Travel behaviour

The literature suggests that density, diversity of land uses, and neighbourhood design -- Cervero and Kockelman's "3Ds" (1997) -- influence travel behaviour. By putting residents and jobs closer to one another, a high-density, mixed-use urban fabric is believed to decrease the length and number of automobile trips in favour of walking and cycling. As discussed in Section 2.5, this effect is believed to be amplified if the street network is more "accessible."

Building on the previous section, which found that land uses are highly segregated at both the neighbourhood and metropolitan region scales, this section examines the travel behaviour of study area residents. The section concludes with a discussion of potential implications for policies that seek to redirect people from automobiles to public transit, walking, and cycling.

Literature review

A wealth of empirical research on factors influencing travel behaviour has been produced in recent decades. As Crane and Crepeau (1998), Boarnet and Crane (2001), Zhang (2004), and Williams (2005) note, this work has been uneven in its methods and often inconclusive and contradictory in its findings. Where correlations and causal relationships have been discerned, researchers disagree about their strength. Still, they have identified a variety of factors that influence travel behaviour.

Newman and Kenworthy's (1999:101) widely cited correlation between density and energy intensity of transport (a loose proxy for automobile use) was based on a comparison of aggregate values for metropolitan regions. As one moves from "macro" to "micro" analysis, however, the findings become more ambiguous.

A variety of built environment variables have been shown to influence travel behaviour, including density, mix of use, and accessibility. In a statistical analysis of land use and travel behaviour in the Puget Sound region, Frank and Pivo (1994) found that travel mode choice was related to the population and employment density of both the origin and destination of trips, though the relationship is not linear. The number of pedestrian shopping trips increased only when density surpassed 32 residents per hectare at trip origin. Trips to work by single-occupant vehicle decreased only when the density at trip destination was higher than 185 jobs per hectare. Frank and Pivo also found that increased mix of use also correlated positively with walking. In a study of Portland-area neighbourhoods, Greenwald (2003) found that pedestrian trips increased at the expense of automobile trips in neighbourhoods featuring accessible street patterns, but that transit use was unaffected.

Similarly, Cervero and Kockelman (1997) used 1990 travel diary data for San Francisco Bay Area residents in conjunction with indicators of density, diversity of uses, and street configuration for 50 census tracts to demonstrate that higher density, use mixture, and street network connectivity lead to small but statistically significant declines in automobile use and trip length. Kulash et al. (1990) found that more connected street systems may reduce vehicle distance travelled within neighbourhoods, but noted that intra-neighbourhood trips constitute only a small proportion of total trips. Distinguishing between travel to local and distant destinations, Handy (1992) explored the linkage between local-area and metropolitan regional urban form and land use patterns in determining travel behaviour.

Others have demonstrated that socioeconomic and demographic variables such as age, gender, income, household structure, and participation in the labour force are also important influences on travel behaviour. On the basis of an Atlanta survey, Helling (1996) found that men's and women's travel behaviour differs. Dieleman et al. (2002) found that in Holland, the presence of children in the household is a more powerful predictor of automobile use than labour market participation.

Some studies link physical and socio-demographic variables. In a study of the Greater Toronto Area, Riekko (2005) found that the propensity to use public transit increased with higher densities, proximity to the metropolitan core, grid street patterns, shorter blocks, greater mix of use, and proximity to rapid transit. At the same time, however, individual and neighbourhood socioeconomic and demographic variables are of central importance: age, gender, dwelling tenure, employment status, and income all affect transit use. Riekko concludes that sociodemographic variables have stronger explanatory power than urban form variables. In a study of 31 cities in the United States, Canada, and Europe, van de Coevering and Schwanen (2006) also found that socioeconomic variables are more important than urban form variables in determining average commuting distance and time and automobile mode share. Scheiner and Kasper (2005) and Schwanen et al. (2005) both investigated how the connection between land use and transportation behaviour is mediated by household composition and associated lifestyles.

"Supply-side" variables also play a role. Proximity of trip origins to potential destinations (including local area jobs-housing balance) clearly matters. Cervero and Duncan (2006) found in a San Francisco Bay Area study that jobs-housing balance is much more effective than retail-housing balance at reducing vehicle distance and hours travelled. Dieleman et al. found that car ownership was the single most important determinant of automobile use: "If people own a car, they use it" (2002:524). Schimek (1996) found that in the United States, average household income (and therefore automobile ownership) was a much stronger predictor of automobile travel than density at trip origin. While automobile ownership is related to income, it is also related to need, indicating complex relationships among density, income, mix of use, and auto ownership and access (Schimek 1996; Vandersmissen et al. 2004). Badoe and Miller (2000:254-55) found that frequency of public transit service is also important, as transit is a viable alternative to the automobile only if it efficiently and conveniently connects origins and destinations.

In a study of Boston and Hong Kong, Zhang (2004) found that land use variables and the relative cost of travel have roughly equivalent effects. He suggests that to maximize the impact of built environment factors, policymakers must also "get the prices right."

Still others have focused on attitudes and perceptions, finding that predispositions, expectations, and self-selection play an important role in determining the propensity to walk or cycle to destinations rather than drive. On the basis of a survey of residents in six neighbourhoods in Austin, Texas, Handy et al. (1998) found that residents' propensity to walk was influenced not only by urban form and proximity to destinations, but also by perceptions of safety, shade, time limitations, and the attractiveness of the visual environment. In another study, Eid et al. (2006) found that rather than suburban form (commonly characterized as sprawl) "causing" obesity, obese people may choose to live in low-density environments; conversely, people who choose to live in older areas closer to the metropolitan core may be more inclined to walk, cycle, or use transit than those living in more peripheral areas.

In a synthesis of more than 50 different empirical studies of the relationship between travel behaviour and the built environment, Ewing and Cervero (2002) conclude that trip frequencies, regardless of mode, are more a function of socioeconomic variables than the built environment. For trip lengths, however, the reverse is true: the configuration of the built environment is more determining. Mode choice appears to depend on both, although socioeconomic variables may be more important than built form.

In short, the research suggests that both local-area urban form at the origin and destination of trips (e.g., street configuration, density, neighbourhood design, and use mixture) and the proximity of different land uses at the metropolitan region scale must be appropriate if people are to make fewer and shorter trips by automobile and more trips on foot, by bicycle, or on public transit. Moreover, the relative cost and convenience of the alternatives must be competitive with the automobile. Regardless of urban form, however, people tend to use cars if they own them.

Research questions

1. How do the travel mode shares for each study area differ by purpose of trip?

2. What are the likely causes of the observed patterns?

Fig. 43: Motorized (automobile, taxi, and motorcycle) mode share for journeys to work, school and childcare, and shopping