3.1. Results
3.1.1. Demographics and Public Transportation Access
Of the study population, 72.9% reported active commuting each day and 50.6% met recommended levels of physical activity (moderate intensity physical activity) from active commuting alone (
Table 2). Respondents with a vocational education had the lowest proportion of active commuters (63.5%). Women had a higher proportion of active commuters than men and the proportion of active commuters decreased with increasing commute distance and age.
Table 2.
Descriptive statistics of study population demographics and distances to work by subgroups of active commuters (≥5 min/day) (yes/no) and meeting recommendations of physical activity (yes/no).
Table 2.
Descriptive statistics of study population demographics and distances to work by subgroups of active commuters (≥5 min/day) (yes/no) and meeting recommendations of physical activity (yes/no).
| Total | Active Commuter (≥5 min/day) | Meeting Recommended Levels of Physical Activity (≥30 min/day) |
---|
Yes | No | Yes | No |
---|
n (%) | n (%) | n (%) | n (%) | n (%) |
---|
Total population | 28,928 (100) | 21,094 (72.9) | 7834 (27.1) | 14,629 (50.6) | 14,299 (49.4) |
Age a | 40.9 (13.1) | 39.7 (13.5) | 44.3 (11.2) | 39.3 (13.7) | 42.6 (12.2) |
Age groups (6 missing) | | | | | |
16–29 years | 6538 (22.6) | 5724 (87.5) | 814 (12.5) | 4245 (64.9) | 2293 (35.1) |
30–45 years | 10,782 (37.3) | 7507 (69.6) | 3275 (30.4) | 5056 (46.9) | 5726 (53.1) |
46–64 years | 11,604 (40.1) | 7860 (67.7) | 3744 (32.3) | 5327 (45.9) | 6277 (54.1) |
Gender (6 missing) | | | | | |
Male | 12,624 (43.6) | 8518 (67.5) | 4106 (32.5) | 5709 (45.2) | 6915 (54.8) |
Female | 16,300 (56.3) | 12,573 (77.1) | 3727 (22.9) | 8919 (54.7) | 7381 (45.3) |
Education (438 missing) | | | | | |
Primary or secondary school | 8150 (28.2) | 6434 (78.9) | 1716 (21.1) | 4608 (56.5) | 3542 (43.5) |
Vocational education | 7742 (26.8) | 4920 (63.5) | 2822 (36.5) | 3273 (42.3) | 4469 (57.7) |
Academy or bachelor degree | 7898 (27.3) | 5822 (73.7) | 2076 (26.3) | 3992 (50.5) | 3906 (49.5) |
Master or PhD degree | 4723 (16.3) | 3593 (76.1) | 1130 (23.9) | 2501 (53.0) | 2222 (47.0) |
Commute distance | | | | | |
≤5 km | 9237 (31.9) | 7957 (86.1) | 1280 (13.9) | 5731 (62.0) | 3506 (38.0) |
5–10 km | 6676 (23.1) | 5117 (76.6) | 1559 (23.4) | 3995 (59.8) | 2681 (40.2) |
10– 20 km | 6516 (22.5) | 4265 (65.5) | 2251 (34.5) | 2730 (41.9) | 3786 (58.1) |
>20 km | 6499 (22.5) | 3755 (57.8) | 2744 (42.2) | 2173 (33.4) | 4326 (66.6) |
The mean commute distance was 14.6 km (SD = 15.9), see
Table 3. Active commuters reported shorter commute distances (12.7 km) than non-active commuters (19.6 km). Mean individual distance to the nearest bus stop was 300 m, whereas the mean distance to train and S-train was approximately 4 km and 13.3 km to the nearest metro stop. Active commuters had on average shorter mean distances to nearest train, s-train and metro stop than non-active commuters.
Table 3.
Distance to the different public transportation modes in the population by subgroups of active commuters (≥5 min/day) (yes/no) and meeting recommendations of physical activity (yes/no).
