Richard Grimal

ARE THE MILLENIALS LESS CAR-ORIENTED ? LITERATURE REVIEW AND EMPIRICAL FINDINGS

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, giving birth to the « peak car » hypothesis.

Some of these research works now also refer to « generation Y », tacitly suggesting that these behaviors could become durable by reflecting a deeper conversion of mental habits and attitudes away from the car, rather than being the simple effect of new living conditions, possibly aggravated by greater economic precariousness. In the last case, indeed, the apparent decline of carbased mobility in generation Y could simply be a temporary delay, that will resorb in the next step of the lifecourse, along with greater travel needs and a higher income for instance. This discussion can be nourished through a review of the different explanations that were given to this phenomenon: while these are many and usually don't allow bringing out a consensus about the main drivers of decreasing car-based mobility among the millenials, authors usually agree that it has to be the outcome of an interaction between a diversity of causes (Delbosc and Currie, 2013).

However, not all explanations are of equal importance, as some of these can be better supported by empirical evidence. The most convincing explanation might be the emergence of new living conditions, given a longer period of

INTRODUCTION

Generation effects play a key role in shaping long-term trends in travel behaviors, synthetizing a large number of factors operating gradually through generational renewal rather than reaching the whole population at a time. Up to cohorts born in the 70's, the process of generational renewal has made travel behaviors increasingly car-oriented, in relation with higher employment rates, the development of suburban areas, an increasing average income and decreasing vehicle costs. However, empirical findings from recent studies suggest a reversal of this trend within the so-called « generation Y »sometimes also referred to as « the millenials » -with decreasing licensure, car equipment and car use. These studies also often refer to "young adults", without necessarily telling whether these so-called new behaviors are caused by a delayed access to car-based mobility, due to new living conditions and economic difficulties, or demonstrate a deeper shift in lifestyles, mental habits and attitudes away from the car. Obviously, this discussion is not without implications to the future of the car and long-term trends in travel demand, as new generations will be majority tomorrow. In this study, we bring a contribution to this debate through a literature review followed by empirical findings, using the French National Unified Base of Local Household Travel Surveys. Given its composition, this database is essentially limited to urban households. Through age-cohort analysis and logistic regression, we try to determine the respective contributions of new living conditions and attitudes to travel behaviors of the millenials. In particular, we try to answer the following research questions:

• Are travel behaviors of young urban millenials different from those of preceding cohorts ? • Which is the contribution of living conditions to new travel behaviors ?

• Are travel-related opinions of the millenials significantly different from their elders, and do they play a role in explaining travel behaviors ?

Section 2 is dedicated to the literature review about travel behaviors of the millenials. In section 3, we present the main characteristics of the Unified Base of Household Travel Surveys, along with the methodology of analysis, while results are displayed in section 4. Finally, we conclude in section 5, by reminding prominent results before drawing out future research tracks and potential implications.

transition towards adulthood, being notably manifested through longer studies, a delayed entrance into the job market, and later family foundation. In parallel, these different stages have become less automatic and more uncertain, in relation with greater economic and relational precariousness [START_REF] Billari | Towards a new pattern of transition to adulthood ?[END_REF]. These factors would long tend to maintain less car-dependent lifestyles, given the absence of strong professional and family constraints, thus delaying the need to drive a car. Either for Metz (2013) or [START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF], the general delay in the lifecourse appears to be the most important explanation for the reduction of car use among young adults. Being a student or having a part-time job also decreases the probability of holding a license [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF]Kuhnimof et al., 2012a[START_REF] Kuhnimof | Who are the drivers of peak car ? A decomposition of Recent Car Travel Trends for six industrialized countries[END_REF][START_REF] Kalinowska | Tight Budgets or Environmental Awareness ? The Changing Travel Behaviour of Young Germans[END_REF][START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF], so that longer studies represent a key factor of decreasing car use, by postponing the entrance into the job market. As was shown by [START_REF] Taylor | Young, underemployed and optimistic: coming of age, slowly, in a tough economy[END_REF], an increasing proportion of young adults is studying, while in parallel, the number of young professionals has considerably decreased. In addition, as family foundation and young children are proven to be additional cases for passing driving license exams [START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF], the longer period preceding family foundation and motherhood [START_REF] Mitchell | The boomerang age from childhood to adulthood: Emergent trends and issues for aging families[END_REF]Metz, 2013) has become another reason to delay driving [START_REF] Oakil | [END_REF][START_REF] Hjortol | Decreasing popularity of the car ? Changes in driving license and access to a car among young adults over a 25 year period in Norway[END_REF]. However on the other hand, an increasing number of young adults are remaining at their parental home [START_REF] Cobb-Clark | Leaving home: what economics has to say about the living arrangements of young Australians[END_REF][START_REF] Mitchell | The boomerang age from childhood to adulthood: Emergent trends and issues for aging families[END_REF], which gives them better opportunities to access to a car through their parent's vehicles, the economy of a rent also making additional revenues available to support vehicle costs [START_REF] Licaj | Individual and contextual socioeconomic disadvantages and car driving between 16 and 24 years of age : A mul level study in the Rhone Département (France)[END_REF].

Another explanation resides in the increasing economic difficulties encountered by the youth, through lower financial resources, higher unemployment [START_REF] Berg | Understanding subgroups of novice drivers: a basis for increased safety and health[END_REF][START_REF] Davis | Transportation and the New Generation: why young people are driving less and what it means for transportation policy[END_REF][START_REF] Van Den Waard | New Drivers in Mobility: What moves the Dutch in 2012 and beyond ?[END_REF] and greater job precariousness, which may have aggravated the effects of general economic background -recession, increasing fuel prices -in this group (Kuhnimof et al., 2012a[START_REF] Kuhnimof | Who are the drivers of peak car ? A decomposition of Recent Car Travel Trends for six industrialized countries[END_REF][START_REF] Kalinowska | Tight Budgets or Environmental Awareness ? The Changing Travel Behaviour of Young Germans[END_REF], thus hindering the access to carbased mobility with respect to its costs, either associated with driving license training [START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF][START_REF] Licaj | Individual and contextual socioeconomic disadvantages and car driving between 16 and 24 years of age : A mul level study in the Rhone Département (France)[END_REF], car ownership [START_REF] Klein | Millenials and car ownership : less money, fewer cars[END_REF] or car use [START_REF] Van Den Waard | New Drivers in Mobility: What moves the Dutch in 2012 and beyond ?[END_REF]. By the way, economical reasons are frequently mentioned by young adults to explain their (at least) temporary renouncement to passing their driving license exams and owning a vehicle [START_REF] Berg | Understanding subgroups of novice drivers: a basis for increased safety and health[END_REF][START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Williams | Teenagers' licensing decisions and their views of licensing policies : A national survey[END_REF]. Beyond the cost of driving license preparation, several authors also mention their increasing difficulty, following reforms oriented by road safety goals [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Senserrick | Australian graduated driver licensing systems[END_REF][START_REF] Masten | Graduated Driver Licensing and Fatal Crashes Involving 16-to 19-Year-Old Drivers[END_REF][START_REF] Raimond | Why are young people driving less ? Trends in license-holding and travel behavior[END_REF]. However, the influence of economic drivers is not always quite clear, as the decline of car use among young adults has sometimes preceded the economic crisis [START_REF] Berg | Understanding subgroups of novice drivers: a basis for increased safety and health[END_REF][START_REF] Davis | Transportation and the New Generation: why young people are driving less and what it means for transportation policy[END_REF], and may also affect higher-income groups which are not yet limited by financial resources [START_REF] Davis | Transportation and the New Generation: why young people are driving less and what it means for transportation policy[END_REF][START_REF] Stokes | Has car use per person peaked ? Age, gender and car use, Presentation to Transport Statistics Users group[END_REF]. In addition, the assessment of resources in the case of young adults is complicated by financial transfers coming from the family [START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF], though parents may also be affected by increasing economic precarity. Nevertheless, it is likely that greater economic precariousness along with the reinforcement of some other budgetary items such as housing costs [START_REF] Rougerie | Prix des logements anciens -Dans les années 2000, ils ont augmenté beaucoup plus vite que les loyers et les revenus[END_REF], may have generally contributed to reduce the available income to support transport-related costs among young adults, thus encouraging them to more severely controlling their budget.

