Analysis of millennials and older adults’ automobility behavior in Hamilton, Ontario

ABSTRACT

This study explores the automobility behavior of millennials (those born between 1980 and 2000) and older adults (65 years and older) and the factors that influence their automobility behavior using cross-sectional data from Hamilton, Ontario. This study focuses specifically on how automobility behavior of millennials and older adults is shaped by their socio-demographic characteristics, living arrangements, attitudes, and preferences toward transportation modes and residential location characteristics. Results from the binomial and ordinal logistic regressions suggest that depending on whether a millennial or older adult lives alone, with a partner, or in an apartment, their automobility behavior differs. The study also finds that positive attitudes and preferences toward sustainable travel behavior make both generations less auto-oriented, especially millennials. Regarding preferred residential location characteristics, compared to older adults, millennials’ preference toward off-street parking in their residential neighborhood is likely to influence their automobile use. Compared to older adults, living arrangements, attitudes, and preferences influence, to a greater extent, millennials’ attributes of automobility. Further, the study also suggests that living arrangements, attitudes, and preferences can differ among millennials and older adults. Consequently, the impact on each of the attributes of automobility behavior will differ.

KEYWORDS:

• Attitudes
• automobility
• millennials
• older adults
• perceptions

1. Introduction

Millennials have become an important target group for promoting sustainable modes of transport, with evidence from several studies suggesting that their travel patterns and transportation preferences differ compared to other generations. Although the start and end points for different generations are somewhat arbitrary, this study considered those who were born between 1980 and 2000 as the millennial generation following the work of Newbold and Scott (2017). Millennials are more likely to bike, walk, and use transit (Brown et al., 2016; Davis et al., 2012; Grimsrud & El-Geneidy, 2014; Newbold & Scott, 2018) and less likely to drive (Klein & Smart, 2017; Kuhnimhof et al., 2012; McDonald, 2015) than earlier generations. Studies also found millennials to be multimodal, often owning multiple mobility tools and choosing transportation modes based on their trip purpose and mode preference (Azimi et al., 2021; Circella et al., 2017; Delbosc & Nakanishi, 2017; Ralph, 2017). Compared to older adults, they also own fewer automobiles, use them less (Klein & Smart, 2017; Zhong & Lee, 2017), and are less likely to hold a valid driver’s license or are more likely to delay obtaining a driver’s license (Davis et al., 2012; Hjorthol, 2016). On the other hand, older adults (65 years and older) are highly auto-dependent (Scott et al., 2009), and some studies anticipate that they will remain so in the future (Newbold & Scott, 2018).

However, this does not necessarily mean that millennials are less auto-oriented as most of those studies were conducted based on data before millennials were in the workforce. Studies based on more recent data that capture millennials in the labor force indicate that they are not as multimodal and sustainable mode-oriented as anticipated, suggesting that millennials are increasingly becoming auto-oriented and showing travel patterns and choices similar to their preceding generations (Garikapati et al., 2016; Klein & Smart, 2017; Krueger et al., 2020; Y. Lee et al., 2020; Newbold & Scott, 2017, 2018).

While exploring the reasons underlying why millennials have postponed their adoption of previous generations’ travel patterns, studies suggest that millennial’s delayed independent lifestyles as the main reason which is characterized by living with their parents in their twenties and thirties, pursuing higher education, entering the full-time workforce later, and delays in forming new households and having children (Delbosc & Nakanishi, 2017; Garikapati et al., 2016; Polzin et al., 2014). In addition, millennials’ preference for urban living and their exposure to the 2007–08 economic recession are suggested as the main factors behind the decline of their automobility, which may have contributed to differences, compared to older adults, in activity-travel patterns, residential location, and lifestyles (Delbosc & Ralph, 2017; Pendyala et al., 2019; Polzin et al., 2014).

One important policy question in the last decade was whether millennials’ less-auto-oriented behavior of the past will persist as they age (X. Wang, 2019; K. Wang & Wang, 2021). Recent studies using longitudinal data from North America show that this is not the case, although some changes can be expected (e.g. Newbold & Scott, 2018; Ralph, 2017; X. Wang, 2019). Although the percentage is still less than their preceding generations, recent studies have found a significant portion of millennials marry at earlier ages, often live in single-family homes, commute alone, live in suburban locations, and rely on personal automobile as their common mode of transport similar to their preceding generations (Circella et al., 2017; Lavieri et al., 2017; Ralph, 2017). In the United States, 30% of the millennials were found to be living with a spouse and child in 2019, whereas the percentage was 40% for Generation X (those born approximately between 1965 and 1979), 46% for baby boomers (those born approximately between 1946 and 1964), and 70% for the Greatest Generation (those born before 1946) when they were the age millennials are now (Barroso et al., 2020). Blumenberg et al. (2019) found that although young adults in the United States are more likely to live in urban areas compared to older adults, the number of young adults moving to the suburbs is growing. The researchers concluded that their analysis does not support the hypothesis that young adults are abandoning suburban living for city life, which is supposed to impact their travel behavior in a more sustainable way than that of previous generations.

Studies based on the General Social Survey (GSS) on Time Use in Canada argued that millennials will increasingly have similar automobility profiles to previous generations in terms driver’s license possession and number of auto trips (Newbold & Scott, 2017, 2018). In their study, Giallonardo (2017) argued that millennials’ automobile use patterns could not be explained by socioeconomic and demographic characteristics to the same extent as those of Generation X – suggesting a significant cohort effect or the effect of the overall life experiences, social conditions and circumstances experienced by the millennial generation to define their auto use in the in the Greater Toronto and Hamilton Area.