Table 3.
Distance to the different public transportation modes in the population by subgroups of active commuters (≥5 min/day) (yes/no) and meeting recommendations of physical activity (yes/no).
km | Total | Active Commuting (≥5 min/day) | Meeting Recommended Levels of Physical Activity (≥30 min/day) |
---|
Yes | No | Yes | No |
---|
Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) |
---|
Distance to work or education | 14.6 (15.9) | 12.7 (14.8) | 19.6 (17.6) | 11.8 (14.0) | 17.1 (17.2) |
Distance to a bus stop | 0.3 (0.2) | 0.3 (0.2) | 0.4 (0.3) | 0.3 (0.2) | 0.3 (0.3) |
Distance to a train station | 4.2 (3.5) | 4.0 (3.3) | 4.8 (4.0) | 3.8 (3.1) | 4.6 (3.8) |
Distance to a S-train station | 4.1 (5.8) | 3.7 (5.4) | 5.3 (6.6) | 3.3 (5.0) | 5.0 (6.4) |
Distance to a metro stop | 13.3 (14.2) | 11.6 (13.3) | 17.9 (15.5) | 10.1 (12.4) | 16.6 (15.1) |
3.1.2. Association between Distance to Public Transportation and Active Commuting
The unadjusted models showed that distance to nearest bus stop or train station was negatively associated with being an active commuter (
Table 4).
Table 4.
Crude and adjusted associations (OR) between objective distance measures to public transportation and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Table 4.
Crude and adjusted associations (OR) between objective distance measures to public transportation and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Distance Measure | Active Commuter (≥5 min/day) | Meeting Recommended Levels of Physical Activity (≥30 min/day) |
---|
Model 1: Crude | Model 3: Fully Adjusted Model | Model 1: Crude | Model 3: Fully Adjusted Model |
---|
OR (CI) | OR (CI) b | OR (CI) | OR (CI) b |
---|
Distance to bus stop (km) | 0.71 (0.63–0.80) | 0.76 (0.67–0.85) | 0.8 (0.71–0.90) | 0.86 (0.76–0.96) |
P-value a | <0.0001 | <0.0001 | 0.0002 | 0.0099 |
ICC | 12.6 | 2.1 | 11.9 | 2.1 |
Distance to bus stop (m) | | | | |
Close (≤200) | 1.00 | 1.00 | 1.00 | 1.00 |
Moderate Close (201–400) | 1.00 (0.94–1.07) | 1.02 (0.95–1.09) | 1.01 (0.95–1.07) | 1.02 (0.96–1.08) |
Moderate Far(401–800) | 0.88 (0.82–0.96) | 0.92 (0.85–1.00) | 0.94 (0.87–1.01) | 0.98 (0.91–1.05) |
Far (>800) | 0.68 (0.58–0.80) | 0.73 (0.62–0.86) | 0.75 (0.63–0.88) | 0.79 (0.67–0.94) |
P-value a | <0.0001 | <0.0001 | 0.0010 | 0.0183 |
ICC | 12.8 | 2.1 | 12.0 | 2.1 |
Distance to train station (km) | 0.93 (0.91–0.94) | 0.97 (0.96–0.98) | 0.94 (0.93–0.96) | 0.98 (0.97–0.99) |
P-value a | <0.0001 | <0.0001 | <0.0001 | 0.001 |
ICC | 11.3 | 2.1 | 10.9 | 2.1 |
Distance to train station (m) | | | | |
Close (0–500) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium Close (501–1000) | 0.92 (0.76–1.12) | 0.97 (0.79–1.18) | 1.08 (0.90–1.29) | 1.13 (0.95–1.35) |
Medium Far (1001–3000) | 0.84 (0.69 – 1.02) | 0.86 (0.71–1.03) | 1.03 (0.87–1.23) | 1.06 (0.90–1.26) |