The third explanation consists in joint evolutions of transport supply, residential choices and work/studying places of young adults, in particular, the increasing concentration of young adults in dense urban areas of the largest cities [START_REF] Hjortol | Decreasing popularity of the car ? Changes in driving license and access to a car among young adults over a 25 year period in Norway[END_REF][START_REF] Oakil | [END_REF], characterized by better transport supply, the close proximity of university clusters and highly qualified jobs, functional mixity and lower distances between activities (Lachmann and Brett, 2011 ;[START_REF] Davis | Transportation and the New Generation: why young people are driving less and what it means for transportation policy[END_REF][START_REF] Van Den Waard | New Drivers in Mobility: What moves the Dutch in 2012 and beyond ?[END_REF]). Yet, young adults living in dense urban areas are less licensed than those dwelling in suburban and rural areas [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Raimond | Why are young people driving less ? Trends in license-holding and travel behavior[END_REF][START_REF] Licaj | Individual and contextual socioeconomic disadvantages and car driving between 16 and 24 years of age : A mul level study in the Rhone Département (France)[END_REF][START_REF] Hjortol | Decreasing popularity of the car ? Changes in driving license and access to a car among young adults over a 25 year period in Norway[END_REF][START_REF] Oakil | [END_REF]. By the way, the opportunity of ensuring their travel needs otherwise is often mentioned by young adults as an important reason for not driving [START_REF] Berg | Understanding subgroups of novice drivers: a basis for increased safety and health[END_REF][START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Williams | Teenagers' licensing decisions and their views of licensing policies : A national survey[END_REF]. An improved transport during the 90's and 2000's, also has probably contributed to promote new lifestyles and to change travel behaviors, especially as greater facilities were offered to the young, making transit very affordable [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF]Kuhnimof et al., 2012a[START_REF] Kuhnimof | Who are the drivers of peak car ? A decomposition of Recent Car Travel Trends for six industrialized countries[END_REF][START_REF] Van Den Waard | New Drivers in Mobility: What moves the Dutch in 2012 and beyond ?[END_REF].

In contrast with such explanations which are essentially based on new living conditions, other authors allude to a more profound transformation of mental habits and attitudes away from the car. Formerly a symbol of social status and independence [START_REF] Steg | Car use: Lust and must. Instrumental, symbolic and affective motives for car use[END_REF], the car would have lost part of its attractiveness in generation Y, being dethroned by more up-to-date items coming from the digital world, such as mobile phones and social networks (Cisco, 2011). For a part, this discourse is supported by empirical findings, in particular the analysis of different surveys, which tend to confirm that the car would be less associated with an image of social status, luxury and prestige [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Cornut | Le Peak car en Ile-de-France : Etude de l'évolution de la place de l'automobile et de ses déterminants chez les franciliens depuis les années 1970[END_REF][START_REF] Delbosc | Using discussion forums to explore attitudes towards cars and licensing among young australians[END_REF]Vincent-Geslin et al., 2017). However, it remains associated with notions of freedom, independence and autonomy [START_REF] Steg | Car use: Lust and must. Instrumental, symbolic and affective motives for car use[END_REF][START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Cornut | Le Peak car en Ile-de-France : Etude de l'évolution de la place de l'automobile et de ses déterminants chez les franciliens depuis les années 1970[END_REF] as well as with adulthood and maturity [START_REF] Delbosc | Using discussion forums to explore attitudes towards cars and licensing among young australians[END_REF]. Attitudes towards the car would then tend to become more opportunistic, with the car becoming a simple transport mean among others, and its benefits and shortcomings being more systematically compared with alternatives (Vincent-Geslin et al., 2017). However, other studies find that aspirations to drive a car remain strong (Kim, 2014 ;[START_REF] Demoli | Survey of Public Attitudes to Quality of Life and to the Environment[END_REF] and are still being associated with an image of social prestige [START_REF] Steg | Car use: Lust and must. Instrumental, symbolic and affective motives for car use[END_REF][START_REF] Van Den Waard | New Drivers in Mobility: What moves the Dutch in 2012 and beyond ?[END_REF], while their realization is hindered out by unfavorable economic conditions.

In parallel, some authors suggest that new mobility patterns could be partially driven by greater environmental awareness [START_REF] Kalinowska | Tight Budgets or Environmental Awareness ? The Changing Travel Behaviour of Young Germans[END_REF][START_REF] Hopkins | Car environmental awareness explain declining preference for car-based mobility amongst generation Y ? A qualitative examination of learn to drive behaviors[END_REF]. However, there are few empirical findings to support this statement, and other studies a contrario demonstrate that new generations are not necessarily more sensitive to environmental concerns, also contesting the validity of the relation between environmental awareness and travel behaviors (Department for Environmental Food and Rural Affairs, 2002 ;[START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Cornut | Le Peak car en Ile-de-France : Etude de l'évolution de la place de l'automobile et de ses déterminants chez les franciliens depuis les années 1970[END_REF].

In the end, there is limited evidence for a significant shift of mental habits away from the car, apart from a tendency to being more opportunistic, which may contribute to explain that young adults delay driving as long as they don't really need it. However, it might also be a consequence of new living conditions and the economy, rather than a real motive for changing behaviors.

To conclude this panorama, information and communication technologies are often considered as a potential motive for changing travel habits. Indeed, though the diffusion of digital innovations was rather general, the millenials have been usually presented as more fond of them. Yet, ICT would favour transit use by increasing the primary utility of travel through allowing young adults to stay connected in order to perform different on-line activities and get real-time information about travel conditions [START_REF] Davis | Transportation and the New Generation: why young people are driving less and what it means for transportation policy[END_REF]. In addition, newspaper articles and some research work have suggested that ICT would allow reducing travel needs by substituting virtual networks to direct physical interaction (Choo et al., 2005 ;[START_REF] Williams | Teenagers' licensing decisions and their views of licensing policies : A national survey[END_REF][START_REF] Rentziou | VMT, Energy Consumption, and GHG Emissions Forecasting for Passenger Transportation[END_REF][START_REF] Sivak | Recent changes in the age composition of drivers in 15 countries[END_REF]. However, this issue remains partly speculative because of the lack of a clear understanding of the relationship between ICT and travel. Several authors have also pointed out the weakness of these results [START_REF] Sivak | Recent changes in the age composition of drivers in 15 countries[END_REF][START_REF] Vine | A tenuous result : Re-analysis of the link between internet-usage and young adults driving-licence holding. Comments on 'recent changes in the age composition of drivers in 15 countries[END_REF], given the complexity of the interdependencies between ICT and travel [START_REF] Mokhtarian | Telecommunications and travel. The case for complementarity[END_REF][START_REF] Andreev | Review: state of teleactivities[END_REF][START_REF] Boyd | Social network sites: Definition, history, and scholarship[END_REF], which may also include phenomena of complementarity -as ICT may help maintaining some relationships [START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF]) -or induction, as an increasing information about opportunities may generate additional travel (Van den [START_REF] Van Den Berg | Size and composition of ego-centred social networks and their effect on geographic distance and contact frequency[END_REF].

