongside travel behaviour; however, understanding about where attitudes and lifestyles have changed will come about through an exploration of the combination of factors to understand the cumulative effect. Each generation has had to cope with new forms of transport technology and negotiate the impact of new mobilities on society (Pooley et al., 2006). Mobility cultures are learnt from being part of a particular cohort that has implications for mobility now and in the future. By understanding past and current influences on cohort groups, better predictions about future and therefore needs over longer time horizons can be made and planned for accordingly.