1. Introduction
Regular physical activity has been consistently associated with numerous physical and mental health benefits [
1,
2,
3,
4]. Physical inactivity is estimated to be the fourth leading cause of global mortality [
5]. The promotion of sufficient levels of physical activity is therefore of utmost importance. One strategy for increasing population physical activity levels is to stimulate active commuter journeys (walking or cycling to work) [
6]. Commuter journeys are a major share of the distance travelled by adults, and are a way in which physical activity could be built into the daily routine.
More than half (54%) of New Zealand adults do not achieve the recommended physical activity levels [
7,
8]. Walking is the main mode of transport to and from work for 7% of adults, but only 3% cycle [
9]. While walking to work is similar to the Dutch population (5%) for example this is much lower than the percentage of Dutch people cycling (24%) [
10].
There is increased attention for the role of urban environments in the accumulation of physical activity through different modes of transport [
11]. The provision of safe and continuous cycling facilities, in combination with strict land-use policies that foster compact, mixed-use developments (allowing for shorter and thus more bikeable trips), has been hypothesized to explain the success of cycling rates in the Netherlands [
12]. Urban environmental features can play a direct role (via the presence of cycle paths or pedestrian crossings), or an indirect role (via better access to public transport and greater barriers for car driving) in stimulating active commuting [
13,
14,
15]. Factors like housing density, walkability, land-use-mix, traffic-related factors, absence of steep inclines, presence and quality of cycling lanes, greenery and street connectivity have all been positively associated with active transport [
11,
16,
17]. Although Nazelle
et al. (2011) and Martin
et al. (2012) concluded that urban transport policies provide the opportunity for an integrative approach to tackle physical activity, carbon emission and traffic congestions [
13,
18], policymakers often face the question as to whether policy interventions at a city or regional level efficiently promote active commuting [
19].
Previous studies that examined associations between the built environment and active travel have a number of limitations. First, many studies focused on the built environment only within the residential neighbourhood [
20]. However, especially for commuter trips, environmental characteristics at both the starting and finishing areas of the commuter trip may be important as well [
21]. Further understanding of the influence of urban land-use and public transport facilities at journey origin and destination is thus warranted. As cross-sectional and quasi-experimental studies often give limited insights in the effect of changes in the built environment over time, they do not allow for the exploration of different policy scenarios. Forecasting models allow for more insight into the consequences of changing certain urban characteristics, without exposing individuals to a potentially adverse environment or having to invest in major urban environmental changes. Examples of such models are the Leeds Integrated Land-Use/Transport model (LILT), the California Urban Futures Model (CUFM) and the Integrated Transportation and Land Use Package (ITLUP) [
22]. The effects of land-use and transport policies can be considered using specific policy scenarios, and the outcomes generated can assist in evaluating different policy options.
Yet, when considering changes in land-use and active transport facilities, it is important to assess the potential differential effects on people from different socioeconomic groups, so as not to increase inequalities (at the expense of the most disadvantaged) in the environmental determinants of health. Existing studies on socioeconomic inequalities in active transport provide inconsistent results [
23]. For example, a Dutch study suggests that more educated adults are more likely to actively commute [
24], whereas studies from the UK and Australia show the reverse [
25,
26,
27]. It remains unclear whether the urban design of the areas people live in contribute to such inequalities.
The Greater Wellington region in New Zealand aims to develop and promote active transport facilities and infrastructure [
28]. The Wellington city centre already has the highest active travel mode share in New Zealand [
9], but aims to further increase active modes during the coming decades [
28]. We examined whether changes in land-use and public transport facilities have the potential to increase active commuter trips by exploring alternative policy scenarios modelled in the Wellington Integrated Land-Use Transportation and Environment Model (WILUTE) system model. The objective of the present study is threefold, to: (i) examine socioeconomic differences in active commuting; (ii) assess environmental correlates of active commuting; and (iii) model the impact of different urban policy scenarios on active commuting.