All these explanations may finally be reduced to the subject of determining whether travel behaviors of the millenials illustrate a structural shift in mental habits and lifestyles, or simply result from a general delay in the transition process towards adulthood, to be resorbed in a later stage of the lifecourse. This issue is obviously critical to forecasting future travel demand, as new generations will be majority tomorrow [START_REF] Polzin | The impact of millenials' travel behavior on future personal vehicle travel[END_REF]. While under the second assumption, long-term consequences on transport demand are likely to be limited, they could be of greater importance in the first case, as new behaviors would then tend to be durable. In favor of this last statement, some studies show that habits acquired during the youth tend to persist later on [START_REF] Bodier | La voiture : une habitude qui se prend jeune[END_REF][START_REF] Lyons | A case study of the development of car dependence in teenagers[END_REF], and that the decreasing access to driving license tends to persist at a later stage of the lifecourse in generation Y [START_REF] Noble | Why are some young people choosing not to drive ?[END_REF][START_REF] Raimond | Why are young people driving less ? Trends in license-holding and travel behavior[END_REF][START_REF] Stokes | Has car use per person peaked ? Age, gender and car use, Presentation to Transport Statistics Users group[END_REF][START_REF] Delbosc | Causes of youth licensing decline: a synthesis of evidence[END_REF]. However, other research works demonstrate that generation Y is simply postponing access to the car until the moment where they really need it (Kim, 2014 ;[START_REF] Garikapati | Activity patterns, time use, and travel of millennials : a generation in transition ?[END_REF]. In addition, we already saw that there was little proof of changing attitudes in generation Y, apart from a tendency to rationalization [START_REF] Cornut | Le Peak car en Ile-de-France : Etude de l'évolution de la place de l'automobile et de ses déterminants chez les franciliens depuis les années 1970[END_REF][START_REF] Demoli | Survey of Public Attitudes to Quality of Life and to the Environment[END_REF]Vincent-Geslin et al., 2017), suggesting that the decline of car use is rather the manifestation of a frustrated desire than the consequence of a personal choice [START_REF] Licaj | Individual and contextual socioeconomic disadvantages and car driving between 16 and 24 years of age : A mul level study in the Rhone Département (France)[END_REF][START_REF] Demoli | Survey of Public Attitudes to Quality of Life and to the Environment[END_REF].

DATA AND METHODOLOGY

The French Unified Base of Local Household Travel Surveys

The analysis relies on the French Unified Base of Local Household Travel Surveys, compiling many surveys realized from 2009 over the French territory, all according to a standardized method designed as CEREMA-Certified Household Surveys (EMC2). Apart from the advantage of methodological unity, the sample is also of important size, which is a factor of greater precision, thus authorizing the study of restricted socio-economic groups and niche markets. More precisely, the database contains three survey types: Household Travel Surveys (EMD), designed for great cities and using a face-to-face protocol, A journey is the use of any motorized mode, for instance transit or the car. A trip may therefore be made up of several journeys, for instance a journey by car to go to the train station, followed by a journey by train to go to work. A loop is made up of all trips between two trips back home. Finally, the opinion component includes answers to a series of questions about general and transport-related issues. The base contains about 210 000 households, 470 000 individuals, 520 000 loops, 1 344 000 trips and 1 051 000 journeys.

Methodology of analysis

The first part of the study will consist in the age-cohort analysis of different sets of indicators, respectively focusing on car equipment and travel behaviors, living conditions and opinions. Indicators of travel behaviors include : a) the proportion of driving license holders ; b) the proportion of individuals holding a personal car for their home-to-work/studying trips ; c) the proportion of daily car users ; d) the proportion of daily or regular transit users -at least twice a week ; e) the average number of daily trips by car as a driver ; f) the average number of daily trips by car as a passenger ; g) the average number of daily trips by transit ; h) the average number of daily trips by walking and cycling. Indicators for living conditions include : a) the employment rate ; b) the proportion of students ; c) the proportion of singles ; d) the proportion of individuals living in households with children ; e) the proportion of central city dwellers ; f) the proportion of unemployed ; g) the proportion of individual housing ; h) the proportion of owners.

Finally, we build up a set of indicators for opinion, starting from answers to the questionnaire, which includes a series of questions about : a) the importance of road safety, noise, pollution, transit, walking, cycling, parking and traffic issues ; b) whether people agree, disagree, or have no opinion, with respect to the following given statements : one should keep on builiding car parks ; the bicycle in town is the future ; in town using a car is necessary ; restricting car use in town is hindering economic activity ; one should keep on developing transit, even if it means disturbing car drivers ; to improve traffic, car use should be restricted in town ; illegal parking should be more severely punished ; c) the choice of adjectives to qualify travel modes -car, transit and cycling -from a series of 44 which may be either positive -for instance quick, cheap, practical, ecological, safe, comfortable -or negative -slow, expensive, unpractical, polluting, dangerous, uncomfortable. The corresponding information is gathered through a restricted set of indicators built up of score variables, respectively for : a) the importance of alternatives to the car ; b) the importance of car externalities (noise, pollution…) ; c) the importance of traffic efficiency (parking and congestion issues) ; d) the score of statements favourable to the car ; e) the score of statements hostile to the car ; f) the score of every modecar, transit, cycling -based on the adjectives used to design them. Score variables are then constructed the following way :

• The importance of alternatives was augmented from one point every time a given alternative was considered important. For instance, if an individual judged as important transit, walking and cycling, he was attributed three points. If an alternative was regarded as unimportant, the importance variable was not augmented. If no opinion was expressed, the importance variable was augmented from 0.5. The same method was applied for the importance of car externalities and traffic efficiency ; • The score variable for pro-car statements was augmented from one point every time an individual agreed with them, and diminished from one point every time he disagreed. The score is unchanged by neutral opinions.

The same method was applied to statements hostile to the car ; • The general score of a mode based on adjectives to design it is finally calculated by attributing the value +1 to a positive adjective, -1 to a negative one, and zero when no opinion was given. However, different weights were attributed to adjectives depending on their rank, as the adjectives first quoted may contribute more to the general image of a given mode than those cited only in second or third rank. Therefore, the first quoted adjective was attributed a weight 1, the second 1/2, and the third 1/3.

However, not all individuals were asked about transport-related opinions, but only about 110 000, forcing us to restrict the sample size within the scope of our study, with respect to the initial sample. Cohorts were constructed using five-year birthdate intervals, resulting in eighteen cohorts, from 1908-15 to 1995-99. In the same way, age was split into five-year intervals, from 16-20 to more than 95, resulting in eighteen age groups. Given the low number of survey waves, only three periods of observation can be estimated for every cohort. The existence of generation effects can be deduced from the comparison of successive cohorts for the same age group, while the general profile of an indicator for successive cohorts gives an indication about age effects, apart from possible period effects which can be highlighted through the existence of simultaneous change across cohorts despite the fact behaviors are observed for different age groups.

The second part is dedicated to travel behavior modelling, out of variables for living conditions and opinions. In particular, we try to assess whether these variables can account for differences between cohorts and age groups.

Through logistic regression procedures, we model the three following indicators: personal car availability, daily driving and regular transit use. Model specification is classical and based on utility functions with a logistic distribution of error terms: * = + + , i=1….N, for binary logistic regression models.

