Exploring Generational Private Mobility Paradigm Shifts through Duration Modeling Analytics: A Greek Case Study
Abstract
:1. Introduction
- The age of getting a car driving license.
- The period since a user obtains a driving license until he/she gets his/her first car.
- The age of getting a motorbike driving license.
- The age of getting a bicycle as an adult.
- The age of travelling by airplane for the first time.
2. Materials and Methods
2.1. Duration Analysis
2.2. Data
- The period from the age of 18 years old (the youngest possible age for getting a car driving license in Greece) until the age they got a car driving license.
- The period from the age of 16 years old (the youngest possible age for getting a motorbike driving license in Greece) until the age they got a motorbike driving license.
- The period from the age they got their car driving license until the age they got their first car.
- The period from the age 18 years old until the age they got their first bicycle as adults.
- The period from their birth until the age they traveled by airplane for the first time.
- (1)
- Definition of the examined period. Since the analyzed periods have different starting points we needed to clearly define each period’s earliest possible starting point. i.e., the starting point of the period until getting a car driving license can′t be earlier than the age of 18 years old.
- (2)
- Definition and calculation of the necessary variables to perform duration analysis. This includes the calculation of the period that was set in the previous step as well as the definition of whether the observation is censored or not. i.e., if someone still hasn’t obtained a car driving license when answering the survey, the observation is censored.
- (3)
- Non-parametric duration analysis using the Kaplan-Meier method.
- (4)
- Test of all the explanatory variables’ effect on duration, with the use of the log-rank test. Variables with a log-rank test p-value less or equal to 0.05 will be used in the next step.
- (5)
- Plot of survival curves for explanatory variables with significant effect on duration, as they were found in the previous step.
- (6)
- Variable selection for the Cox Proportional Hazards Model, using a step by step selection method, as it was described by Collet [61].
- (7)
- Correlation check of the selected variables to avoid multicollinearity.
- (8)
- Check the Proportional Hazards Assumption for a simple Cox model or an extended or stratified Cox model if the assumption is not satisfied at first.
- (9)
- Build a Cox Proportional Hazards Model, using the variables selected in the steps above.
- (10)
- Model validation using the Cox-Snell and Deviance residuals for each model.
3. Results
3.1. Duration Analysis of the Age at Which Someone Gets Their Car Driving License
3.2. Duration Analysis of the Interval from Getting a Car Driving License until Getting a First Private Car
3.3. Duration Analysis of the Age at Which Someone Their Motorbike Driving License
3.4. Duration Analysis of the Age at Which Adults Get Their First Bicycle
3.5. Duration Analysis of the Age at Which Someone Travels by Airplane for the First Time
3.6. Mutual Evolution of Mobility Milestones
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Getting a Car Driving License | Getting Their First Car | Getting a Motorbike Driving License | Getting Their First Bike as Adults | Taking Their First Airplane Trip | |
---|---|---|---|---|---|
Gender | |||||
Male | Reference | - | 4.56 | - | - |
Female | 0.46 | - | Reference | - | - |
Age Group | |||||
18–30 | Reference | Reference | Reference | ||
31–45 | 1.89 | 0.68 | 0.60 | ||
46+ | 1.53 | 0.16 | 0.37 | ||
Parents’ Income | |||||
0–1200 Euro | Reference | - | - | - | - |
1201–2000+ Euro | 3.28/1.57 1 | - | - | - | - |
Use of a private car by family | |||||
Yes | Reference | - | - | - | - |
No | 0.48 | - | - | - | - |
Place they grew up in | |||||
Village | - | Reference | - | Reference | 0.68 2 |
Town | - | 0.40 | - | 0.34 | |
City | - | 0.66 | - | 0.74 | Reference |
Need of a motorized vehicle for commuting | |||||
Yes, with a motorized vehicle | Reference | 2.67 | - | ||
No /Yes, walking or cycling | 0.53 | Reference | - | ||
Sustainable Transport | |||||
Neutral | - | 1 | Reference | Reference | |