Using a sample of millennials and older adults in Hamilton, Ontario, this study extends current work by comparing the automobility behavior of millennials and older adults (65 years and older). Automobility behavior is defined according to four attributes related to automobile use: having a valid driver’s license, number of automobiles in the household, using automobile as a common mode of transport, and using an automobile as a driver. Hypotheses explored concern whether the two generations’ attitudes and preferences toward transportation modes and residential location characteristics, in addition to their living arrangements and sociodemographic characteristics, affect their automobility behavior and whether any differences exist in how these factors impact each generation’s automobility behavior.

2. Why do we need a generational lens to explore automobility behavior?

Canadians are aging rapidly. By 2031, nearly one in four Canadians will be 65 years or older (Statistics Canada, 2017a), indicating the need for policy interventions in different sectors such as transportation, housing, social services, and healthcare. On the other hand, millennials, represent 25% of Canada’s population (Statistics Canada, 2017a). As unique travel behavior traits exist among generations, transportation planners and policymakers must understand how travel behavior will evolve into the future to inform transport infrastructure investment decisions and policy making (Garikapati et al., 2016; Pendyala et al., 2019). Although recent studies claim that the millennial generation’s travel patterns might not as distinct from older generations as previously thought, their travel patterns are still expected to be more sustainable compared with those of previous generations. Even focusing only on these two generations (millennials and older adults), the need for generation-specific transportation policy is noticeable. For example, for older adults, provision of age-friendly transportation options is needed to support active ageing, including ensuring their access to health and social facilities and keep them remain engaged within their community. On the other hand, for millennials, measures should be taken to keep them engaged within their current sustainable travel pattern and reduce the likelihood of their increased automobile dependence in future.

In contrast to millennials, the travel behavior of older adults (65 years and older) is characterized by greater automobile use and auto-dependency (Hough et al., 2008), higher rates of holding a driver’s license, and limited use of transit (Chudyk et al., 2017; Fordham et al., 2017; Newbold & Scott, 2017). Automobility behavior among older adults is influenced by declining health with aging, with the potential for driving cessation (Moniruzzaman et al., 2013; Siren & Haustein, 2013; Yang et al., 2018). The death of a partner also influences older adults’ automobility behavior, especially among older females where a change from car to public transit use has been noted (Ahern & Hine, 2012; Mollenkopf et al., 2011). However, due to health and safety issues, including driving cessation among older adults, alternatives such as appropriate age-friendly transport infrastructure and services and accessible mobility environments should be available (Cui et al., 2017; Mercado et al., 2010) to avoid mobility loss, social exclusion, and disempowerment (O’hern & Oxley, 2015; Pantelaki et al., 2020; Sheller, 2004).

On the other hand, millennials’ current travel needs and patterns are seemingly different compared to older generations. However, studies (e.g. Busch-Geertsema & Lanzendorf, 2017; Y. Lee et al., 2020; Newbold & Scott, 2018; Ralph, 2017; X. Wang, 2019) suggest that millennials’ sustainable travel behavior will change to a greater extent compared to their current state and will follow (in some cases, already following) the same travel patterns of older generations as they navigate transitions through their life course, such as family formation and job change. For example, Blumenberg et al. (2016) study suggested that a change in employment status is a significant factor behind young adults’ change in travel behavior. They also stated that although young adults have traveled less distance (in terms of daily person-kilometers of travel) than preceding generations during their early adulthood, we should not expect them to behave the same at later stages of their lives once they achieve life-course milestones. K. Wang and Wang (2021) and K. Wang and Akar (2020) suggested that millennials’ life-course milestones have a higher contribution to their driving distances than any other factors. Other studies also suggested that millennials usually follow previous generations’ travel patterns when their life-course milestones, such as family formation or extension, residential moves to suburban or exurban areas, rise in income, and change in personal preferences, are achieved (Circella et al., 2017; Delbosc & Nakanishi, 2017; Hjorthol, 2016; Klein & Smart, 2017; Y. Lee et al., 2020; Newbold & Scott, 2018; Ralph, 2017; X. Wang, 2019). Several studies that adopted the mobility biography (an approach to explore changes in travel behavior over the trajectories of life events) to explore the intersectionality of life course and travel behavior of millennials suggest that life-course milestones and childhood experiences influence millennials’ travel behavior (e.g. Delbosc & Nakanishi, 2017; Konietzka & Neugebauer, 2023; Van Acker et al., 2020).

According to Brown et al. (2016), many millennials have stayed, for an extended period, in urban neighborhoods with good transit services during their adulthood, whereas older North Americans grew up under rapid post-World War II industrialization and have spent their lives in a society characterized by automobility and long-distance travel (Coughlin, 2009). Most older adults also grew up and currently live in suburban and rural settings where transportation systems are predominantly auto-oriented (Rosenbloom, 2012). They prefer to ‘age-in-place’ (Pruchno, 2012) indicating a reluctance to change residential location. In the United States, older adults living in urban areas are unsurprisingly more transit-oriented than their suburban counterparts (J. S. Lee et al., 2014), whereas in Canada, even after living in dense neighborhoods, older adults prefer to drive (Turcotte, 2012), and driving is likely to remain their primary mode of transport in the future (Newbold & Scott, 2018). The findings of Smart and Klein (2018) suggest that individuals who have been exposed to and used better quality public transportation services during their youth are more likely to maintain sustainable transportation choices during the later stages of their lives. Thus, compared to older adults, millennials are expected to make more sustainable transportation choices in the future.