In this notation, * stands for latent utility of personal car availability, daily driving or regular transit use, while is an intercept representing the absolute preference for the event, with respect to the reference case. represents the marginal increase in the utility of the event resulting from a unit increase in , where are exogenous variables. Model estimation is performed through maximum likelihood. Models are then used to simulate age cohort profiles for travel behavior indicators, which are then compared with observations. Average number of daily trips by active modes Age-cohort analysis shows that driving license rates have increased across generations born before 1950, before remaining stable thereafter. Access to driving license is also characterized by the existence of age effects with licensure increasing until 40, where it tends to level off at the constant level of 90 % across all cohorts, which then tend to merge into a single profile, indicating the lack of significant generation effects. Cohorts born after 1980 are no exception to this rule, only a slight decrease of licensure being noticed in cohort 1975-80. The access to driving license also tends to become slightly more gradual. Finally, differences in licensure between cohorts are slender, suggesting that access to driving license is still considered a necessity by the vast majority of young adults, included young urbans.

RESULTS

Age cohort analysis

Results are very different when it comes to personal car availability for hometo-work/study trips. First of all, the general lifecourse profile of personal car equipment displays increasing equipment until 45, followed by decline thereafter, the maximum rate being observed for cohort 1960-75, between 35 and 50. Apart from age effects, a clear generational effect is emerging, with constantly decreasing equipment rates from cohort 1965-70. This result implies, in particular, that this phenomenon isn't limited to the millenials, but has in fact began as soon as generation X. However, it tends to accelerate from cohort 1975-80 and even more from cohort 1985-90. Generation Y is therefore characterized by the acceleration of demotorization among young urbans.

By then considered indicators of car use, we see that the proportion of daily drivers is increasing until the age of 45, before falling down in the second part of the lifecourse. Though the proportion of daily car users among urban dwellers is generally quite homogeneous across cohorts, a decline can be noticed in generation Y, with an acceleration in cohorts born after 1985. By contrast, the proportion of regular transit users is decreasing until 45-50, slightly comes back to growth until 75, before falling down in the end. Apart from living conditionsresidential location of young adults, household composition, job status -the decline of transit use over the lifecourse could also express the modification of preferences resulting from cumulative learning effects -with the accustomance to higher levels of comfort through the car, for instance. Finally, there is an important generational effect which is specific to the millenials, with an acceleration in cohorts born after 1985.

Regarding indicators of daily trip frequency by transport mode, we see that the average number of trips by car as a driver increases until 45, before decreasing in the second part of the lifecourse. Strong generation effects are noticeable across cohorts born after 1975, with a constant decrease of the average number of daily trips by car as a driver from a cohort to another. As for personal car availability, this trend towards declining car use has thus began before generation Y, but this time with no acceleration in cohorts born after 1985, suggesting that the decrease in daily driving to work/study was compensated by an increasing mobility for other purposes. In parallel, the average number of transit trips has increased in cohorts born after 1980, with a strong acceleration in cohorts born after 1985. By contrast, all cohorts tend to merge into a single profile when it comes to the average trip freqency by car as a passenger and by active modes -walking and cycling -suggesting the lack of significant generation effects. Daily trip frequency by car as a passenger and by active modes tend to present similar lifecourse profiles, characterized by decrease until 45, recovery between 45 and 70, and decline in the end.

Finally, the most striking change in travel behaviors of young urban millenials is the massive shift from driving to transit, especially in cohorts born after 1985. However, despite an acceleration among the millenials, this process had already began in generation X. This transition goes along with trends towards demotorization, while practices of car as a passenger and active modes tend to remain stable. Yet, young urban millenials pass driving license exams as much as their elders, suggesting a rationalization of behaviors, where driving license is still perceived as an insurance for mobility, but driving is delayed as long as young adults do not really need it. Transit use was also probably reinforced due to a period effect, which could be related either to the delayed effects of the economic crisis or to greater traffic and parking difficulties. The shift from driving to transit among urban millenials could result from new living conditions, for instance related to longer studies, a delayed entrance into the job market, their increasing concentration in dense urban areas, and later family foundation. But it could also be the consequence of changing opinions and attitudes towards the car, for instance related to greater environmental awareness. In order to disentangle the respective contributions of these potential orders of causality to new travel behaviors, we now proceed to age-cohort analysis for the corresponding variable sets. After a steady growth in generations born before 1960 -in relation with the massive entrance of women into the worforce after the war -the employment rate decreased across all cohorts born after 1965. However, the most important decline occured in cohorts born after 1985, especially before 25, along with a strong growth in the population of students. In addition, the unemployment rate has constantly increased across all cohorts born after 1945, as well as over the period of observation, probably due to the effects of the economic crisis. While the rise of unemployment is a long-run structural trend, the increasing number of students was specific to cohorts born after 1985. As a result, the number of working adults has considerably reduced before 25 in these cohorts, in relation with longer studies and higher unemployment rates. In parallel, an increasing proportion of adults are living alone before 35 across all cohorts born after 1980, while the proportion of adults having children has strongly decreased across cohorts born after 1975. Regarding geographical and housing-related factors, the most striking evolution was the tremendous increase in the concentration of young adults living in central cities in cohorts born after 1975, which might be an essential reason for the increasing use of transit, given the dramatic gap in transit supply and transit accessibility between central cities and the remaining urban areas. The increasing concentration of young adults in central cities may largely contribute to explain demotorization by making car equipment unnecessary, unless residential choices of generation Y were self-induced by prior travel plans. Finally, while the proportion of individual housing has not changed much, the proportion of owners fell down, a result which is consistent with longer studies and delayed family foundation as these evolutions also lead to differ settlement plans.

A synthesis of evidence finally suggests that an important transformation of living conditions has contributed to generate a massive shift from driving to transit among urban millenials. The main factor seems to be the decreasing proportion of young professionals, associated with an increasing number of students before 25, as commuting was proved to be a decisive factor of car equipment plans. Longer studies have generalized among French millennials, following the near suppression of selection at university, resulting in a massive shift from work to studies before 25, especially in cohorts born after 1985. Besides, young adults are increasingly concentrated in central cities before 35, where transit supply is much more efficient and appropriate than for the remaining urban areas, while owning and using a car is made more and more difficult by the lack of available space, limited parking supply and increasing congestion. However, the increasing centrality of young adults may also be partially the result from residential self-selection of unlicensed and car-less adults, and from student life, as students often tend to privilege urban life and amenities. However, generational gaps in residential location tend to persist even later on in the lifecourse (35-40), though most adults have then entered the workforce, indicating that other factors come into play. The growing concentration of highly-qualified jobs in dense urban areas might be one of them. Finally, the different stages of family foundation are generally delayed.

The combination of these trends tends to corroborate the thesis of a general delay in the lifecourse, inducing a longer transition towards adulthood. When it comes to opinion variables, the importance given to alternatives to the car appears to be weakly dependent from age and generation, except in cohorts born before 1945, for which they are considered less important. In particular, no specificity of generation Y is noticeable. On the other hand, a period effect

0 0,5 1 1,5 2 2,5
Importance of alternatives to car is applying to all cohorts, with the importance of alternatives to the car increasing over time. It could result either from the increasing difficulties and unefficiency of urban transport -whether it be transit with increasing security issues, malfunctions and disruptions, or road with increasing congestion and parking difficulties -or from the increasing cost of car use, due to higher fuel price levels associated with a lower average income due to the economic crisis.