Unimportant | |||||
Very Unimportant | |||||
Very Important | |||||
Important | - | 1 | 0.47 | 1.79 | |
Environmental Awareness | |||||
Very Important | - | Reference | - | - | |
Important | - | 1.28 | - | - | |
Neutral | - | 0.76 | - | - | |
Unimportant | - | 1.99 | - | - | |
Very Unimportant | - | 13.61 | - | - | |
License Necessity | |||||
Somewhat Necessary | Reference | - | - | - | |
A Little Necessary | |||||
Not Necessary | |||||
Very Necessary | |||||
Necessary | 2.54 | - | - | - |
References
- Kuhnimhof, T.; Buehler, R.; Dargay, J. A New Generation: Travel Trends among young Germans and Britons. In Proceedings of the 90th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 23–27 January 2011. [Google Scholar]
- Kuhnimhof, T.; Armoogum, J.; Buehler, R.; Dargay, J.; Denstadli, J.M.; Yamamoto, T. Men Shape a Downward Trend in Car Use among Young Adults-Evidence from Six Industrialized Countries. Transp. Rev. 2012, 32, 761–779. [Google Scholar] [CrossRef]
- Keyes, A.K.M.; Crawford-Brown, D. The changing influences on commuting mode choice in urban England under Peak Car: A discrete choice modelling approach. Transp. Res. Part F Traffic Psychol. Behav. 2018, 58, 167–176. [Google Scholar] [CrossRef]
- Clark, B.; Chatterjee, K.; Melia, S. Changes in level of household car ownership: The role of life events and spatial context. Transportation 2016, 43, 565–599. [Google Scholar] [CrossRef] [Green Version]
- Clark, B.; Chatterjee, K.; Melia, S. Changes to commute mode: The role of life events, spatial context and environmental attitude. Transp. Res. Part A Policy Pract. 2016, 89, 89–105. [Google Scholar] [CrossRef] [Green Version]
- Le Vine, S.; Jones, P.; Lee-Gosselin, M.; Polak, J. Is heightened environmental sensitivity responsible for drop in young adults’ rates of driver’s license acquisition? Transp. Res. Rec. 2014, 2465, 73–78. [Google Scholar] [CrossRef]
- Hopkins, D. Can environmental awareness explain declining preference for car-based mobility amongst generation Y? A qualitative examination of learn to drive behaviours. Transp. Res. Part A Policy Pract. 2016, 94, 149–163. [Google Scholar] [CrossRef]
- Pojani, E.; Van Acker, V.; Pojani, D. Cars as a status symbol: Youth attitudes toward sustainable transport in a post-socialist city. Transp. Res. Part F Traffic Psychol. Behav. 2018, 58, 210–227. [Google Scholar] [CrossRef]
- Belgiawan, P.F.; Schmöcker, J.D.; Abou-Zeid, M.; Walker, J.; Fujii, S. Modelling social norms: Case study of students’ car purchase intentions. Travel Behav. Soc. 2017, 7, 12–25. [Google Scholar] [CrossRef]
- Delbosc, A.; Currie, G. Causes of Youth Licensing Decline: A Synthesis of Evidence. Transp. Rev. 2013, 33, 271–290. [Google Scholar] [CrossRef]
- Newbold, K.B.; Scott, D.M. Driving over the life course: The automobility of Canada’s Millennial, Generation X, Baby Boomer and Greatest Generations. Travel Behav. Soc. 2017, 6, 57–63. [Google Scholar] [CrossRef]
- Oakil, A.T.M. Securing or sacrificing access to a car: Gender difference in the effects of life events. Travel Behav. Soc. 2016, 3, 1–7. [Google Scholar] [CrossRef]
- Mayne, K.; Baltatzi, E. European Bicycle Market Analysis 2015; European Cyclists’ Federation: Brussels, Belgium, 2015. [Google Scholar]
- Sigurdardottir, S.B.; Kaplan, S.; Møller, M.; Teasdale, T.W. Understanding adolescents’ intentions to commute by car or bicycle as adults. Transp. Res. Part D Transp. Environ. 2013, 24, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Nikitas, A. Understanding bike-sharing acceptability and expected usage patterns in the context of a small city novel to the concept: A story of ‘Greek Drama’. Transp. Res. Part F Traffic Psychol. Behav. 2018, 56, 306–321. [Google Scholar] [CrossRef] [Green Version]
- De Neufville, R.; Odoni, A. Airport Systems, Planning Design and Management; McGraw-Hill Education: New York, NY, USA, 2013. [Google Scholar]
- Tao, S.; He, S.Y.; Thøgersen, J. The role of car ownership in attitudes towards public transport: A comparative study of Guangzhou and Brisbane. Transp. Res. Part F Traffic Psychol. Behav. 2019, 60, 685–699. [Google Scholar] [CrossRef]
- Brittanica Encyclopedia Brittanica Encyclopedia—Greece. Available online: https://www.britannica.com/place/Greece/Religion (accessed on 16 November 2020).