Millennials have also been exposed to rapid technological evolution while growing up more so than any previous generation – a factor that may have played a distinct role in shaping the cohort’s lifestyle, preferences, values, and attitudes (McDonald, 2015). Millennials are more technology-oriented than older generations, including their greater use of technology-induced transport support solutions such as smartphone apps and ride-sharing options (e.g. Uber, Lyft) (Alemi et al., 2018; Circella et al., 2017; Jamal & Habib, 2020; Jamal et al., 2021; Y. Lee et al., 2020; K. Wang & Akar, 2020). In recent years, studies found that the use of information and communications technologies (ICT) is an important factor in shaping millennials’ travel behavior (Hong & McArthur, 2019; K. Wang & Wang, 2021).

A substantial portion of the millennial cohort is already in the workforce, and others will enter soon. As they make up the largest living age cohort in North America (22% and 25% in the United States and Canada, respectively), their travel behavior will have a large impact on future transportation options, related facilities, services, and infrastructure, and therefore, travel demand (Circella et al., 2017; Myers, 2016). On the other hand, older adults are retiring, and their trip patterns mostly consist of non-work trips. Alemi et al. (2018) found that differences exist between millennials’ and previous generations’ travel behavior, and this difference is also noticeable when they analyzed the generational cohorts by urban and non-urban residency. The findings of K. Wang and Akar (2020) suggested that millennial automobility behavior is strongly associated with residential location choice and life-cycle events rather than an increase in wealth and income like older generations.

After an extensive review of the current literature on millennials and older adults’ travel behavior, Jamal and Newbold (2020) concluded that differences exist between these two generations’ lifestyles, living arrangements, residential locations, and attitudes and preferences toward transportation modes and the environment, which, consequently, have the potential to differentially impact their travel behaviors. Thus, it is important to develop an understanding of the travel behaviors among different generational cohorts and quantify their impact on current and future travel demand from diverse policy perspectives such as transportation infrastructure development, urban form interventions, provision of age-friendly transport options, transport facilities in residential locations, and promotion of e-commute and shared mobility options from congestion and environmental perspectives.

Although a large literature has explored millennial and older adults’ travel behavior separately, several gaps exist. First, in terms of attitudes and preferences towards transport options, studies on young adults have emphasized attitudinal questions (e.g. attitudes towards automobile, environment, etc.), whereas, for older adults, studies mostly considered how their perception and experiences with different travel options influence their travel behavior (Jamal & Newbold, 2020). Based on the available literature, it is difficult to understand different generations’ travel behavior based on the same attitudinal determinants, as different studies considered different variables for different generations.

Second, although the role of sociodemographic attributes are widely recognized within the transportation literature, there is a paucity of research concerning how living arrangements, attitudes, and preferences influence travel behavior. To date, most studies have used household travel surveys that rarely capture living arrangements, the roles of attitudes and preferences towards transportation options and preferred residential location characteristics. Only a limited number of available studies suggest significant impact of lifestyles and living arrangements on older adults’ and millennials’ travel behavior (e.g. Ahern & Hine, 2012; Brown et al., 2016; McDonald, 2015; Moniruzzaman et al., 2013).

Third, most previous studies focused either on millennials/young adults’ or older adults’ travel behavior. Only a few studies explored generational differences in the same geographic and temporal contexts (e.g. Circella et al., 2017; Newbold & Scott, 2018; X. Wang, 2019; K. Wang & Akar, 2020; K. Wang & Wang, 2021) – however, their focus was at the national level, not the city or local level. Although such studies were conducted in developed countries, geographic contexts can differ locally in terms of demographic composition and land-use patterns. Even available transport options and infrastructure can differ from one area to the next, influencing the residents’ travel behaviors differently (Krueger et al., 2020). Therefore, context-specific research is needed to develop more relevant insights into the travel behavior of any geographic location and formulate appropriate policy.

This study addresses these gaps by simultaneously focusing on millennials and older adults and how different factors may impact their automobility behavior differently. First, the study contributes to the literature through a survey designed to capture millennials’ and older adults’ automobility behavior based on the same attributes using Hamilton, Ontario as a context. Second, along with sociodemographic characteristics, the study explores the role of living arrangements, attitudes and preferences towards transportation options, and residential location characteristics to explore these two generations’ automobility behavior.

3. Study area, data, and method

Hamilton, with a 2021 population of 569,353 and a land area of 1,117 square kilometers (Statistics Canada, 2022), is located at the western end of Lake Ontario. Based on the 2016 census, millennials composed 25% of the city’s population while 18% of Hamiltonians were 65 years and older (older adults) with an additional 6% between 60 and 64 years old (Statistics Canada, 2017b), indicating that in 2020, approximately 24% of the city’s population would be 65 years and older. These statistics suggest that nearly similar numbers of millennials and older adults reside in Hamilton. As both cohorts represent a similar percentage of the population in Hamilton, a similar number of respondents were recruited from each cohort when the survey was administered by Dynata Research¹ between October and November 2019. Dynata uses a survey panel consisting of individuals (i.e. survey panelists) willing to participate in surveys on topics in exchange for incentives. The surveys are invitation only and a sample based on a client’s criteria, closely matched to the recent census and social benchmark of the geographic area considered (Dynata, n.d.), is delivered. Although there is a possibility of low generalizability, convenience sampling offers time savings and less uncertainty in data collection, and experienced survey respondents who are usually familiar with different survey formats and questions (Abotalebi et al., 2020). The panel nature of convenience samples is also useful in terms of testing social and scientific theories and formulating hypotheses for future studies (Coppock & McClellan, 2019).