The perception of car externalities is also rather independent from age and generation. It is not more pronounced in generation Y, where it even seems to go slightly backwards. These results tend to confirm prior findings from the literature about the absence of specific environmental awareness in generation Y, in contradiction with what is often stated by newspaper articles. Only age effects remain, which are nonetheless limited. The awareness of car externalities is very homogeneous between 35 and 70 where it is maximal. The most credible explanation is that individuals in these age groups become more sensitive to these concerns as they are more exposed to them, notably in relation with daily

Age and generation have no more influence on the sensitivity to traffic efficiency: while cohort are unappreciable, age effects are limited to a lower sensitivity to this issue before 30, and after 75. Here again, the degree of awareness to a given issue seems to be directly related to the degree of exposure, suggesting that, beyond the possible influence of opinions on behaviors, there might also be a reverse direction of causality, where transportrelated opinions and attitudes are shaped by travel experience and habits. In addition, the sensitivity to this issue increased over the period of observation, suggesting that traffic and parking difficulties may have aggravated in relation with growing congestion, policies dissuasive to the car and the lack of parking supply.

The degree of agreement to pro-car statements doesn't show off significant generation effects, but reveals the existence of strong age effects. When ageing, people tend to manifest a higher degree of consentment to pro-car statements, with an acceleration after 65. However paradoxically, the same people also tend -at least until 75 -to agree more with anti-car assertions. However, the degree of consent to anti-car statements tends to increase more slowly than for pro-car assertions. These results may illustrate the combination of two phenomena: first, there might be a conformism bias, implying that with growing age, people would generally give more easily their consent to any given proposition; it could also mean that opinions are becoming less neutral as they are more strongly shaped by acquired experience, so that the elders would more easily identify the respective advantages and shortcomings of any mode. Second, despite a clearer perception of its shortcomings, trade-offs would nonetheless tend to favour more and more the car, given its benefits in terms of comfort, autonomy and efficiency, also in relation with decreasing physical capacities.

We finally come to the image of different modes, based on the choice of adjectives to design them. Again, neither significant cohort effects nor specificity of the millennials can be highlighted, while strong age effects emerge. On the whole, the image of the car is improving over the lifecourse, just like for transit. However, the image of transit has considerably degraded during the period of observation. This particular outcome may several causes among which increasing insecurity, malfunctions and network saturation in great cities. On the contrary, the image of the bicycle tends to decrease after 50, in relation with the decline of physical capacities and increasing safety concerns. However, the general image of transport modes is improving over the lifecourse, possibly exhausting the existence of an optimistic bias when ageing, related to accumulated assets and an improved social status.

Finally, opinions and attitudes towards different modes do not allow to identify significant generation effects, apart from the importance attributed to alternatives to the car which is lower in cohorts born before World War II. In particular, opinions of generation Y are similar to (those of) their elders. These results tend to confirm the lack of specific environmental awareness of the millennials, consistently with prior findings from the literature. On the contrary, age effects are found and, to a lesser extent, period effects. First of all, individuals are more sensitive to car-related issues such as car efficiency and car externalities in the middle of the lifecourse, when they are more exposed to them in relation with their daily travel experience. In addition, perceptions of transport modes would tend to become more precise over the lifecourse, in relation with accumulated travel experience. Age effects may also reflect different biases either of conformism or optimism. However, opinions would generally tend to become more and more favourable to the car over the lifecourse. Finally, the image of transit has degraded over the period, a result which could reflect the loss of attractivity and efficiency of this mode.

In the absence of significantly different opinions in generation Y, changing travel behaviors seem to be principally caused by new living conditions, related to a longer and more uncertain transition path towards adulthood, resulting from an interaction between longer studies, a delayed entrance into the workforce, the increasing spatial concentration of young adults in dense urban areas and finally, later settlement and family foundation. In order to look more-in-depth into this issue, we now come to the modelling part, where we estimate travel behavior models with exogenous variables containing both variables for living conditions and opinions. Models are then used to simulate travel indicators, which in turns allows us to estimate the accuracy of their predictions. Regarding personal car availability, age and generation effects happen to be non-significant, while variables for living conditions and opinions are significant at the 5 % level. In particular, there is no residual effect for generation Y when controlling for living conditions and opinions. The most influent variable is the job status, with work strongly increasing the probability of holding a personal car. Then come geographical and housing-related variables, with living in a central city reducing the probability of holding a car, and occupying a house, being an owner or commuting greater distances increasing it. By contrast, household-related variables -living with a partner, having children -have rather limited influence on personal car equipment. Among attidudinal variables, the most influent is the score of adjectives to design the car, followed by the importance of traffic efficiency, which both tend to be correlated with higher car availability. Conversely, the image of transit and the importance attributed to alternatives are negatively correlated with personal car equipment. By contrast, the degree of consent to pro-and anti-car statements, as well as the importance of car externalities, are much less influent. In general, despite significant effects, attitudinal variables tend to be far less influent than living conditions in explaining personal car equipment. As a result, these look sufficient to explain gaps between cohorts, in particular the strong decline of personal car equipment in generation Y.

Modelling results

Personal car availability

When it comes to daily driving, age and cohort effects remain significant, even when controlling for living conditions and attitudinal variables, implying that some unobserved factors of variability play a role in explaining differences according to age and generation. Ceteris paribus, the probability of daily driving tends to rise across cohorts until reaching a maximum in cohort 1950-55, then constantly decreases across later cohorts, implying that some unobserved factors contribute to decreasing car use in post-war generations. Everything else being equal, maximum driving occurs among young adults between 20 and 30, while it is decreasing thereafter. This result puts into perspective the apparent preference of young adults for transit, which seems to be in fact principally related to living conditions -lack of professional activity, concentration in central cities, single life -rather than the result of a lower appetite for car use as, everything else being equal, young adults would even tend to drive more than the average. Here again, among living conditions the most influent variable is the employment status, with employment increasing the probability of driving, followed by geographical and housing-related variables, with living in a central city reducing the probability of driving, while living in a house, being an owner and travelling greater distances to work are increasing it. The hierarchy of attitudinal variables is more or less the same than for personal car equipment, with the score of adjectives to design the car being the most influent followed by the importance of traffic efficiency (positive effect), of alternatives to the car -negatively correlated with daily driving -and the score of adjectives to design transit -with a negative correlation to driving. Other attitudinal variables -importance of car externalities, scores of pro-and anti-car statements -are less influent.

Finally, we come to the model for daily and regular transit use. This time generation effects are not significant, while age effects are. Ceteris paribus, the probability of regularly using transit decreases until the age of 40-50, then slightly increases until the age of 60, falls down again between 60 and 70, rises up a little before 70 and 75, and decreases constantly thereafter. The general trend is nonetheless of decreasing transit use over the lifecourse. As previously stated, this result could be the expression of learned experience, with an increasing accustomance to the benefits of the car in terms of comfort, convenience and autonomy, in relation with increasing safety concerns and diminishing physical capacities. Apart from ageing effects, several variables related to living conditions tend to be equally important in explaining regular transit use, including: living in a central city, increasing the probability of using transit; being a worker, having the reverse effect; living in a house, also reducing the probability of using transit. Other variables related to living conditions, though less influent, are not negligible, among which family-related variables -living with a partner, having children -and being an owner, which also tends to reduce the probability of using transit. Among opinion variables, several are of equal importance, including the score of adjectives to design transit, the importance of alternatives to the car -positively influencing transit frequentation -and the score of adjectives to design the car, having the reverse effect. Other attitudinal variables are of almost equal importance, as the importance of traffic efficiency and the image of the bicycle, both negatively affecting transit use. Finally, respective scores of pro-and anti-car statements, as well as environmental concerns, are of little effect on transit use. In general, just like for driving, the influence of opinion variables tends to be of lesser importance than living conditions in arousing the choice to use transit.