- The Economist Acropolis Now. Available online: https://www.economist.com/leaders/2010/04/29/acropolis-now (accessed on 16 November 2020).
- Tue New York Times The Euro Is a Straitjacket for Greece. Available online: https://www.nytimes.com/roomfordebate/2015/06/30/should-greece-abandon-the-euro/the-euro-is-a-straitjacket-for-greece (accessed on 16 November 2020).
- Kyriakidis, A. The Greek Crisis 2009–2015: A Comprehensive Analysis of the EU-IMF Financial Assistance Programs. Int. J. Employ. Stud. 2016, 24, 7. [Google Scholar]
- Statista Research Department Number of Passenger Cars per 1000 Inhabitants in Greece from 1990 to 2017. Available online: https://0-www-statista-com.brum.beds.ac.uk/statistics/452020/greece-number-of-cars-per-1000-inhabitants/ (accessed on 16 November 2020).
- Lalioti, K.M. Carsharing: The Study of an Innovative and Ecological Way of Transport. Master’s Thesis, University of Piraeus, Pireas, Greece, 2012. [Google Scholar]
- City of Thessaloniki, Metropolitan Development Agency of Thessaloniki. Resilient Thessaloniki: A Strategy for 2030. THEPTA Report (In Greek); Thessaloniki Public Transport Authority (THEPTA): Thessaloniki, Greece, 2020. [Google Scholar]
- Bakogiannis, E.; Siti, M.; Tsigdinos, S.; Vassi, A.; Niksitas, A. Monitoring the first dockless bike sharing system in Greece: Understanding user perceptions, usage patterns and adoption barriers. Res. Transp. Bus. Manag. 2019, 33, 100432. [Google Scholar] [CrossRef]
- Politis, I.; Papadopoulos, E.; Fyrogenis, I.; Nikolaidou, A.; Delivopoulos, G.; Tsampouris, I. WP 2.1 Building 4-Stage Travel Demand Model; CHANGE Project: Thessaloniki, Greece, 2019. [Google Scholar]
- Rodriguez, G. Lecture Notes on Generilized Linear Models. Available online: http://data.princeton.edu/wws509/notes/ (accessed on 16 November 2020).