For a sample of 100 millennials and 100 older adults (aged 65+) living in Hamilton, the survey collected information on travel characteristics, sociodemographic characteristics, living arrangements, level of technology use, lifestyle preferences, and attitudes toward transportation options. Sociodemographic characteristics included age, gender, income, education, employment, and marital status. A range of automobility behavior-related questions were collected – namely, household automobile ownership, automobile usage as a driver or passenger, driver’s license possession, medical conditions restricting driving, and frequency of driving. The survey also asked respondents about their most frequently used transport mode and frequency of using public transit and shared mobility services (e.g. ride sharing, car sharing). Lifestyle preferences and attitudinal questions gathered individuals’ attitudes towards transportation choices (e.g. automobile, transit, active modes) for their typical weekday trips, why and when different modes are used and why they are not used. A total of 27 lifestyle and transportation mode-related attitudinal and preference-based questions were asked on a 3-point Likert scale: Agree – Neither agree nor disagree – Disagree.

Additionally, the survey contained 12 statements concerning attitudes and preferences toward driving using the same Likert scale. The survey collected information on reasons for residential location choice (13 statements) and preferred characteristics of the residential location (28 statements). A more detailed analysis of the attitudes and preferences toward transportation modes and residential location characteristics is available in the study by Jamal et al. (2022).

Table 1 presents sociodemographic profiles of the survey respondents, according to generation. In terms of gender ratio, 55% of sampled millennials and 41% of sampled older adults were male whereas, the male-female ratio of Hamilton’s population in 2016 was 51:49 and 44:56 for millennials and older adults, respectively (Statistics Canada, 2017b). A comparison of demographic characteristics and driver’s license possession between the sample and 2016’s Transportation Tomorrow Survey² is provided by Jamal et al. (2022) and concludes that the Hamilton sample is reasonably representative of Greater Toronto and Hamilton Area’s millennials and older adults in terms of gender, student status, employment, and drivers’ license possession. As shown in the table, millennial respondents are mostly students and employed compared to older adults who are mostly retired. Sixty-five percent of millennials were single at the time of the survey. Forty-four percent of older adults lived alone, whereas almost one-third (34%) of millennials lived with their parents. A higher number of millennials (19%) lived with roommates or similarly aged people compared to older adults (2%). Similar proportions of millennials (83%) and older adults (79%) held valid driver’s licenses. Almost the same proportion of individuals from both cohorts used automobiles as their common mode of transport in 2018, the previous year of the survey (63% millennials and 66% older adults), and were drivers (69% millennials and 70% older adults). The average number of automobiles was higher for households with millennials (1.94) compared to older adults (1.13).

Table 1. Sociodemographic profiles of millennials and older adults.

	Millennials (n = 100)	Older Adults (n = 100)
Gender
Male	55	41
Female	45	59
Student Status (yes)	33	4
Employed (yes)	73	7
Retired (yes)	0	89
Marital Status
Married/Living common-law	31	45
Widowed/Separated/Divorced	3	45
Single (never married)	65	6
Prefer not to answer	1	4
Living Arrangement
Alone	9	44
With parents	34	1
With partner/spouse	18	33
Living with partner/spouse and children	15	4
Living with children	4	6
Living with roommates/similar age people	19	2
Prefer not to answer	1	10
Valid driver’s license* (yes)	83	79
Most common mode of transportation in 2018
Automobile	63	66
Other mode	37	34
Using automobile as a driver (yes)	69	70
Number of automobiles in the household
0	18	21
1	17	51
2	33	22
3	17	06
4	15	0
Average number of automobiles in the household	1.94	1.13

* This includes graduated driver’s licenses where an individual is permitted to drive alone with some restrictions, e.g. zero blood alcohol level. – General Social Survey 2011, Cycle 24

To develop a nuanced understanding of factors influencing automobility behavior, binomial and ordinal logistic regressions were used to reveal factors affecting the automobility behavior of millennials and older adults. We tested all relevant variables collected in the survey (e.g. sociodemographic characteristics, trip attributes, living arrangements, attitudes, preferences, etc.) as to their relationships to the dependent variables. A series of interaction effects between cohort (millennials) and selected independent variables were also tested. The interaction variables were used in the models to reveal whether living arrangements, attitudes, and preferences impact differently the automobility behavior of millennials and older adults. Variables included in the final models were statistically significant (10% significance level), except for a few that were retained as their cohort interactions significantly impacted the dependent variable in question, offering valuable insights regarding cohort effects. A Variance Inflation Factor (VIF) test was conducted for all models to check the magnitude of multicollinearity among the predictors. Only predictors with VIF values up to five were considered.

Binomial logistic regression models explored factors associated with having a valid driver’s license, using automobile as a common mode of transport, and using an automobile as a driver. Ordinal logistic regression was used to explore the covariates associated with the number of automobiles in the household. Factors examined included living arrangements, travel-related attributes, attitudes and preference toward transport options and residential characteristics, and sociodemographic characteristics.

4. Results

Table 2 reports the binomial logistic regression results for having a valid driver’s license. Regardless of generation, those who self-rated their health as ‘very good or excellent’ have a higher likelihood of holding a valid driver’s license. Also, those who self-rated their technology adoption as ‘high’ are more likely to possess a valid driver’s license. Auto users are more likely to have a valid driver’s license compared to non-auto users, and a higher number of vehicles in the household increases the likelihood of holding a valid driver’s license.