Far (>3000) | 0.65 (0.52–0.80) | 0.75 (0.62–0.91) | 0.88 (0.72–1.07) | 0.99 (0.83–1.18) |
P-value a | <0.0001 | 0.0002 | 0.0101 | 0.1254 |
ICC | 12.8 | 2.1 | 12.2 | 2.1 |
Distance to S-train station (m) | | | | |
Close (0–500) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium Close (501–1000) | 0.99 (0.87–1.12) | 1.03 (0.90–1.17) | 1.02 (0.91–1.13) | 1.03 (0.93–1.15) |
Medium Far (1001–3000) | 0.79 (0.70–0.90) | 0.89 (0.78–1.00) | 0.90 (0.80–1.00) | 0.96 (0.86–1.07) |
Far (>3000) | 0.53 (0.44–0.62) | 0.81 (0.69–0.94) | 0.64 (0.55–0.75) | 0.87 (0.76–1.00) |
P-valuea | <0.0001 | 0.0002 | 0.0017 | 0.0260 |
ICC | 9.6 | 2.0 | 9.3 | 1.9 |
Distance to metro stop (m) | | | | |
Close (0–500) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium Close (501–1000) | 0.83 (0.66–1.04) | 0.86 (0.68–1.08) | 1.03 (0.87–1.21) | 1.04 (0.88–1.22) |
Medium Far (1001–3000) | 0.66 (0.52–0.84) | 0.78 (0.63–0.98) | 0.93 (0.77–1.12) | 1.05 (0.89–1.24) |
Far (>3000) | 0.27 (0.21–0.35) | 0.56 (0.45–0.71) | 0.42 (0.35–0.51) | 0.74 (0.61–0.88) |
P-value a | <0.0001 | <0.0001 | 0.0017 | <0.0001 |
ICC | 5.2 | 1.6 | 5.8 | 1.7 |
After adjusting for potential confounders, greater distance to a bus stop and a train station was associated with significantly lower odds of being an active commuter as well as with meeting recommended levels of physical activity. Residing >400 m from a bus stop was associated with significantly lower odds of being an active commuter compared to residing within 400 m, and residing >800 m from a bus stop was associated with significantly lower odds of being an active commuter compared to residing within 800 m. For trains, S-trains and metro there was a similar dose-response trend, as greater distance to a station was associated with lower odds of being an active commuter. For trains and S-trains, there was only a significant difference in the association for those residing >3 km from a train or S-train station compared to residing within 500 m. The adjusted models for meeting recommendations of physical activity showed that for trains, metro and S-trains, there was only a significant difference in the association for those residing >3 km compared to residing within 500 m.
3.1.3. Association between Density and Service of Public Transportation and Active Commuting
The categorised density and service measures and their association with active commuting are shown in
Table 5 and
Table 6. For the adjusted models, density of bus stops, bus routes within 1 km and number of transport modes within walking and cycling distance were all positively associated with being an active commuter.
Table 5.
Crude and adjusted associations (OR) between objective density measures of public transportation and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Table 5.