- Klein, J.P.; Moeschberger, M.L. SURVIVAL ANALYSIS Techniques for Censored and Truncated Data, 2nd ed.; Springer: New York, NY, USA, 2003. [Google Scholar]
- Jovanis, P.; Chang, H.L. Disaggregate model of highway accident occurrence using survival theory. Accid. Anal. Prev. 1989, 21, 445–458. [Google Scholar] [CrossRef]
- Hansher, D.A.; Mannering, F.L. Hazard-based duration models and their application to transport analysis. Transp. Rev. 1994, 14, 63–82. [Google Scholar] [CrossRef]
- Sexton, B.; Grayson, G. Further Analyses of Accident Data from the Cohort II Study: When Do Drivers Have Their First Accident and Does It Have an Impact on Their Subsequent Driving? Transport Research Laboratory: Crowthorne, UK, 2009. [Google Scholar]
- Shin, H.C.; Madanat, S. Development of a stochastic model of pavement distress initiation. J. Infrastruct. Plan. Man. 2003, 4, 61–67. [Google Scholar] [CrossRef] [Green Version]
- Loizos, A.; Karlaftis, M.G. Prediction of Pavement Crack Initiation from In-Service Pavements A Duration Model Approach. Transp. Res. Rec. J. Transp. Res. Board. 2005, 1940, 38–42. [Google Scholar] [CrossRef]
- Vlahogianni, E.I. Modeling duration of overtaking in two lane highways. Transp. Res. Part F Traffic Psychol. Behav. 2013, 20, 135–146. [Google Scholar] [CrossRef]
- Dorantes Argandar, G.; Tortosa Gil, F.; Ferrero Berlanga, J. Measuring situations that stress Mexicans while driving. Transp. Res. Part F Traffic Psychol. Behav. 2016, 37, 154–161. [Google Scholar] [CrossRef]
- Choudhary, P.; Velaga, N.R. Modelling driver distraction effects due to mobile phone use on reaction time. Transp. Res. Part C 2017, 77, 351–365. [Google Scholar] [CrossRef]
- Yadav, A.K.; Velaga, N.R. Modelling the relationship between different Blood Alcohol Concentrations and reaction time of young and mature drivers. Transp. Res. Part F Traffic Psychol. Behav. 2019, 64, 227–245. [Google Scholar] [CrossRef]
- Tiwari, G.; Bangdiwala, S.; Saraswat, A.; Gaurav, S. Survival analysis: Pedestrian risk exposure at signalized intersections. Transp. Res. Part F 2007, 10, 77–89. [Google Scholar] [CrossRef]
- Yang, X.; Huan, M.; Si, B.; Gao, L.; Guo, H. Crossing at a Red Light: Behavior of Cyclists at Urban Intersections. Res. Artic. 2012. [Google Scholar] [CrossRef]
- Mannering, F.L.; Hamed, M.M. Occurence, frequency, and duration of commuters’ work-to-home departure delay. Transp. Res. Part B 1990, 24, 99–109. [Google Scholar] [CrossRef]
- Mannering, F.; Murakami, E.; Kim, S.-G. Temporal stability of travelers’ activity choice and home-stay duration: Some empirical evidence. Transportation 1994, 21, 371–392. [Google Scholar] [CrossRef]
- Lee, E.T.; Wang, J.W. Statistical Methods for Survival Data Analysis, 3rd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003. [Google Scholar]
- Kleinbaum, D.G.; Klein, M. Survival Analysis A Self-Learning Text, 3rd ed.; Springer: New York, NY, USA, 2012. [Google Scholar]
- Cox, D.R. Regression Models and Life-Tables. J. R. Stat. Soc. Ser. B 1973, 34, 187–220. [Google Scholar]
- Donev, V.; Hoffmann, M. Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks. Int. J. Pavement Eng. 2017, 20, 313–331. [Google Scholar] [CrossRef] [Green Version]
- Clark, T.G.; Bradburn, M.J.; Love, S.B.; Altman, D.G. Surival Analysis Part I: Basic concepts and first analyses. Br. J. Cancer 2003, 89, 232–238. [Google Scholar] [CrossRef]
- Onwezen, M.C.; Bartels, J. Which perceived characteristics make product innovations appealing to the consumer? A study on the acceptance of fruit innovations using cross-cultural consumer segmentation. Appetite 2011, 57, 50–58. [Google Scholar] [CrossRef] [PubMed]
- Abeliotis, K.; Koniari, C.; Sardianou, E. The profile of the green consumer in Greece. Int. J. Consum. Stud. 2010, 34, 153–160. [Google Scholar] [CrossRef]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020. [Google Scholar]
- Wickham, H.; Roamain, F.; Lionel, H.; Kirill, M. Dplyr: A Grammar of Data Manipulation; R Package Version 0.7.6; 2018. Available online: https://cran.r-project.org/web/packages/dplyr/index.html (accessed on 16 November 2020).
- Therneau, T.; Grambsch, P.; Fleming, T. A Package for Survival Analysis in S; Springer: New York, NY, USA, 2015. [Google Scholar]
- Kassambara, A.; Kosinski, M. Survminer: Drawing Survival Curves Using “ggplot2”; R Package Version 0.4.2; 2018. Available online: https://cran.r-project.org/web/packages/survminer/index.html (accessed on 16 November 2020).