Table 2. Binomial logistic regression analysis of having a valid driver’s license.

Variables	Coefficient	P-value	Odds ratio
Intercept	−2.490	0.005	0.083
Sociodemographic characteristics
Millennial	−1.531	0.280	0.216
Self-rated health (very good/excellent)	1.288	0.042	3.626
Self-rated level of technology/smart device adoption (high)	2.418	0.014	11.219
Travel related attributes
Most common mode of transportation (automobile)	3.390	0.000	29.674
Number of vehicles in the household	0.906	0.010	2.473
Attitudes toward transportation modes and preference toward the residential location
I prefer to walk rather than drive whenever possible (agree)	−1.788	0.057	0.167
Millennials × I prefer to walk rather than drive whenever possible (agree)	4.369	0.004	78.954
I like driving (agree)	2.926	0.002	18.644
Millennials × I like driving (agree)	−2.503	0.066	0.082
McFadden pseudo-R-squared	0.604
AIC	98.358
Number of observations	200

Regarding attitudinal variables, regardless of generation, those who prefer to walk rather than drive whenever possible are less likely to hold a valid driver’s license whereas those who agreed that they like driving are more likely to hold a valid driver’s license.

In terms of cohort interaction, the results indicate that among millennials, those who agreed that they prefer to walk rather than drive whenever possible are most likely to hold a valid driver’s license, and millennials who did not agree with the statement are least likely to hold a valid driver’s license. On the other hand, older adults who agreed with the statement have the lowest likelihood of holding a valid driver’s license compared to older adults who disagreed and millennials in general. Millennials who disagreed that they like driving have the lowest propensity to hold a valid driver’s license whereas older adults who agreed with the statement have the highest likelihood of holding a valid driver’s license.

The number of automobiles in the household was modeled using ordinal logistic regression, with results presented in Table 3. In contrast to the available literature, millennial households are more likely to have more automobiles than older adults. Also, student status is positively associated with number of automobiles in the household. Households with more adults (above 16 years) are more likely to have a higher number of automobiles. Low-income households of both generations are more likely to have fewer automobiles than higher-income households. Individuals who primarily use the auto for their daily trips are more likely to reside in households with more automobiles than those who do not use automobiles regularly. Regardless of generation, households of those who mentioned that they need a car to do many things are more likely to have a greater number of automobiles.

Table 3. Ordinal logistic regression model of the number of automobiles in the households.

Variables	Coefficient	P-value	Odds ratio
Sociodemographic characteristics
Millennials	2.269	0.000	10.836
Household income (no income or less than CAD 40,000)	−1.022	0.010	0.340
Student (full time)	1.916	0.007	6.885
Number of adults (above 16 years old) in the household	0.667	0.000	1.900
Living with a partner/spouse	0.508	0.233	1.554
Millennials × Living with a partner/spouse	−1.554	0.026	0.210
Living in an apartment	−0.557	0.200	0.602
Millennials × Living in an apartment	−1.316	0.051	0.246
Travel related attributes
Common mode of transportation (automobile)	1.525	0.000	4.043
Attitudes toward transportation modes and preference toward the residential location
I need a car to do many things I like to do (agree)	0.900	0.027	2.683
It is easy to use public transport where I live (agree)	−0.090	0.844	0.943
Millennials × It is easy to use public transport where I live (agree)	−1.257	0.046	0.255
Threshold coefficients:
0|1	0.937	0.049	2.398
1|2	3.519	0.000	30.782
2|3	5.798	0.000	291.390
3|4	7.551	0.000	1622.246
Log-likelihood (null)	−298.342 (df=4)
Log-likelihood	−209.9732 (df=16)
AIC	451.9465
Number of observations	200

In terms of cohort interactions with living arrangements, households of millennials living with a partner/spouse have fewer automobiles compared to millennials not living with a partner/spouse. In fact, households of millennials who are not living with a partner/spouse have the highest propensity of having a higher number of automobiles compared to all other households. Focusing only on older adults, results suggest that those with a partner/spouse are more likely to have a higher number of automobiles in their household compared to older adults living without a partner/spouse.

In terms of dwelling type, millennials living in apartments have fewer automobiles in their households compared with millennials who live in other types of dwelling. In terms of effect size, millennials living in other types of dwellings have the highest propensity to have a higher number of automobiles in their household among all other groups. Among the households of older adults, those who live in other types of dwellings are more likely to have more automobiles compared with those who live in apartments.

In terms of cohort interactions with attitudinal statements, millennials who did not agree that it is easy to use public transit where they live have the highest propensity to have a higher number of automobiles in their household compared with millennials who mentioned the opposite and older adults in general. Focusing on the households of older adults, those who agreed with the statement have fewer automobiles compared to those who did not agree that it is easier to use public transport from their residence.

Table 4 shows the results of the binomial logistic regression for auto use. The dependent variable is using an automobile as the most common mode of transport in the past year (in 2018). Results suggest that females are less likely to use an automobile compared to males, regardless of generation. Those who do not have an automobile in their household are, not surprisingly, less likely to use automobiles. On the other hand, those living alone are more likely to use automobiles than those in multi-person households where other transport options, including automobiles, would more likely be available. Similarly, those who hold a valid driver’s license and do not use public transit at all are more likely to use the auto as their most common mode of transport.

Table 4. Binomial logistic regression analysis of automobile use (dependent variable = most common mode of transportation in 2018: automobile).