As a result, model estimation generally tends to confirm the prevailing influence of living conditions on car equipment and mode choice for daily travel, while opinions, though significant, are of lesser importance. Among variables related to living conditions, the most influent is job status, with working generally being favorable to car use, followed by geographical and housing-related factors, while variables related to household composition have little effect, apart from the choice of using transit which is negatively correlated with living in a family. Among attitudinal variables, the most significant effects are constituted by the scores of adjectives to design different modes, the imporance of traffic efficiency, and the importance of alternatives to the car. On the contrary, scores of pro-and anti-car statements, as well as environmental concerns, are weakly influent. This last result tends to confirm previous findings from the literature showing that environmental awareness plays little role in explaining travel behaviors of the millenials. Regarding the importance of attitudinal variables, the major concern remains about the direction of causality between opinions and travel behaviors. Though attitudes may influence behaviors, the reverse proposal may be true in relation with accumulated learning experience, leading either to arouse greater awareness of travel-related issues or to change preferences: for instance, the search for travel efficiency may be a motive for using the car, but in turns, exposure to congestion may raise awareness of transport efficiency issues. In addition, opinions may also respond to a(n unsaid) motive of self-justification, where these are consciously or unconsciously reshaped in order to (ex-post) justify previous choices regarding the way of life which may in fact be the outcome of external constraints : for instance, stated preferences for transit may serve to self-justify a choice by default in the absence of driving license or of sufficient revenues to buy a car.

Coming to age and cohort effects, controlling for living conditions considerably reduces their apparent influence. In particular, they have no remaining significant effect on car equipment decisions, suggesting that living conditions alone are enough to explain age-and cohort-related differences. In addition, generation effects disappear from the model for transit use, which is principally determined by living conditions and age effects. As a result, cohort effects only remain significant in explaining decisions of daily driving. However, one may ask again whether these residual effects might not themselves be explained by unobserved variables which were not included yet into the model, in particular related to economic conditions. We finally simulate indicators of travel behavior using the prior logistic models. We compare the accuracy of predictions according to different models, including either variables related to living conditions, opinions or age and cohort effects, which in turns allow us to identify a « sufficient » model, i.e a model which is sufficient to give good predictions of travel behavior indicators by age and cohort, while adding supplementary variables provides little benefit. For every indicator, the model based on living conditions alone is compared with the « sufficient » model, except in the case of personal car availability, where living conditions alone already allow to give a very good description of personal car availability by age and cohort, while attidudinal variables give little extra benefit. In this case, the model based on living conditions alone is the sufficient model, so that only one outcome was displayed. In particular, the model succeeds in forecasting the decrease of personal car equipment in cohorts born after 1975, with an acceleration in cohorts born after 1985. It seems therefore that the decrease of car equipment can be essentially explained the greater number of students and decreasing employment rate, the growing concentration of young adults in central cities, and delayed family foundation. However, it fails in explaining the persistence of low car after 25 in cohorts born after 1985, even though millenials have then entranced the labor force. Consequently, other factors might be at stake, possibly the persistence of difficult economic conditions -job precariousness, low housing costs -in the early career, along with increasing traffic and parking difficulties. More or less the same results are found for the diffusion of daily driving though in this case, the model based on living conditions alone is not the sufficient model which also requires to include cohort effects. In particular, cohort effects better account for the persistence of low car availability even though millenials have entered the job market. Finally coming to the last indicator about transit use, living conditions alone only account for half of the difference between age groups, which is better predicted only through the introduction of age effects. However this means that, when controlling for age effects -the decreasing appetite for transit with age -living conditions explain the increasing use of transit by the millenials.

In the end, change in living conditions -which may all be synthetized the general concept of a longer and more uncertain transition towards adulthood -explains much of travel behavior change among the millenials. By contrast, opinions and attitudes play little role, especially as opinions from the millenials do not differ substantially from (those of) their elders. These results tend to disclaim the regular statement of millenials being less car-oriented. Instead, the way of life has changed, notably in relation with longer studies caused by the quasi-suppression of selection at the entrance of the university within cohorts born after 1985.

CONCLUSION

The generation born between 1980 and 2000, sometimes designed as the millenials or generation Y, shows off different travel behaviors from their elders, with an apparently declining appetite for car to the benefit of transit and active modes. Researchers and experts have imagined different explanations to these trends which, despite their diversity, can be roughly split into two families: the first one assumes a profound transformation of attitudes, aspirations lifestyles in generation Y, resulting in potentially durable outcomes for mobility over a long-run period, while the second essentially sees a delay caused by a longer transition towards adulthood, possibly aggravated by economic conditions, which should resorb later on in the lifecourse. Through age cohort analysis and logistic regression performed over the French Unified Base of Local Household Travel Surveys, we bring a contribution to this debate by considering the case of French young urban millenials. First, the results confirm that travel behaviors of the millenials differ substantially from their elders, especially for cohorts born after 1985, showing off an important shift from driving to transit, along with lower personal car availability, while the practice of car use as a passenger and active modes tends to remain the same. However, opinions and attitudes of generation Y do not play any role in these trends, as they do not differ substantially from their elders. On the contrary, millenials have experienced significant change in living conditions: in particular, the gradual suppression of selection at the entrance of university has resulted in considerably increasing the number of students within cohorts born after 1985 before twenty-five. In parallel, the proportion of young singles and couples without children has increased, along with the concentration of young adults in central cities. All these trends tend to corroborate the general assumption of a longer phase of transition towards adulthood which, associated with the increasing concentration of highschool establishments and highly qualified jobs in dense urban areas within the largest metropolitan cities, tends to favour less car-dependent lifestyles. In addition, travel behaviors by age and cohort are already quite well predicted through models based on living conditions alone, thus explaining most of age and generation effects. However, they don't explain the persistence of low driving after twenty-five, despite the fact that the millennials have then entered the workforce. Therefore, other factors should be involved, in particular the lack of financial resources, job precariousness and increasing housing costs. Greater parking and traffic difficulties could also play a role in reshaping behaviors, by dissuading millenials to use their car on a daily basis.

Finally, we draw out a few research tracks to deal more in-depth with this issue:

• A deeper analysis of the economic conditions experienced by the youth accross generations is required, to determine their contribution to the decline of car use. Though an income variable was not available in the Unified Base of Household Travel Surveys, it could be simulated through a model estimated from external data, using available information about socio-professional category, employment status and localization.

Whether young adults are still living with their parents should also be accounted for, along with indicators of housing and travel costs for different modes. The prolongation of economic precariousness beyond twenty-five could explain at least partially the persisting decline of car use in this age group ; • Though regression procedures have established a correlation between opinions and travel behaviors, the direction of causality remains uncertain, given the possibility of learning effects shaping opinions, or of self-justification leading to present ex-post lifestyles as the result of personal preferences. A deeper insight into the interdependencies between behaviors and opinions would be then required to determine the dominant direction of causality, through the implementation of more sophisticated models ;

• For now, the analysis is limited to French urban millenials. However, given the importance of car dependence, a comparison with French young countrymen and suburban adults would be instructive, using another source. In addition, opinions might also be strongly correlated with geographic factors and the degree of education, as France is experiencing multiple economic, social, territorial and cultural gaps.

By showing that travel behavior change in generation Y is rather resulting from living conditions than from a deeper shift in mental habits and attitudes, we may also assume that such change is not necessarily perennial, the transition towards adulthood having simply been delayed among the millenials. At first sight, these results tend to put into perspectives expectations about new generations to reduce car use. On the other hand, the decline of car use should be considered structural if economic precariousness of generation Y was to persist later on in the lifecourse, resulting in the durable impoverishment of new generations. However, in this case, one should also ask himself about the social significance for the decline of car use as it would then represent some kind of « Pyrrhic victory », being obtained only at the price of a general socio-economic regression. Indeed, it couldn't be then really considered as a progress, where the economic footprint of human activity is reduced through technological and organisational improvement, however without undermining the foundations of social cohesion and economic dynamism.