- Jackson, C. {flexsurv}: A Platform for Parametric Survival Modeling in {R}. J. Stat. Softw. 2016, 70, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wickham, H. ggplot2: Elegant Graphics for Data Analaysis; Springer: New York, NY, USA, 2016. [Google Scholar]
- Arnold, J.B. ggthemes: Extra Themes, Scale and Geoms for “ggplot2”; R Package Version 4.0.1. 2018. Available online: https://rdrr.io/cran/ggthemes/ (accessed on 28 April 2021).
- Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S, 4th ed.; Springer: New York, NY, USA, 2002; ISBN 0-387-95457-0. [Google Scholar]
- Fox, J.; Weisberg, S. An {R} Companion to Applied Regression, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2019. [Google Scholar]
- Soetaert, K. plot3D: Plotting Multi-Dimensional Data; 2019. Available online: https://cran.r-project.org/web/packages/plot3D/index.html (accessed on 16 November 2020).
- Soetaert, K. plot3Drgl: Plotting Multi-Dimensional Data—Using “rgl”; R Package Version 1.0.1; 2016. Available online: https://cran.r-project.org/web/packages/plot3Drgl/index.html (accessed on 16 November 2020).
- Kennedy, N. forestmodel: Forest Plots from Regression Models; 2020. Available online: https://cran.r-project.org/web/packages/forestmodel/index.html (accessed on 16 November 2020).
- Collet, D. Modelling Survival Data in Medical Research, 3rd ed.; Chapman & Hall/CRC: Boca Raton, FL, USA, 2014. [Google Scholar]
- Vittinghoff, E.; McCulloch, C.E. Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression. Am. J. Epidemiol. 2007, 165, 710–718. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Republic of Greece Government Gazette. Presidential Decree # 51; Republic of Greece: Athens, Greece, 2012. [Google Scholar]
- Prieto, M.; Caemmerer, B. An exploration of factors influencing car purchasing decisions. Int. J. Retail Distrib. Manag. 2013, 41, 738–764. [Google Scholar] [CrossRef]
- Klein, N.J.; Smart, M.J. Millennials and car ownership: Less money, fewer cars. Transp. Policy 2017, 53, 20–29. [Google Scholar] [CrossRef] [Green Version]
- Pyddoke, R.; Creutzer, C. Household Car Ownership in Urban and Rural Areas in Sweden 1999–2008; Working papers in transport economics; CTS—Centre for Transport Studies Stockholm: Stockholm, Sweden, 2014. [Google Scholar]
- Carroll, P.; Benevenuto, R.; Caulfield, B. Identifying Hotspots of Transport Disadvantage and Car Dependency in Rural Ireland. Transp. Policy 2020, 101, 46–56. [Google Scholar] [CrossRef]
- Kopp, P. The unpredicted rise of motorcycles: A cost benefit analysis. Transp. Policy 2011, 18, 613–622. [Google Scholar] [CrossRef]
- Heinen, E.; van Wee, B.; Maat, K. Commuting by Bicycle: An Overview of the Literature. Transp. Rev. A Transnatl. Transdiscipl. J. 2010, 30, 59–96. [Google Scholar] [CrossRef]
- Handy, S.L.; Xing, Y.; Buehler, T.J. Factors associated with bicycle ownership and use: A study of six small U.S. cities. Transportation 2010, 37, 967–985. [Google Scholar] [CrossRef]
- Akar, G.; Clifton, K.J. Influence of individual perceptions and bicycle infrastructure on decision to bike. Transp. Res. Rec. 2009, 2140, 165–172. [Google Scholar] [CrossRef]
- Rietveld, P.; Daniel, V. Determinants of bicycle use: Do municipal policies matter? Transp. Res. Part A Policy Pract. 2004, 38, 531–550. [Google Scholar] [CrossRef]
- European Cyclists’ Federation ECF Cycling Barometer. Available online: https://ecf.com/resources/cycling-facts-and-figures/ecf-cycling-barometer (accessed on 16 November 2020).