3. Results
Sociodemographic characteristics of the 481 participants are described in
Table 2. Fifty-three percent of commuters were men, the mean age was 42.34 years (sd = 11.64) and 28.7% received a low yearly income. Of the 1806 commuter trips, 592 were active commuter trips (32%, of which 12 were cycling commuter trips). Of the 582 active commuter trips, 34% were part of a multi-modal trip and 66% were active only. The average duration of an active trip was 9.9 min (sd = 1.1), and the average distance travelled was 1.0 kilometers (sd = 0.1). The cycling trips were removed from further analyses as they only made up a very small proportion of the active commuter trips.
Firstly, we examined socioeconomic differences in active commuting.
Table 3 shows participants with low and medium incomes had a significantly lower likelihood of active commuting than people on high incomes. Analysis using duration of active trips as outcome (
Table S2) suggests that individuals on medium and low incomes made shorter active commuter trips, although coefficients were not significant.
Secondly, we examined the association between built environmental (land-use and transport) variables and active commuting. We observed reasonably high correlations between the neighbourhood-level measures (
Table S3), most notably between housing, apartment and population density. More deprived neighbourhoods tended to have higher population density and higher apartment density, but also had higher walkability and higher transit scores. Areas further away from the Wellington Central Business District (WCBD) had lower population, housing and apartment density, higher deprivation and lower walkability and transit scores. This finding is supported by the maps in
Figure S1.
Focusing on the fully adjusted models,
Table 4 shows that a higher housing density, higher walkability and higher transit score
in the home area were significantly associated with higher likelihood of active commuter trips of people living in these areas. For example, a one point higher walk- or transit score (on a scale from 0 to 100) was associated with a 2% higher likelihood of active commuting. A higher housing density
in the area where people started their commuter trip was significantly associated with a lower likelihood of active commuting. A higher parking price, higher number of rail stations, higher land-use mix, higher job accessibility and higher bus frequency—but lower bus stops and lower housing density—
in the area where people ended their commuter trips (at work), were significantly associated with higher likelihood of active commuting. We found evidence for interaction between level of income and a number of built environment variables. Only the association of active commuting with land-use mix, bus frequency, job accessibility and parking price was similar in high and low income groups. Walkability, transit scores, population density, housing density and apartment density in the home area were positively related to active commuting in low income individuals, and not in high income individuals. Number of bus stops and train frequency was negatively related to active commuting in low income individuals, and not in high income individuals. For example, a higher transit score was associated with a higher odds of active commuting (OR = 1.07,
p < 0.001) in low income individuals and not in high income individuals (OR = 1.01, ns).
We repeated analyses with trips with a distance of less than 20 and less than 15 kilometers, and results were essentially unchanged.
Table S4 shows that higher land-use mix in the home area was significantly associated with a longer duration of active commuter trips. A higher number of bus stops in the home area was significantly associated with a shorter duration of active commuter trips of people living in these areas.
Finally, we examined the impact of built environment policy scenarios on active commuting. We generated two scenarios on the basis of the results in
Table 4. We calculated the probability that any trip in 2031 would be an active trip in a “business as usual” scenario compared with the probability in an alternative scenario. The probability was based on the seven statistically significant variables in the “end traffic zone” model from the second step. These were: housing density (
β = −0.198), land-use mix (
β = 0.010), number of bus stops (
β = −9.2210), bus frequency (
β = 0.020), number of rail stations (
β = 0.536), job accessibility (
β = 0.086) and parking price (
β = 1.447).
If business in 2031 were similar to business in 2006 (business as usual), the probability of a commuter trip being an active trip would be 19.7%. This result is based on projected population growth during this period, and the associated changes in variables directly associated by population changes (such as housing density). Following the business-as-usual scenario, fewer active commuter trips would be conducted in 2031 than in 2006. In the alternative scenario, we decided to focus on two variables that were significantly related to active commuting, would require relatively few structural changes in the built environment, and did not have differential effects on high- and low income individuals: increasing parking price of on-street parking, and increasing bus frequency in the region. The number of rail stations (but not train frequency) in the destination area was also associated with an increased likelihood of active commuting, but building new rail stations would require a relatively large investment in supporting infrastructure (i.e., rails). We hypothesized a 20% increase of bus frequency and in meshblocks where there was free on-street parking in 2006, we hypothesized this would shift to paid parking with a price of NZD0.60 per hour (the minimum on-street parking price in 2006). (As paid parking was only implemented in 12 out of 185 traffic zones in 2006, changing the parking fees in those traffic zones (for example by 20%) did not affect the estimation of the forecasted proportion of active commuter trips.)