Crude and adjusted associations (OR) between objective density measures of public transportation and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Density Measure | Active Commuter (≥5 min/day) | Meeting Recommended Levels of Physical Activity (≥30 min/day) |
---|
Model 1: Crude | Model 3: Fully Adjusted Model | Model 1: Crude | Model 3: Fully Adjusted Model |
---|
OR (CI) | OR (CI) b | OR (CI) | OR (CI) b |
---|
Density of bus stops | | | | |
Low (0–5) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium low (6–10) | 1.29 (1.20–1.39) | 1.25 (1.16–1.34) | 1.19 (1.11–1.28) | 1.16 (1.08–1.25) |
Medium high (11–15) | 1.56 (1.42–1.71) | 1.32 (1.20–1.45) | 1.38 (1.26–1.51) | 1.22 (1.12–1.34) |
High (>15) | 2.42 (2.12–2.76) | 1.52 (1.32–1.75) | 1.64 (1.46–1.85) | 1.22 (1.08–1.38) |
P-value a | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
ICC | 6.4 | 1.8 | 8.4 | 1.9 |
Bus routes at stops within 1 km | | | | |
Low (0–2) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium low (3–4) | 1.17 (1.08–1.26) | 1.14 (1.05–1.23) | 1.10 (1.02–1.19) | 1.09 (1.01–1.17) |
Medium High(5–6) | 1.49 (1.34–1.65) | 1.27 (1.14–1.41) | 1.30 (1.18–1.43) | 1.18 (1.07–1.29) |
High (>6) | 1.75 (1.56–1.96) | 1.31 (1.16–1.48) | 1.32 (1.19–1.46) | 1.09 (0.98–1.22) |
P-value a | <0.0001 | <0.0001 | <0.0001 | 0.0082 |
ICC | 8.1 | 1.8 | 9.8 | 2.0 |
TMI 1 km | | | | |
0 c | 0.67 (0.53–0.83) | 0.67 (0.54–0.85) | 0.77 (0.60–0.98) | 0.78 (0.61–0.99) |
1 | 1.00 | 1.00 | 1.00 | 1.00 |
2 | 1.29 (1.20–1.40) | 1.19 (1.11–1.29) | 1.18 (1.10–1.27) | 1.12 (1.04–1.19) |
3 | 1.53 (1.30–1.79) | 1.35 (1.16–1.56) | 1.14 (1.00–1.30) | 1.07 (0.94–1.20) |
P-value a | <0.0001 | <0.0001 | <0.0001 | 0.0018 |
ICC | 10.7 | 1.9 | 11.1 | 2.0 |
TMI 3 km | | | | |
1 c | 1.00 | 1.00 | 1.00 | 1.00 |
2 | 1.35 (1.21–1.51) | 1.19 (1.07–1.33) | 1.29 (1.15–1.45) | 1.16 (1.04–1.29) |
3 | 1.85 (1.61–2.12) | 1.42 (1.24–1.62) | 1.70 (1.49–1.95) | 1.38 (1.21–1.57) |
4 | 4.30 (3.57–5.18) | 1.87 (1.53–2.28) | 2.79 (2.35–3.31) | 1.44 (1.21–1.71) |
P-value a | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
ICC | 4.9 | 1.7 | 6.0 | 1.7 |
No significant associations were found between bus service measures at the nearest stop (routes and service frequency) and being an active commuter. A higher bus convenience (combined distance with service frequency) at the nearest stop was associated with significantly higher odds of being an active commuter except for the medium-low category. No significant association was found between the bus convenience at the “best” stop and being an active commuter. In the adjusted models for meeting recommended levels of physical activity, density of bus stops, the bus frequency at the “best” stop and transport modes within cycling distance were positively associated with meeting recommendation of physical activity.
Table 6.
Crude and adjusted associations (OR) between objective measures of public transportation services and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Table 6.
Crude and adjusted associations (OR) between objective measures of public transportation services and being an active commuter and meeting recommended levels of physical activity. Between neighbourhood variation is expressed by Intra-class correlation coefficient (ICC). Significant associations are highlighted in bold text.