- Aggelidou, T.V. Municipality of Thessaloniki: Bicycle Master Plan. Master’s Thesis, Aristotle University of Thessaloniki, Thessaloniki, Greece, 2016. [Google Scholar]
- Confederation of the European Bicycle Industry. European Bicycle Market; Springer: Brussels, Belgium, 2016. [Google Scholar]
- European Parliament. The promotion of cycling. Policy Dep. B Struct. Cohes. Policies Transp. Tour. 2010, 1–74. Available online: https://www.ecf.com/sites/ecf.com/files/European-Parliament-2010_Promotion-of-Cycling.pdf (accessed on 16 November 2020).
- Heinen, E.; Maat, K.; Van Wee, B. The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transp. Res. Part D Transp. Environ. 2011, 16, 102–109. [Google Scholar] [CrossRef]
- Halldórsdóttir, K.; Christensen, L.; Jensen, T.C.; Prato, C.G.; Giacomo, C.; Linda Christensen, B.; Thomas Christian Jensen, B.; Carlo Giacomo Prato, B. Modelling Mode Choice in Short Trips—Shifting from Car to Bicycle. In Proceedings of the European Transport Conference, Glasgow, UK, 10–12 October 2011. [Google Scholar]
- González-Iglesias, B.; Gómez-Fraguela, J.A.; Luengo-Martín, M.Á. Driving anger and traffic violations: Gender differences. Transp. Res. Part F Traffic Psychol. Behav. 2012, 15, 404–412. [Google Scholar] [CrossRef]
- Bergdahl, J. Sex differences in attitudes toward driving: A survey. Soc. Sci. J. 2005, 42, 595–601. [Google Scholar] [CrossRef]
- Özkan, T.; Lajunen, T. What causes the differences in driving between young men and women? The effects of gender roles and sex on young drivers’ driving behaviour and self-assessment of skills. Transp. Res. Part F Traffic Psychol. Behav. 2006, 9, 269–277. [Google Scholar] [CrossRef]
- Hjorthol, R. Decreasing popularity of the car? Changes in driving licence and access to a car among young adults over a 25-year period in Norway. J. Transp. Geogr. 2016, 51, 140–146. [Google Scholar] [CrossRef] [Green Version]
- Sivak, M.; Schoettle, B. Recent changes in the age composition of drivers in 15 countries. Traffic Inj. Prev. 2012, 13, 126–132. [Google Scholar] [CrossRef]
- Haustein, S.; Møller, M. Age and attitude: Changes in cycling patterns of different e-bike user segments. Int. J. Sustain. Transp. 2016, 10, 836–846. [Google Scholar] [CrossRef] [Green Version]
- Dill, J. Are Millennials Really the Generation That Bikes? Available online: https://trec.pdx.edu/blog/are-millennials-really-generation-bikes (accessed on 31 March 2021).
- Burrows, M. Younger Workers in Cities More Likely to Bike to Work. Available online: https://www.census.gov/library/stories/2019/05/younger-workers-in-cities-more-likely-to-bike-to-work.html (accessed on 31 March 2021).