Keeping all other factors constant, a 20% increase in bus frequency per day would result in 20.6% of commuter trips being active. Compared to the 19.7% in the business as usual scenario, this is an increase of 4.5%. An increase in paid parking areas—keeping all other factors constant—would result in a percentage of 35.6% of commuter trips being active, an increase of 80%. Increasing both bus frequency and paid parking would lead to a probability of 36.9% of all commuter trips being active.
4. Discussion
This study examined the independent associations of land-use and public transport facilities—both in the home and the commuter trip environment—with active commuting, and assessed socioeconomic differences in active commuting. In contrast with previous studies [
25,
26,
38], we showed that people on lower incomes conducted less active commuting and generally made active commuter trips of shorter duration. The Wellington region has previously been described as a regional outlier with regard to the relation between income and active commuting [
39]. It could be that the particular geographic composition of the Wellington region leads to these findings, in the way that some of the areas where socioeconomically deprived residents live are so sprawling that active commuting is more difficult. As such, higher incomes in the Wellington region allow for shorter commutes and the integration of active modes with public transport [
39]. This emphasizes the importance of spatial context in research on socioeconomic inequities in active commuting [
39].
Only a very small proportion of active commuter trips were cycling trips, so we focused on walking trips to work only. Several aspects of the neighbourhood built environment and transport facilities were associated with residents’ likelihood of active commuting by facilitating or inhibiting active commuting. In line with a recent Australian study [
21], both the areas where trips started and ended were of importance for active commuting. As shown in a systematic review by Saelens and Handy [
40], and more recently the study by Kerr
et al. [
31], walking for transport to work was positively related to land-use mix. An additional positive association was found with housing density (as shown by
i.e., [
41,
42]), but only in the home area. The fact that analysis with home area generated different results than analysis with areas of the start and end of commuter trips may have several explanations. On the one hand, it may suggests that different mechanisms play a role. Although decisions to commute actively are made at the start of the trip, naturally they are not independent of the area where the trip ends, and the area where people reside. In the home area, a dense neighbourhood with good walking routes and good accessibility to transit can “push” adults into active commuting, while in the destination area, a combination of different transit options and job accessibility can “pull” (attract) adults into active commuting and away from taking the car. On the other hand, this finding may suggest that the choice of geographical unit level is of importance. Density in the home area was measured at the meshblock level, while density in the start and end traffic zone was measured at the traffic zone level, which is considerably larger. A previous study demonstrated that while compactness (density) at the county level was associated with walking, density at the metropolitan level was not associated with walking [
43]. It might be that a dense, residential meshblock provides a proxy for the opportunity for individuals to walk to nearby facilities via a dense network of footpaths, while a high residential density at the traffic zone level may represent a lack of land-use mix.
Results from the logistic regression analysis also indicated that parking price and bus frequency were two important public transport variables related to active commuting. As building new rail stations requires a relatively large change of the urban form and infrastructure, and may decrease active commuting in low income adults, we decided to focus only on parking price and bus frequency. Although few natural experiments have been conducted, several observational studies have described that individuals travelling with public transport are considerably more likely to meet the weekly physical activity recommendations [
15,
44,
45]. Another study showed that although changes in relative parking fees have little effect on mode choice (also shown by Feeney [
46], individuals are relatively responsive to a change from “no” to “some” parking costs [
47]. However, the effects of increased parking fees are dependent on parking demand, time of the day, possibilities for alternative transport modes and much more. As paid parking is generally only accepted in dense areas (although often parkers do not pay), pricing is not seen as the best means of stimulating the use of public and active transport. Instead, improvements in public transport facilities are seen as most preferred and most effective [
48]. On the other hand, changing one environmental feature is not likely to shift population physical activity levels and a fixed parking price increase alone would have a greater impact on low income people. Therefore, focusing on an urban development that integrates increased public transport services with more accessible destinations, in combination with increased parking fees may favor active commuting. Moreover, decreased car use and increased public transport use could have additional effects, such as more compact, people-oriented urban design and increased road flow efficiency.