Bus Service Measure | Active Commuter (≥5 min/Day) | Meeting Recommended Levels of Physical Activity (≥30 min/Day) |
---|
Model 1: Crude | Model 3: Fully Adjusted Model | Model 1: Crude | Model 3: Fully Adjusted Model |
---|
OR (CI) | OR (CI) | OR (CI) | OR (CI) |
---|
Bus routes at nearest stop | | | | |
Low (≤1) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium (2) | 1.00 (0.93–1.07) | 0.98 (0.91–1.05) | 1.00 (0.93–1.06) | 1.00 (0.93–1.07) |
High (>2) | 1.03 (0.95–1.12) | 0.97 (0.88–1.07) | 0.94 (0.87–1.01) | 0.92 (0.84–1.01) |
P-value a | 0.7272 | 0.7919 | 0.1362 | 0.1372 |
ICC | 13.7 | 2.0 | 12.7 | 2.1 |
Frequency of bus service at nearest stop | | | | |
Low (0–2) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium-low (3–6) | 0.90 (0.83–0.98) | 0.92 (0.85–1.01) | 0.91 (0.84–0.99) | 0.95 (0.88–1.03) |
Medium-high (7–15) | 1.02 (0.93–1.12) | 1.00 (0.91–1.11) | 0.98 (0.90–1.06) | 1.00 (0.92–1.09) |
High (>15) | 1.07 (0.96–1.18) | 0.96 (0.84–1.08) | 0.98 (0.90–1.07) | 0.99 (0.89–1.10) |
P-value a | 0.0008 | 0.1148 | 0.1142 | 0.5287 |
ICC | 12.6 | 2.1 | 12.3 | 2.1 |
Frequency of bus services at “best stop” | | | | |
Low (≤ 10) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium low (11–20) | 1.21 (1.10–1.32) | 1.09 (0.99–1.19) | 1.19 (1.09–1.30) | 1.10 (1.01–1.19) |
Medium high (21–40) | 1.43 (1.30–1.57) | 1.15 (1.04–1.26) | 1.37 (1.25–1.50) | 1.16 (1.06–1.27) |
High (>40) | 1.99 (1.77–2.24) | 1.26 (1.11–1.43) | 1.62 (1.46–1.81) | 1.18 (1.05–1.32) |
P-value a | <0.0001 | <0.0001 | <0.0001 | 0.0142 |
ICC | 7.2 | 1.6 | 8.0 | 1.8 |
Bus convenience at nearest stop | | | | |
Low (1) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium-low (2) | 1.17 (1.06–1.29) | 1.12 (1.01–1.25) | 1.15 (1.04–1.27) | 1.10 (1.00–1.21) |
Medium-high (3) | 1.07 (0.98–1.16) | 1.06 (0.97–1.15) | 1.05 (0.97–1.14) | 1.05 (0.97–1.41) |
High (4) | 1.30 (1.19–1.43) | 1.19 (1.08–1.32) | 1.15 (1.05–1.25) | 1.10 (1.00–1.21) |
P-value a | <0.0001 | 0.0016 | 0.0042 | 0.1591 |
ICC | 11.7 | 2.1 | 12.7 | 2.1 |
Bus convenience at “best” stop | | | | |
Low (1) | 1.00 | 1.00 | 1.00 | 1.00 |
Medium-low (2) | 1.05 (0.98–1.13) | 1.01 (0.94–1.08) | 1.01 (0.95–1.09) | 0.98 (0.91–1.05) |
Medium-high (3) | 1.19 (1.10–1.29) | 1.08 (1.00–1.17) | 1.13 (1.05–1.22) | 1.07 (0.99–1.15) |
High (4) | 1.28 (1.12–1.47) | 1.14 (0.99–1.32) | 1.10 (0.99–1.23) | 1.06 (0.94–1.19) |
P-value a | <0.0001 | 0.1175 | 0.0021 | 0.0857 |
ICC | 12.3 | 2.0 | 11.8 | 2.1 |
Unique bus routes and transport modes within walking distance showed a positive trend but having a high number of bus routes and three transport modes were not significantly associated with higher odds of meeting the recommendations of physical activity. No significant association was found between bus services at the nearest stop or bus convenience and meeting recommended levels of physical activity.
The ICC in the two empty models showed a noticeable significant between-neighbourhood variation of 13.6% in being an active commuter and 12.7% in meeting recommendations of physical activity. The ICC in the unadjusted models varied from 5.3% to 12.7% and was significantly reduced to between 1.6% and 2.1% in the fully adjusted models.