Figure 1 :

 1 Figure 1 : Indicators of travel behavior by age and cohort Source : French Unified Base of Household Travel Surveys

Figure 2 :

 2 Figure 2 : Indicators of living conditions by age and cohort Source : French Unified Base of Household Travel Surveys

Figure 3 :

 3 Figure 3 : Synthetic indicators of opinions by age and cohort Source : French Unified Base of Household Travel Surveys

Figure 4 :

 4 Figure 4 : Simulated travel behaviors by age and cohort -Left : model with living conditions only ; Right : sufficient model Source : French Unified Base of Household Travel Surveys

Table 1 :

 1 Parameter estimates for models of personal car availability, daily driving and regular transitSource : French Unified Base of Household Travel Surveys

	Daily driving	Daily or regular transit

© AET 2018 and contributors


Khaled Medini
email: khaled.medini@emse.fr

Integrating Experiential Learning and Accreditation Requirements -A Case Study Based Course

Keywords: Industrial Transition, Case Study, Experiential Learning, Accreditation, Mass Customisation. CE -Concrete Experience AE -Active Experimentation RO -Refective Observation Feeling Thinking

The increasingly evolving industrial context in manufacturing and service industries calls for an adaptation of the practices and content of engineering education curricula. More active learning approaches are required in order to easily grasp new business challenges and strategies, such as customer centric enterprise and mass customisation. This paper reports on a case study based lecture about mass customisation which was given to students pursuing a Master of Science degree in Industrial Engineering. The lecture echoes the Kolb model for Experiential Learning and refers to guidelines about accreditation requirements. An analysis of the achievement of learning outcomes is realized based a student survey conducted upon two different classes.

Introduction

Manufacturing and service industries challenges are increasingly evolving which calls for a proactivity of the company so as to keep up with these evolutions. Recent technological advances, increasing market segmentation and individualised demands, competition on both prices and quality, are among the factors motivating companies to pursue more customer centric business strategies, such as mass customisation (MC). In a simple way, MC aims to fulfil customer individual demands with near mass production efficiency [START_REF] Salvador | Cracking the Code of Mass Customization[END_REF][START_REF] Medini | Customer order fulfilment in mass customization context: an agent based approach[END_REF]. A full understanding of MC and customer orientation in operations management requires an adaptation of the practices and content of engineering education curricula, towards more active learning approaches [START_REF] Kolb | Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education[END_REF] [START_REF] Medini | Teaching customer-centric operations managementevidence from an experiential learning-oriented mass customisation class[END_REF]. This paper reports on a case study based lecture about MC, which has been given to students pursuing a Master of Science in Industrial Engineering. The lecture echoes the Kolb model for Experiential Learning and refers to guidelines about accreditation requirements. An analysis of the achievement of learning outcomes is performed based a students' survey conducted upon two different classes.

The remainder of the paper is organised as follows: Section 2 provides a brief overview of the related background literature. Section 3 reports on a method for designing a course integrating an active learning approach and accreditation requirements. Section 4 shows a designed course following the proposed method. Section 5 discusses the achievement of the learning outcomes based on students' perceptions, considering two courses with different integration levels of experiential learning. Concluding remarks are given in Section 6.

Background literatureexperiential learning theory and application in engineering education

Experiential Learning theory sees the learning as "the process whereby knowledge is created through the transformation of experience. Knowledge results from the combination of grasping and transforming experience". According the experiential learning theory, learning is a recursive process assimilated to a four-stage cycle spanning over: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE) (Figure 1). Students can start learning through concrete experience that they observe and reflect upon, deriving from their reflection abstract concepts. They then draw new implications from these abstract concepts and test them through active experimentation [START_REF] Kolb | Experiential learning: experience as the source of learning and development[END_REF][START_REF] Kolb | Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education[END_REF].

Fig. 1: Kolb learning cycle

Kolb and Kolb (2005) identified a number of educational principles that ensure the consistency of the experiential learning experience of the students and avoid misseducative experiences: respect for learners and their experience, begin learning with the learner's experience of the subject matter, creating and holding a hospital space for learning, making space for conversational learning, making space for development of expertise, making space for acting and reflecting, making spaces for feeling and thinking, making space for inside-out learning, making space for learners to take charges of their own learning. These practices within engineering courses support an improvement of the students' retention rate [START_REF] Kolb | Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education[END_REF].

Experiential learning has been widely used in a variety of engineering education contexts and within many universities all over the world; examples of these are reported in the following research works [START_REF] Bassetto | Experiencing Production Ramp-Up Education for Engineers[END_REF][START_REF] Visich | B2C Mass Customization in the Classroom[END_REF][START_REF] Medini | Teaching customer-centric operations managementevidence from an experiential learning-oriented mass customisation class[END_REF]. Despite the large use of experiential learning and the increasing awareness of its benefits to the students, guidelines for designing effective experiential learning courses or activities are scarce. Most of experiential learning applications are inspired by the basic learning cycle and design methods of the activities or courses, are either implicit or briefly presented.

Course design following EUR-ACE guidelines and experiential learning principle

This section provides a structured method for course design following an experiential learning principle. The method builds on EURA-ACE (European Accreditation for Engineering) guidelines for educational programmes design, and on learning cycle and educational principles from the experiential learning theory. More specifically, the general steps of the method are inspired by previous works on how to develop a programme based on accreditation criteria (EURA-ACE) [START_REF] Chuchalin | Engineering curriculum design based on accreditation criteria[END_REF]. The identification and articulation of learning modules and modes is inspired by the experiential learning cycle and educational principles.

Figure 2 shows the steps of the method, reported on in the boxes. Vertical arrows refer to the main used concepts from literature. Full line horizontal arrows illustrate the sequence of the steps, while the dashed arrows show some of the main iterations which occur during the design process. The general design steps are needs analysis, general objectives identification, learning outcomes definition, learning modules identification, building syllabus, and course assessment plan.

Fig. 2: Course design method

Needs analysis is recognized as an important step in course design which contributes towards more contextualised content (Lekatompessy, 2010). This step is expected to enlighten the course designers on the learners' current skills, knowledge gaps, objective and subjective needs, and on the expected use of the know-how and knowledge transmitted through the course. Needs analysis is not a one-shot action; on the contrary, it should support a continuous improvement process of the course.

Needs analysis contributes to the subsequent steps namely the formalisation of the course general objectives and learning outcomes. Similarly to the programme design context (ENAEE, 2005), in course design, the general objectives should reflect how the needs of the students (or any other targeted public) are met through the course.

Learning outcomes refer to the graduate attributes in terms of technical and soft skills that can be achieved through the course [START_REF] Chuchalin | Engineering curriculum design based on accreditation criteria[END_REF]. The definition of learning outcomes uses the revised taxonomy of Bloom (Krathwohl,

Needs analysis

Context of the course design

General objectives

Overall vision of course objectives

Learning Outcomes Technical and soft graduate attributes

Learning Modules

Course main content

Build Syllabus Evaluation, teaching technologies

Course assessment

How the course will be evaluated

Learning Cycle

Educational principles

Bloom revised Taxonomy

2002

). The dimensions of the revised taxonomy are as follows: Remember -Retrieving relevant knowledge from long-term memory. Understand -Determining the meaning of instructional messages, including oral, written, and graphic. Apply -Carrying out or using a procedure in a given situation. Analyze -Breaking material into its constituent parts and detecting how the parts relate to one another and to an overall structure or purpose. Evaluate -Making judgments based on criteria and standards. Create -Putting elements together to form a novel, coherent whole or make an original product.