One of the novel aspects of this study pertains to the insight into where commuter trips actually took place. While previous studies examining effects of land-use on active transport were uncertain of built environments individuals were exposed to, we had information on the home area and areas of the start and the end of the trips (although the large traffic zones provided a relatively crude measure of exposure). Further, the use of a forecasting model allowed for the exploration of a scenario that involved changes in parking price and bus frequency, without exposing individuals to these changes or having to invest in such changes in the built environment. Although in reality, several factors change at the same time, specifically testing for the effect of changing a single factor while keeping all other factors fixed is an advantage of forecasting models that provides useful insight into the potential gains of specific environmental interventions. In the future, using smaller spatial scales (e.g., by applying microsimulation) will allow for the modelling of complex travel behaviours by taking into account individual-level interdependencies [
22]. This may generate even more realistic scenarios. According to a review showing that many transportation policies have an inequitable impact on the travel behaviours in different socioeconomic groups [
49], we also took into account the potential adverse effects of changes in land-use and public transport facilities on low income groups by studying socioeconomic differences in active commuting, and interactions between built environment and income level.
Despite the innovative approach in this study, we have to acknowledge a number of limitations of this study. This study was conducted in a specific region of New Zealand among a relatively small (although randomly selected) sample, and the model was built on the specific characteristics of the Wellington region at baseline (in 2006). As such, investments in train frequency or other public transport facilities that are currently not present in the Wellington region (e.g., light rail), may prove to positively impact active commuting in other regions.
Further, the reliability of the results presented is subject to the validity of the model, the validity of the data, and the validity of the assumptions made. We used cross-sectional data from 2006 as input for the forecasting. By the time of publication, the data was nearly ten years old, and the built environment or demographic composition of the population may have been changed. On the other hand, using data from 2006 allowed us to validate the trip generation part of the model using actual data from the NZHTS from 2012 and comparing this to the estimated trips in 2012 based on the model. This showed that the model only had small errors: the mean absolute percentage error was −1.50% for category 3 (adults with low income) and 0.12% for category 4 (adults with medium or high income). Still, using cross-sectional data as a basis for forecasting may have resulted in an overestimation of the effect. As such, the findings should be interpreted with caution.
Then, we have to acknowledge some limitations with regard to exposure to the built environment. For each participant of the NZHTS, information on the location of their home area (at the meshblock level), and the location of the start and end of their journey to work (at the traffic zone level—which can contain 20 meshblocks) was available. This allowed for the exploration of the influence of urban land-use and public transport facilities at journey origin and destination, as well as in the residential neighbourhood. Yet, the use of two different level of geographical units provided contradicting results, and there is a remaining uncertainty about true “exposure” when only taking into account administratively defined area boundaries [
50].
With regard to our outcome measure; we conducted analyses with odds of “any active commuting”, in which active commuter trips were defined as walking to work with a duration of at least ten minutes. Although we aimed to study active commuter trips, we removed cycling trips to work as they made up only a small proportion of the active commuter trips. Using this dichotomous outcome variable did not allow for a distinction between active trips with, for example, a duration of 10 min versus 45 min. As the duration of an active trip is also important in assessing the potential for active commuting to increase physical activity levels, we additionally conducted analyses with “duration of active commuter trips” as outcome variable. These analyses showed that land-use and public transport facilities were not significantly associated with duration of active commuter trips. This may suggest that changes in urban land use and public transport may persuade non-active commuters to commute actively, but may not extend the duration of active commuter trips of those who already commute actively.
Lastly, we need to acknowledge that many environmental factors were interrelated, and that some of the more “favorable” factors (higher job accessibility, housing density, land-use mix and transit options) clustered around the WCBD. This corresponds to the notion that the built environment is multidimensional, with many factors interacting [
51]. Further, the findings are embedded in the specific and complex social and geographic context of the Wellington region. This limits the interpretation of scenario modelling with a specific focus on urban design and transport features.