3.1.4. Subgroup Analysis
For respondents with commute distance ≤10 km, increasing density of bus stops, bus routes within 1 km, frequency of bus services at “best” stop, bus convenience at the nearest stop and TMI at 1 km and 3 km were all positively associated with significantly higher odds of being an active commuter (models not presented). Furthermore, there was a trend for bus convenience at the “best” stop to be related to active commuting. For respondents with commute distances >10 km the associations were insignificant to a large extent. For meeting recommendations of physical activity, the subgroup analysis showed a strong positive association between all density measures and ≥30 min of active commuting per day for those with commute distances of 5–10 km. In those having ≤5 km commute distance, there was a positive trend between the density measures and ≥30 min of active commuting per day. For commute distances >10 km the associations was insignificant.
For women, increasing distance to public transportation was associated with lower odds of being an active commuter and higher density was associated with higher odds of being an active commuter. For men, the associations were insignificant to a large extent and with no clear trend. Only transport modes accessible within 3 km showed a trend towards increasing number of transport modes being associated with significantly higher odds of being an active commuter. The associations for women attenuated in the models of meeting recommended levels of physical activity but remained significant.
For the age group between 30 and 45 the significant associations found in the adjusted models remained. These associations were significant but less pronounced in the age group between 46 and 64. For the age group between 16 and 29, the associations were insignificant to a large extent and with no clear trend for both being an active commuter as well as meeting recommendations of physical activity.
3.2. Discussion
The findings of the present study suggest that being an active commuter is associated with proximity to public transportation, number of bus routes, bus service frequency and accessible transport modes within walking or cycling distance. Public transportation characteristics that facilitate active commuting are thus complex and need to be better modeled and described than by distance measures alone.
This study strengthens the evidence on the association between access to public transportation and active commuting and advances previous research by introducing different density measures not only based on access but also on transit services and accessible transport modes. The study highlights the importance of considering not only the nearest stop but also alternative services to describe access to public transportation. While no significant association was found for number of routes and service frequency at the nearest stop, positive associations were found between bus service frequency and being an active commuter at the “best” stop. This suggests two very different conclusions about the association between public transportation and active commuting. In urban or suburban areas the “best” stop might be located close to the nearest stop; therefore, it is important to include other stops than nearest stop in measures of local public transportation facilities [
41]. Having access to more transport mode choices than a bus within walking or cycling distance also had a positive effect on being an active commuter. One explanation for the non-significant associations with the nearest stop might be that the variation in the measures at the nearest stop is too low to reveal an association. The association with the bus frequency has only been investigated in relation to active commuting in few other studies [
15,
21,
42]. Dalton
et al. [
15] found that medium (tertiles) and low bus frequencies were significantly associated with lower odds of using public transportation compared to having a high frequency. Kamada
et al. [
21] did not find a significant association, but their sample size was small and therefore had very low statistical power.
In accordance with other studies [
15,
18,
19,
20,
21], negative associations were found between the distance to the nearest stop or station and being an active commuter as well as with meeting recommendations of physical activity. The results suggests that shorter walking distances to a bus stop supports active commuting, whereas the attractiveness of the other public transportation modes (metro, trains and s-trains) diminishes with access distance. This is in line with other studies showing that people will walk further to trains than to buses [
42,
43]. Due to the large study area many respondents have very long distances (>3 km) to the train, S-train and metro stations. This clearly attenuates the associations for these three transport modes. Locally, the three transport modes are very important for commuting by public transportation in the region with direct and fast services to the main city centres.
The positive associations found between the different density measures and being an active commuter is supported by other studies findings [
22,
24,
25,
26]. The alternative density measures, the unique bus routes within 1 km and number of transport modes measures the effect of having additional services within walking distance and show strong associations with active commuting. The outcomes for the density measures reveal the importance of both easy access to public transportation and to different transport modes and routes that enable more destinations to be reached. The positive associations found may not only reflect a higher use of public transportation, walking and cycling in areas of high density, but also restrictions for car use such as lack of parking space and traffic congestion [
39] and better connected street networks that allow more direct travelling and the presence of cycle lanes that facilitate active commuting.