Variables	Coefficient	P-value	Odds ratio
Intercept	−1.755	0.151	0.173
Sociodemographic characteristics
Millennials	0.008	0.993	1.008
Gender (female)	−2.204	0.000	0.110
Living alone	1.218	0.077	3.380
Travel related attributes
No/zero vehicle in the household	−2.15	0.016	0.116
Valid driver’s license possession	2.544	0.002	12.733
Transit use (not at all)	1.965	0.001	7.138
Attitudes toward transportation modes and preference toward residential location
I prefer to walk rather than drive whenever possible (agree)	1.263	0.016	3.537
I’m not interested in driving (agree)	−2.595	0.003	0.075
Taking public transport, walking and cycling meet my travel needs (agree)	0.033	0.972	1.033
Millennials × Taking public transport, walking and cycling meet my travel needs (agree)	−2.699	0.021	0.068
I prefer to have lots of off-street parking (garages or driveways) in the neighborhood (agree)	0.397	0.567	1.487
Millennials × I prefer to have lots of off-street parking (garages or driveways) in the neighborhood (agree)	1.854	0.081	6.386
McFadden pseudo-R-squared	0.563
AIC	140.49
Number of observations	200

In terms of attitudes, those who are not interested in driving are less likely to use auto as their common mode of transport. Surprisingly, those who stated that they prefer to walk rather than drive are more likely to be auto-users, although this may reflect a decision based on trip distance.

Turning to cohort interactions with attitudinal factors, millennials who agreed that taking public transport, walking, and cycling meet their travel needs, have the lowest likelihood to use an automobile as the most common mode of transport compared to older adults in general and millennials who disagreed with the statement. Although older adults who agreed with taking public transport, walking, and cycling to meet their travel needs have a higher likelihood of using the automobile compared to older adults who disagreed, the effect size is lower compared to that of millennials (−1.722 vs −4.413, respectively). Regarding preferred characteristics of residential location, millennials preferring to live in neighborhoods with lots of off-street parking (garages or driveways) are more likely to use the automobile as a common mode of transport compared to millennials not agreeing with the statement, and older adults. On the other hand, older adults who did not agree with the statement have the lowest propensity to use the automobile as a common mode of transport compared to those who agreed and millennials.

Table 5 highlights the correlates of using the automobile as a driver as opposed to being a passenger. Regardless of generation, those who live alone are more likely to be drivers while traveling by automobile. Similarly, those who do not use transit at all are more likely to be a driver. On the other hand, low-income individuals and females are less likely to be a driver. Regardless of generation, living in an apartment is negatively associated with being a driver while traveling.

Table 5. Binomial logistic regression analysis of using the automobile as a driver.

Variables	Coefficient	P-value	Odds ratio
Intercept	6.028	0.000	415.021
Sociodemographic characteristics
Millennials	1.108	0.348	3.029
Household income (no income or less than CAD 40,000)	−2.566	0.004	0.077
Gender (female)	−5.236	0.000	0.005
Living alone	3.622	0.000	37.41
Living in apartment	−1.118	0.087	0.327
Travel related attributes
Transit use (not at all)	1.169	0.077	3.219
Attitudes toward transportation modes and preference toward residential location
I’m not interested in driving (agree)	−3.671	0.000	0.025
I don’t need a car to get around (agree)	−4.100	0.000	0.017
Closeness to public transit is important to me while selecting residential location (agree)	−1.601	0.011	0.202
I can rely on other people to drive me places (agree)	−0.791	0.315	0.453
Millennials × I can rely on other people to drive me places (agree)	−3.224	0.022	0.040
I prefer to have lots of off-street parking (garages or driveways) in the neighborhood (agree)	1.200	0.138	3.321
Millennials × I prefer to have lots of off-street parking (garages or driveways) in the neighborhood (agree)	1.985	0.099	7.728
McFadden pseudo-R-squared	0.657
AIC	113.08
Number of observations	200

In terms of attitudes, those not interested in driving are less likely to be drivers while using an automobile for travel. Similarly, those who mentioned that they do not need a car to fulfill their travel needs are less likely to be drivers. Also, those who emphasized that closeness to public transit is important to them while selecting residential location are less likely to be drivers.

Regarding cohort effects on attitudes, millennials and older adults who cannot rely on other people to drive them places have higher likelihoods of using an automobile as drivers, with millennials who disagree with the statement having the greatest propensity to be a driver. In terms of preferred residential characteristics, millennials who prefer to have lots of off-street parking (garages or driveways) in the neighborhood have the greatest propensity to be a driver compared to millennials who prefer the opposite, and older adults.

5. Discussion, limitations, and conclusion

5.1. Discussion

Exploring travel behavior through a generational lens can help transportation planners and policymakers make informed decisions about infrastructure and services such as investing in age-friendly transportation infrastructure and programs, providing active transportation, expanding public transportation networks, or initiating affordable transit policies. This study explored hypotheses related to whether millennials’ and older adults’ attitudes and preferences toward transportation modes and residential location characteristics, in addition to their living arrangements and sociodemographic characteristics, affect their automobility behavior and whether any differences exist in how these factors affect each generation’s automobility behavior.

The results of this work raise several generation-specific hypotheses and questions that need further testing with larger data sets and in different contexts. First, does gender and generational differences in living arrangements impact automobility behavior? Our results suggest that females are less likely to be auto-oriented than their male counterparts. Similarly, living arrangements (i.e. living alone or with a partner or living in an apartment) appear to impact the number of automobiles in households, automobile use as a common mode of transportation and using automobile as a driver. Both predictors could be associated with the ability to access and share different mobility tools along with the geographic location of the household relative to public transit.