In the next step of course design, the learning modules are identified and mapped to learning outcomes. The share of the grades among learning modules is partly based on learning modules importance to learning outcomes achievement. The identification of learning modules is inspired by experiential learning theory in order to define the learning cycle. The four learning stages of experiential learning theory are used as guidelines to identify the focus of each learning module. Altogether, learning modules should support a specific learning cycle.

The subsequent step, build syllabus, consists in defining how learning modules will be actually conducted (e.g. sequence, organisation, teaching technologies, etc.) and how the grades are assigned. This step is referred to in Figure 2 by Build Syllabus.

In the final step in course design, course assessment, the methods and ways of assessing the course will be designed. Usually, students' surveys are an easy-toimplement method and provide valuable insights into student perceptions.

Designed MC course

This section reports on the design of new course following the presented method, starting from a similar course which has been given to MSc students in 2016. The use of the method supported substantial modifications in both objectives and content of the course. Next paragraphs illustrate the six design steps. Needs analysis. The course is developed within an MSc programme in Industrial Engineering. The students have backgrounds in industrial or mechanical engineering. They hold either a bachelor or an Engineer or equivalent MSc degree in these domains. Most of the students are international and come from Europe, North Africa, Asia and Latin America. This also means that the course language should be no other than English, as it is the most likely common language for all students. Within the context of industrial transition, the students lack a deep understanding of how business strategies such as MC could foster the industrial renewal and how they can actually be implemented.

General objectives. The objective of the course is threefold; first introduce MC as a customer centred strategy, second enlighten the students on how MC can actually be implemented in the manufacturing and service industries, third enhance students' research abilities and critical thinking.

Learning outcomes (LOs). The learning outcomes of the course are as follows:

 LO1 -Understand MC philosophy and main capabilities (choice navigation, solution space development, robust process design (Salvador et al. ( 2009));

 LO2 -Analyse how these capabilities can be put in practice;  LO3 -Evaluate the experience of a given company with MC implementation;  LO4 -Plan the required actions for a service company to benefit from MC;  LO5 -Criticize the soundness of case studies analysis performed by other peers.

Learning modules (LMs).

Learning modules include the following:  LM1 -An introduction into the topic.  LM2 -A seminal paper reading about MC capabilities.  LM3 -Analysis of mass customisation case studies.  LM4 -Review of peers' work about case studies analysis.

The planned contribution of learning modules to the learning cycle is reported on in Figure 3.

Fig. 3: LMs mapping to learning cycle

Table 1 reports on the mapping between learning modules and learning outcomes: a '+' in the crossing between a given row i and a given column j means that LMi contributes to the achievement of LOj. Clearly, case studies (LM3) have the most significant contribution to learning outcomes. However, these cannot be achieved without the other learning modules altogether. This is quite expected because of the involvement of LM3 in various learning stages of the experiential learning cycle. Build syllabus. The course is organised as follows; LM1 is planned in the beginning so as to provide an introduction and the instructions to the students. Afterwards, students are left with the paper reading (LM2). A summary of the paper is required which enhances also the reflective observation activity in reference to experiential learning cycle. Upon finishing off with the seminal paper, students start working on three assigned case companies (LM3). The students are asked to report on how these companies implemented the three MC capabilities, seen in LM2. The provided input material consists basically of i) written interviews of mass customisation experts speaking about case companies' experiences with MC, and ii) case companies' websites. A first deliverable including the article summary and case

Concrete Experience Active Experimentation Abstract Conceptualisation

Reflective observation

LM1 -LM2 -LM3 LM3 LM2 -LM3 LM4
studies analysis is due one week after the beginning of the course. Then students are asked to identify the actions required to pursue MC considering an integrated product and service offering. A second deliverable summing up this effort is due two weeks after the beginning of the course. Upon receiving students' deliverables, a peer review process is launched and need to be finished within one week. A presentation session is planned few days later to discuss the case studies. The interactions between students are enhanced by their recent peer review experience.

Requisite skills can be met with an industrial engineering or engineering management background. The evaluation is based on the deliverable quality, the presentation and discussion, and a final exam about MC case.

Course assessment. The questionnaire will be given to students few days after the last session. A hard copy is used to increase the response rate and avoid nonexploitable answers. The questionnaire include the following sections: students background, MC implications to firms strategy and competitiveness, MC implications to variety management, MC implications to production and supply chain management, Experiential learning approach, and MC topic relevance. A full version of the questionnaire can be found in [START_REF] Medini | Teaching customer-centric operations managementevidence from an experiential learning-oriented mass customisation class[END_REF].

Students perception and insights into learning outcomes achievement

A survey was conducted in 2018 upon achieving the course. In order to evaluate the course improvement actions, the results were compared with the ones from a previous survey conducted in 2016 with another class taking the same course but with a different structure [START_REF] Medini | Teaching customer-centric operations managementevidence from an experiential learning-oriented mass customisation class[END_REF]. Figure 4 depicts the aggregate results of the surveys according to the questionnaire sections.

Fig. 4: Summary of the survey results

In the case of the adapted course, the sample size ranges between 12 and 13 while in the case of the traditional course the sample size is in the range 14 -17. The questions are answered using a 5 levels grid (1 strongly disagree, 5 strongly agree). In student surveys of the previous traditional course and the redesigned, adapted course, most of the students are international and have similar past education.

MC topic relevance

Adapted course Traditional course

Overall, it can be seen that there is an improvement in the achievement of the objectives. Unsurprisingly, MC implications to production and supply chain management were better grasped in the traditional course. This is because the case studies are not directed towards these topics. Moreover, the traditional course included two simulation sessions addressing partly production management issues. However, the perceptions of the students from the adapted course indicated that this course brings more evidence of the relevance of the MC topic, and of the experiential learning approach.

Figure 5 shows a world cloud derived from students' answers to the open-ended questions of the survey, related to the adapted course. The size of the words is proportional to their frequency of occurrence. The world cloud is generated by the R software [START_REF] Ihaka | R: A language for data analysis and graphics[END_REF] with the following rules: number of the words in the cloud should not exceed 30 (for the sake of readability), and minimum occurrence frequency of the words in students comments is 2 (to cover as many topics as possible).

Fig. 5: Word cloud of students' comments

It can be seen that case studies are prevailing in the answers of the students, highlighting particularly a positive perception of the students. The case studies helped some students to understand '…in a more concrete and independent way how mass customisation is implemented…". Some other less frequent key words reflect the positive impact of the learning practices, e.g. self-learning, apply, real. Interestingly, the word "prof" (professor) appeared several times highlighting the need of some students to get continuous feedback on their work by the instructor. This can be addressed by intermediate discussion sessions. The survey revealed another potential improvement avenue that is, to visit companies and gain more insights into their experience with the mass customisation.

Although these findings need more evidence, they provide valuable insights into the impact of the proposed approach and into some potential improvement avenues.

Conclusion

The interdisciplinary topics brought by the industrial transition require proactivity on the side of academicians so as to design adapted course content and use appropriate teaching methods. This paper calls for integrating experiential learning theory and accreditation guidelines in the (re)design of courses in the engineering education domain to keep up with the industrial transition context. It was shown through the surveys that these approaches are likely to improve the achievement of the course objectives and student satisfaction.

Table 1 .

 1 LOs and LMs mapping

		LO1 LO2 LO3 LO4 LO5
	LM1	+
	LM2	+	+
	LM3	+	+	+	+
	LM5		+	+	+

Acknowledgements

The author would like to thank Omar Ezzat for his support in the peer review process and the management of the various deliverables from the students.