In the present study, having a high frequency and a short distance to the nearest stop are associated with significantly higher odds of being active compared to having longer distances and low frequency. This was not significant for the “best” stop convenience measure or in association to meeting the recommendations of physical activity. Kamada
et al. [
21] did not find a significant association between their convenience measure and active commuting, but their results showed the same positive trend that higher convenience was associated with higher odds of active commuting. It is highly questionable, however, whether the two studies are comparable as Kamada
et al. [
21] investigated women living in a rural setting in Unnan City, Japan, with a generally low public transportation service level.
The findings suggest that better access to public transportation through shorter distances between stops (higher access coverage) and higher diversity in bus routes and transport modes is associated with active commuting. From a planning perspective, it is less costly to change the service frequency than to make infrastructural changes to promote active commuting. Further studies could evaluate what level of service frequency is the most attractive to commuters. Studies have found that commuters are willing to walk further to some transit services than others [
43]. Providing fewer but better served stops could potentially promote active commuting through more walking and cycling to stops. A better service level may have a larger impact in the rural areas characterized by low public transportation access coverage.
The finding of a significant interaction with commute distance is in line with previously presented results; indicating, that distance to work is a strong predictor of travel mode choice when commuting [
15,
17]. When the commuting distance is > 10 km the number of commuters who cycle all the way to work decreases markedly and car-based commuting becomes dominant [
40]. This is also evident from the results of this study where car-based commuting is likely to be the main reason why the associations were weaker for the respondents residing far (>10 km) from work or study.
Women’s commute travel choices seem to be more influenced by access to public transportation than men. The associations found in the full model remain significant and in the same magnitude for women, but for men these associations become insignificant. Laverty
et al. [
4] finds a similar association with travel mode, indicating that women are more likely to walk or use public transportation when commuting than men. Men’s travel choice may be more influenced by car ownership. However, data on car ownership were not available in the present study.
The 16 to 29 year olds are to a large extent walking or cycling in combination with using public transportation which may explain the non-significant associations between the access measures and being an active commuter in this age group. The travel choice in the 30 to 45 year old group seems to be much more influenced by access to public transportation and a higher access and service level result in higher odds of being an active commuter and meeting recommended levels of physical activity. The associations become less pronounced for the 46 to 64 year olds. This may be the result of more car-based commuting and possibly also caused by less cycling or walking due to functional decline with age.
One of the challenges for future transport planning is to create solutions that enable time-competing multimodal trips for those with longer commute distances, thereby incorporating active transportation into otherwise car dominant commuting. Many initiatives have already been implemented to increase multimodal travelling in Denmark. It is now possible to get your bicycle on the S-train, some busses and trains. This enhances the availability of public transportation both in the access and the egress stages thereby reducing the weakest part of a multimodal trip [
31]. Shared bicycles have been introduced in many European cities and a study from Helsinki, Finland [
44] show that bicycle sharing could reduce public transportation travel time in the study area by more than 10%. Better walk paths between adjacent, but not connected, public transport stops have also been found to improve transfer and thereby reduce travel time [
45]. Furthermore Copenhagen and adjacent municipalities are introducing super cycle highways from the suburban areas to Copenhagen City centre with only a few stops with resulting reduction in travel time [
46]. Infrastructural changes should be supported by restrictions on car parking which has been shown to reduce car-based commuting [
40]. Promotion of public transportation through more direct routes and reduction of fare prices may also have a positive impact [
47].
The potential for active commuting to provide health benefits is evident from a number of studies on total physical activity. These studies find that commuting solely by active modes, or active commuting in combination with public transportation or car, was significantly more active than only using motorized modes of transport [
11]. Transit users has been found to have higher daily levels of total physical activity, but did not differ in non-transit related walking or non-walking physical activity [
48]. The current study showed a high level of respondents benefitting from active commuting and meeting recommended levels of physical from active commuting alone. In contrast, a recent study performed in the Capital Region of Denmark [
49] shows that while the number of bicycle trips rose in the Copenhagen City Center and suburban areas from 2007 to 2012, a decrease was found in the other municipalities. This stresses the need for addressing strategies to support those with lower access to public transportation.