Second, does life-cycle stage impact automobility behavior? One of the results that differs from previous studies is that students and millennials are more likely to reside in households with a higher number of automobiles than older adults. A possible reason could be that because of their delayed life-cycle and economic conditions, many of today’s millennials are still living with their parents (Delbosc & Nakanishi, 2017). In this study, 34% of the millennials were living with their parents. Therefore, the number of automobiles in the households may be reflective of living with their parents. We also found that the number of adults living in households is positively related to the number of automobiles in the households and the descriptive analysis suggests that the average number of automobiles is higher in households with millennials compared with older adults. When millennials form a household and start living separately from their parents, they might be less likely to have automobiles or drive less (De Vos & Alemi, 2020; Delbosc & Ralph, 2017; Garikapati et al., 2016; Polzin et al., 2014). Our study supports this, with the results suggesting that millennials who live with a partner/spouse and are living in apartments are likely to reside in a household with fewer automobiles whereas millennials living without a partner/spouse and living in other types of dwellings (except apartments) have the highest propensity to reside in a household with a higher number of automobiles.

Third, how important are attitudes and preferences toward transportation modes and residential location characteristics as an influence on automobility behavior? Similarly, is there a difference in how they influence each generation’s automobility behavior? Our results suggest that there is an association between automobility behavior with attitudes and preferences towards transportation modes and locational characteristics and also there is a difference in their extent of impact (i.e. positive/negative association). For example, attitudes and preferences towards sustainable travel behavior (e.g. ‘I prefer to walk rather than drive whenever possible’, ‘I’m not interested in driving’, ‘Taking public transport, walking and cycling meet my travel needs’, ‘I don’t need a car to get around’, ‘Closeness to public transit is important to me while selecting residential location’) may differentially influence both generations. Millennials’ positive attitudes and preferences toward sustainable travel behavior and living near transit influence them to be less auto-oriented compared to older adults. On the other hand, the preference towards off-street parking expressed by millennials in their residential neighborhood is likely to increase their auto-dependency compared to older adults.

Similarly, does each generation show similar automobility behavior based on the same attitudes and preferences? Results of the cohort interactions show that older adults who disagreed that they like driving are more likely to hold a valid driver’s license compared to millennials. Also, older adults who agreed that taking public transport, walking, and cycling meet their travel needs are more likely to use an automobile as a common transportation mode compared to millennials who agreed with the statement and older adults who disagreed. For the number of automobiles in the household, older adults who agreed with the statement have fewer automobiles compared with millennials who agreed with the statement. These counterintuitive findings suggest that depending on the attitudes and preferences, there is a group of older adults who are more mobile and multimodal compared to other groups and in addition to automobiles, they use non-automobile modes. The possible underlying reasons could be unavailability or unaffordability of an automobile to older adults (Nobis, 2007), preference towards being multimodal, or preferring modes that are accessible and suit the travel purpose.

Overall, the study results suggest that compared to older adults, millennials’ interactions with living arrangements, attitudes, and preferences influence automobility behavior to a greater extent – meaning that millennials’ automobility behavior is more likely to be influenced by their living arrangement, attitudes towards transportation modes, and preferences towards locational characteristics supporting standpoints of McDonald (2015), Giallonardo (2017), Jamal and Newbold (2020), K. Wang and Akar (2020), and Konietzka and Neugebauer (2023).

Fourth, are there differences between personal preferences and actual behavior regarding automobility behavior of both cohorts? The results suggest that millennials who did not agree with the statement that they prefer walking over driving have a higher likelihood of not holding a valid driver’s license. On the other hand, regardless of generation, those who agreed with the statement show a higher likelihood of using automobiles as a common mode of transportation. Although we cannot explain this mismatch specifically in our context, De Vos (2018) suggested four possible reasons behind the mismatch between the preferred transport mode and actual transport modes choice such as a lack of skills to use the preferred mode, a lack of transportation options, the presence of transportation barriers, and the presence of travel habits with certain modes. On the other hand, Sheller (2004, p. 22) suggests that despite being aware of the environmental and health benefits of sustainable transportation options (e.g. transit, cycle, walk, etc.), ‘too many people find automobiles too comfortable, enjoyable, exciting, even enthralling’. The underlying reason is that the automobile has been deeply embedded in developed countries and reinforced by physical structure, specifically road networks and living environments, along with gender roles, family formation, urbanism, national identity, and transnational processes (Sheller, 2004).

Based on the study, we can suggest that although there are several similarities in how different factors affect the automobility behavior of millennials and older adults, there are also differences between and within the effects. As noted by Sheller (2004), ‘auto culture’ or ‘auto dependency’ is a complex outcome of housing conditions, engagement in labor markets, changing patterns of gender role and family formation, and the place of transportation in values, perceptions, preferences, and attitudes that form a strong emotional component. Similarly, our analysis suggests that depending on their living arrangements, attitudes, and preferences, each generation’s automobility behavior can differ from one another.