- Barabino, B.; Cabras, N.A.; Conversano, C.; Olivo, A. An Integrated Approach to Select Key Quality Indicators in Transit Services. Soc. Indic. Res. 2020, 149, 1045–1080. [Google Scholar] [CrossRef]
Variable Name | Levels | Percentage | Variable Type | Short Variable Description |
---|---|---|---|---|
sex | Male | 55.06% | Categorical | Gender of the respondent |
Female | 44.96% | |||
birth | Ordinal | Year of birth of the respondent | ||
job | Private Employee | 21.84% | Categorical | Main area of employment during the last decade |
Public Servant | 14.87% | |||
Freelancer | 14.24% | |||
Unemployed | 5.38% | |||
Household | 1.90% | |||
Student | 36.71% | |||
Pensioner | 3.48% | |||
Other | 1.58% | |||
edu | Primary School Graduate | 0.63% | Categorical | Education level |
Secondary School Graduate | 0.00% | |||
Highschool Graduate | 31.96% | |||
Technical School Graduate | 7.28% | |||
University Graduate | 32.59% | |||
Graduate School | 27.54% | |||
funds | 0–400 Euros | 5.38% | Ordinal | Average monthly household income during the last decade |
401–800 Euros | 12.97% | |||
801–1200 Euros | 27.53% | |||
1201–1600 Euros | 18.35% | |||
1601–2000 Euros | 16.47% | |||
More than 2000 Euros | 19.30% | |||
par_funds | 0–400 Euros | 5.70% | Ordinal | Average monthly parents’ household income when the respondent was 18–23 years old |
401–800 Euros | 12.34% | |||
801–1200 Euros | 23.73% | |||
1201–1600 Euros | 15.51% | |||
1601–2000 Euros | 16.14% | |||
More than 2000 Euros | 26.58% | |||
grow | City | 68.35% | Categorical | Area in which the respondent grew up |
Town 1 | 17.72% | |||
Village 2 | 13.93% | |||
job_trsp | Yes, on feet or by bicycle | 16.46% | Categorical | Necessity of transportation for commuting |
Yes, with a mechanized vehicle | 39.87% | |||
No | 43.67% | |||
sustain | Very | 47.78% | Ordinal | Importance of the sustainability of the transport vehicle |
Quite | 33.86% | |||
Moderately | 12.03% | |||
A Little | 5.06% | |||
Not at all | 1.27% | |||
environ | Very | 26.58% | Ordinal | Environmental awareness |
Quite | 55.38% | |||
Moderately | 12.34% | |||
A Little | 5.38% | |||
Not at all | 0.32% | |||
tech | Very | 33.84% | Ordinal | Respondent’s familiarity with technology |
Quite | 47.12% | |||
Moderately | 12.69% | |||
A Little | 5.14% | |||
Not at all | 1.21% | |||
lic_nec | Very | 46.52% | Ordinal | How necessary the respondent feels is for everyone to get a driving license |
Quite | 34.81% | |||
Moderately | 12.34% | |||
A Little | 5.38% | |||
Not at all | 0.95% | |||
car_lic | Categorical | Year in which the respondent got a car driving license | ||
fam_car | Yes | 85.5% | Categorical | Whether the respondent’s family used a car for transportation |
No | 14.5% | |||
bike_lic | Ordinal | Year in which the respondent got a motorbike driving license | ||
first_car | Ordinal | Year in which the respondent got his first car | ||
first_bike | Ordinal | Year in which the respondent got his first bicycle as an adult | ||
first_plane | Ordinal | Year in which the respondent first travelled by airplane |
Getting a Car Driving License | Getting Their First Car | Getting a Motorbike Driving License | Getting Their First Bike as Adults | Taking Their First Airplane Trip | |
---|---|---|---|---|---|
Gender | Higher for male | Higher for male | |||
Age Group | Higher for Younger than 45 years | Higher for over 30 | Higher for younger ages | Higher for younger ages | |
Parents’ Income | Higher for higher incomes | Higher for lower incomes | Higher for higher incomes | ||
Use of a private car by family | Higher if they used one | ||||
Place they grew up in | Higher for villages | Higher for cities | Higher for cities | ||
Need of a motorized vehicle for commuting | Higher with need | Higher with need | |||
Sustainable Transport | Higher for not important | Higher for important | |||
Environmental Awareness | Higher for not important |
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Fyrogenis, I.; Politis, I. Exploring Generational Private Mobility Paradigm Shifts through Duration Modeling Analytics: A Greek Case Study. Future Transp. 2021, 1, 54-81. https://0-doi-org.brum.beds.ac.uk/10.3390/futuretransp1010005
Fyrogenis I, Politis I. Exploring Generational Private Mobility Paradigm Shifts through Duration Modeling Analytics: A Greek Case Study. Future Transportation. 2021; 1(1):54-81. https://0-doi-org.brum.beds.ac.uk/10.3390/futuretransp1010005
Chicago/Turabian StyleFyrogenis, Ioannis, and Ioannis Politis. 2021. "Exploring Generational Private Mobility Paradigm Shifts through Duration Modeling Analytics: A Greek Case Study" Future Transportation 1, no. 1: 54-81. https://0-doi-org.brum.beds.ac.uk/10.3390/futuretransp1010005