Strengths and Limitations
This study has several strengths. The individual GIS-based objective measures for distance from home address to public transportation, calculated using network analysis and the inclusion of transport service characteristics, provided a sound setting for studying the association between access to public transportation and active commuting. The study tested a wide range of new objective density measures and the inclusion of the “best” stop in the analysis enabled a discussion to take place about how well conclusions based on nearest stop capture the significance of public transportation to active commuting. The large study population selected from one of the largest health surveys in the world and the individual register-based socioeconomic data provide a unique study base. Estimates of the ICC showed a clear amount of variation between the neighbourhoods on the outcome variable. The neighbourhood effect was accounted for in the 3-step multilevel model and significant reduction in the variation among neighbourhoods was observed.
The main limitations of the study are the self-reported daily active commuting which may be subject to information bias. Respondents might have reported daily active commuting even though they had only taken the bus a few times a month, which would have made the share of active commuters too high. The survey is cross-sectional in design so it is not possible to conclude on causality and self-selection may lead to an apparent association between active travel and transit facilities [
50]. Adjusting for individual education in combination with employment (selected population) and neighbourhood SES, met some of the limitations regarding self-selection [
51]. The high proportion of respondents reporting active commuting in this study (72.9%) is substantially higher than in other studies. Results may therefore not be generalizable to other countries or cities where active commuting is not as common. However, the observed associations are quite similar to other studies, which indicate that the findings may be comparable. The Health Survey had a response rate of 52.3%. The implication of this response rate was tested in a non-response analysis. The analysis showed that the response rate was highest among women, middle-aged individuals and individuals of higher socio-economic position and lowest among men, young and elderly individuals and individuals of lower socio-economic position. Accordingly a number of statistic weights have been calculated by Statistics Denmark to adjust for the non-response on municipality level. The weights have not been applied to the individual data in this study because the difference between including and not including the weights in the regression analysis gave no significant difference in the estimates. This may be due to the fact that this study analyses individuals and the weights are based on the municipality population.
The active commuting information is restricted to time spent walking or cycling to work or study, and it does not refer to time spent in usage of public transportation or car. This restricts the analysis to looking at active commuting and not the individual choice of travel mode. It is not possible to examine the association between use of public transportation and active commuting in this study, but only the association between access to public transportation and active commuting in general. Further studies would benefit from including information on commute mode in order to investigate the association between different features of the built environment and different commute travel behaviours. The Health Survey 2013 has been expanded and includes this information. The outcome variable also makes it difficult to separate non-active commuters and active commuters. The 5 min cut-off value was chosen under the assumption that car-based commuters report none or only a few minutes of walking and cycling to work or study. 73.8% report any active commuting, 72.9% report 5 min or more, 69.6% report 10 min or more and 50.6% report 30 min or more. The associations are thus strong with low sensitivity to the cut-off value. Due to the high bus coverage especially in the Copenhagen metropolitan area, 5 min were chosen as cut-off value to ensure that short trips walking to and from transit were captured in the analysis.
Whether the services at nearest or “best” stop was able to transport the respondents to work or study is unknown. The distance measures are based on network distances along the road network. Walkers and cyclists are likely to follow cut-throughs such as crossing green areas, taking alternative paths or street crossings not included in the road network. The shortest path distances to public transportation used in this analysis may thus overestimate the “real” walking or cycling distances.
A number of confounders identified in other studies were not included in this study. Car ownership is often a strong predictor in analyses of travel mode choice [
52]. It was not included in this study as it was not the aim to investigate how car ownership affects active commuting. Health measures such as general health state, disability and chronic diseases may affect travel choice and it would be good to include those in further analyses.