Transit to eternal youth: lifecycle and generational trends in Greater Montreal public transport mode share

Abstract

Young people appear to be using public transit more than their predecessors, reversing 20 th century trends, but the importance of such findings depends on whether high transit use persists as these riders age. This paper examines whether transit mode share for commuting trips is increasing; socio-economic and geographic trends are also explored to attempt to determine whether these trends are likely to continue. The study uses repeated cross-sectional origindestination surveys of Greater Montreal (1998, 2003 and 2008). Over 45,000 home-to-work and home-to-school trips are studied for each survey year. A general lifecycle pattern of decreasing transit share with age is apparent within cohorts until individuals reach their early 30s, followed by decades of stability. This pattern appears to hold in recent years, but with higher youth use rates, and it is argued that the higher use will continue as current younger cohorts mature.Suburbanization by those in their early 30s is evident and, along with household composition changes, appears to explain much of the final within-cohort mode share declines before equilibrium. Transit providers might see lasting ridership gains, as those currently in their early 30s and younger replace lower-use cohorts in the workforce, provided service provision keeps pace. Addressing the needs of young people, whose mode choices are comparatively unsettled, should be a priority for transit agencies to ensure higher transit usage in the future.

A tale of two millennials

“…Data source Country City Narrative (Sakaria & Stehfest, 2013) Own survey (online) USA Major metro areas 1 (Oakil et al, 2016) Vehicle registration and SSD data Netherlands National 1 (Thigpen & Handy, 2016) Own survey (online) USA University campus (UC Davis) 1 (Hjorthol, 2016) National Travel Survey Norway National 1 (Rive et al, 2015) Own survey (online) New Zealand National 1 (Grimsrud & El-Geneidy, 2014) O-D survey Canada City (Montreal) 1 (Kuhnimhof, Buehler, Wirtz, & Kalinowska, 2012) German Mobility Panel and Income and Expenditure Survey Germany National 1 (Zipcar, 2013) Own survey (online) USA National 1 (Delbosc & Currie, 2014b) Own survey (intercept) Australia City (Melbourne) 1 (Delbosc, 2016) Licensing statistics Australia and USA National (USA) and State (Victoria) 1 (Delbosc & Currie, 2014a) Household travel survey Australia City ( In addition to methodological causes, Narrative 1 may have more prominence due to its power as a compelling story. This could occur in four ways.…”

“…If you believe the recent flood of research, compared to previous generations millennials are delaying when they get a driving license (Delbosc & Currie, 2013;Hjorthol, 2016), they are less likely to own a car (Hjorthol, 2016;Kuhnimhof, Buehler, & Dargay, 2011;Oakil, Manting, & Nijland, 2016), they are driving less (McDonald, 2015;Kuhnimhof et al, 2011;Klein & Smart, 2017) and in some countries they are using public transport more (Grimsrud & El-Geneidy, 2014;Kuhnimhof et al, 2011;Rive, Thomas, Jones, Frith, & Chang, 2015). There is still a great deal of speculation over whether millennials will continue these sustainable travel habits as they age (Delbosc, 2016;Oakil et al, 2016; Ministry of Infrastructure and the Environment, 2014), but the underlying narrative of the "multimodal millennial" is quite strong.…”

A New Market Segmentation Approach: Evidence from Two Canadian Cities

“…Although much research attempts to elucidate what motivates drivers to switch to taking transit (Abou-Zeidet al 2012;Curtis and Headicar 1997), fewer studies attempt to understand how to maintain and increase ridership among existing transit users. It is important for transit agencies to focus on retaining existing users, as it is known that individuals stop using transit for many reasons, including changes in income, family size, the availability of another mode, as well as reasons related to the quality of service (Evans 2004;Grimsrud and El-Geneidy 2014;Perk et al 2008).…”

Analyzing Student Travel Patterns With Augmented Data Visualizations

“…Further representations include the ability for analysts to use Betaville to visualize with "time scale" factors that allow for clustered narratives to be revealed within specific time frames, such as the relationship between optimal travel time and the location of affordable student housing, and immersive "fly-through" experience of particular itineraries. The integration of Betaville and StoryFacets will allow analysts to see other relationships and patterns that will bring to light narratives outside of the scope of commuting and transportation, to make the most of new levels of availability of current local data, rather than inference from generalized patterns or historical data from elsewhere (8,9).…”