Based on our investigation, we can conclude that millennials in Hamilton are not less auto-oriented than older adults. Millennials living with parents may have access to their family-owned or leased automobiles compared to those who are living alone. In our study, 65% of the millennials are single and 34% are living with their parents. In Canada, 34.7% of millennials live with their parents (Statistics Canada, 2017c). In future, automobility behavior may be further complicated by higher education, recession and COVID-19. Many older millennials started their careers during the Great Recession of 2008 to 2009, facing severe employment and a housing market crisis (Delbosc & Ralph, 2017; Mawhorter, 2017). Since 2020, younger millennials are facing economic instability due to COVID-19 and another housing market crisis. According to a recent report by Statistics Canada (2020), millennials are facing higher consumption expenditures than preceding generations due to high housing and utility costs. The report also stated that during COVID-19, employment rates among millennials fell more sharply compared to older generations in service-producing industries as millennials tend to comprise a higher share of the workforce in those industries. In addition, millennials will soon no longer be in the ‘young adults’ category and may be moving out of high-density urban neighborhoods after achieving life-course milestones (K. Wang & Akar, 2020) – which is also uncertain for millennials in Hamilton due to the sudden price escalation in the housing market during the COVID-19 period (The Financial Post, 2020; The Hamilton Spectator, 2021). Eventually, these events will impact their behavior both as a consumer in the economy and commuter along with discretionary travel. Their automobility behavior may change in the future again depending on other factors as well. Although further exploration with larger samples is needed, results of our study suggest that millennials’ automobility behavior depends on their sociodemographic, living arrangement, and attitudes towards travel and locational preferences. Even if we assume that their attitudes and preferences will remain the same in the future, they may become more auto-oriented compared to now due to changes in their sociodemographic and living arrangements. Therefore, planners and policymakers should take initiatives to help them make more informed decisions on sustainable mode choices by providing information, incentives, appropriate infrastructure, and accessible services.

5.2. Limitations

The study is limited methodologically by its small sample size and cross-sectional nature, with the need to explore millennials’ and older adults’ travel behavior based on a larger and multi-year dataset. The sample size restricts the study’s potential to explore millennials’ and older adults’ travel behavior by applying advanced analytical methods such as structural equation modeling (SEM) to explore the interrelationships among the four automobility characteristics. To be specific, due to the sample size, the study was unable to explore the interrelationships among the outcome variables such as possession of a valid driver’s license, using an automobile as a common mode of transport, using an automobile as a common mode of transport and using an automobile as a driver along with the predictor variables that impact the outcome variables. SEM may offer a more complex relationship as driver’s license may mediate a household’s auto ownership and individuals’ use of automobile as drivers.

Second, in terms of the questionnaire, we used a 3-point Likert scale to capture attitudinal and preference-related variables. Our intention was to use them as predictor variables and therefore, we collapsed them and used them as dummy variables in our models for ease of interpretation. If ordinal variables are to be used as dependent variables or latent constructs in SEM models, it is recommended to use 7-point Likert scale for reliable results (Taherdoost, 2019).

Third, the study focused on subjective measurements as predictor variables such as attitudes and preferences toward transportation modes and residential location characteristics. Although the survey asked for postal codes, it was optional to respond. Twenty-five percent of respondents did not share their postal codes. As a result, objective measurements such as built-environment-related characteristics and accessibility characteristics were excluded from the analysis given the lack of geographic detail for a significant portion of the respondents.

Fourth, the survey collected information on the number of automobiles in the household. It did not ask who owns the automobile. If we had specific information on whether a millennial or an older adult owned an automobile, we would be able to explore whether millennials’ ownership of automobiles is different or similar to that of older adults. Also, this information would allow us to more explicitly identify whether millennials residing in households with a higher number of automobiles are reflective of their parents’ ownership of automobiles.

Finally, the survey was conducted in the pre-COVID-19 era. The COVID-19 pandemic may have two significant impacts on automobility. First, the pandemic prompted changes in living arrangements, with millennials either delaying moves or returning to live with their parents. Second, many individuals shifted towards technology-oriented substitutes and telework, which impacted travel behaviors. Research has suggested that individuals are shifting towards personal modes of transport (e.g. auto, bicycle, walk) and avoiding transportation modes that need to be shared with others (e.g. transit, ride sharing) due COVID-19 (e.g. Bucsky, 2020; Hensher et al., 2021). Based on their data from 2020 in the United States, Barbour et al. (2021) found that older respondents (above 50 years old) are less likely to telework during the pandemic. In contrast, young adults (below 30 years old) showed a heterogeneous behavior towards telework and willingness to continue teleworking in the post-pandemic period depending on their nationality (American born vs others), number of children in the household, and residential location (Barbour et al., 2021). A future research direction could be to assess the impact of COVID-19 on the travel behavior of different generations in Canada and whether any heterogeneity in travel behavior has been generated within and between generations due to COVID-19.

5.3. Conclusion

This study highlights intergenerational differences in automobility among millennials and older adults using Hamilton, Ontario, as a case study. Being Canada’s 9^th and Ontario’s 3^rd largest city based on population, Hamilton itself can represent many North American metropolitan areas with similar population, land area, climate, land-use patterns, etc. However, each city has distinct characteristics in terms of its socio-cultural, ethnic diversity and geo-political situation along with housing stock and housing markets that can shape travel patterns differently. Also, due to limited sample and panel-based data, results from this study may not be applicable to cities with similar characteristics. Therefore, results of this study should be interpreted with caution in different locations and should be tested further with larger samples.

Ethics approval

Ethics approval is from the McMaster Research Ethics Board (MREB), McMaster University. MREB#: 1854.

Acknowledgments

This research was supported by a grant from the Social Sciences and Humanities Research Council of Canada (SSHRC, # 435-2017-1141). The first author would also like to acknowledge the 3 years Doctoral Fellowship she received from the Social Sciences and Humanities Research Council of Canada (SSHRC).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Social Sciences and Humanities Research Council of Canada [435-2017-1141].

Notes

1. https://www.dynata.com/.

2. A regional household travel survey conducted in the Greater Toronto and Hamilton Area every five years, coinciding with the Canadian census.