Exploring the role of the built and natural environment in encouraging active travel for different trip purposes in Montreal

Pegah Salsabilian

McGill University

https://orcid.org/0000-0002-7798-7726

José Arturo Jasso Chávez

McGill University

https://orcid.org/0000-0002-1405-2376

Kevin Manaugh

McGill University

https://orcid.org/0000-0003-2975-030X

DOI: https://doi.org/10.5198/jtlu.2025.2604

Keywords: Active transport, Walking, Cycling, Built environment, Accessbility, Non-linearity


Abstract

Transportation research has extensively examined the influence of both the built and natural environment on active travel. While most studies assume linear relationships, some evidence indicates that this might not always be the case. This paper addresses this by identifying the nature of the relationship between the built and natural environment (BNE) and active travel (AT) across several trip purposes: school, shopping, work, and leisure trips in Montreal, Canada. We also identify areas with low and high potential for active travel. Using Generalized Linear Models with the Tweedie family and including a spatial lag covariate, we found that the relationship between BNE and AT is not always linear. In some cases, higher access levels to sidewalks, bike lanes, walkable destinations, and transit stops, constantly increase AT but with cubic or logarithmic relationships. Other variables, such as dwelling density, intersection density, park access, tree coverage, industrial diversity, and proximity to water bodies, also encourage active travel but only up to a certain threshold, beyond which further increases do not increase AT, and in some cases, can lead to a decline, forming an inverted "U" relationship. These relationships vary across trip purposes. Central areas in Montreal show the best potential to support active travel, while the rest of the city displays low levels of support, depending on the trip's purpose. The findings highlight the importance of accounting for non-linear relationships, as improvements in the BNE do not always translate into higher levels of active travel.


References

ARTM. (2018). ARTM – Faits saillants—EOD 2018. Autorité régionale de transport métropolitain. https://www.artm.quebec/faits-saillants-eod-2018/

Badland, H., & Schofield, G. (2005). Transport, urban design, and physical activity: An evidence-based update. Transportation Research Part D: Transport and Environment, 10(3), 177–196. https://doi.org/10.1016/j.trd.2004.12.001

Baobeid, A., Koç, M., & Al-Ghamdi, S. G. (2021). Walkability and its relationships with health, sustainability, and livability: Elements of physical environment and evaluation frameworks. Frontiers in Built Environment, 7, 721218. https://doi.org/10.3389/fbuil.2021.721218

Bartshe, M., Coughenour, C., & Stephen, H. (2021). The relationship between tree canopy and social capital on physical activity in college students. Journal of American College Health, 71(6), 1705–1714. https://doi.org/10.1080/07448481.2021.1947299

Berrigan, D., Pickle, L. W., & Dill, J. (2010). Associations between street connectivity and active transportation. International Journal of Health Geographics, 9(1), 20. https://doi.org/10.1186/1476-072X-9-20

Cervero, R., & Duncan, M. (2003). Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay Area. American Journal of Public Health, 93(9), 1478–1483. https://doi.org/10.2105/AJPH.93.9.1478

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6

Cheng, L., De Vos, J., Zhao, P., Yang, M., & Witlox, F. (2020). Examining non-linear built environment effects on elderly’s walking: A random forest approach. Transportation Research Part D: Transport and Environment, 88, 102552. https://doi.org/10.1016/j.trd.2020.102552

City of Montreal. (2019). Canopy [Dataset]. Données Québec. https://www.donneesquebec.ca/recherche/dataset/vmtl-canopee

City of Montreal. (2023). Actifs de voirie (Base de données complète—Chaussée, Îlot, Intersection, Trottoir, Zone) [Dataset]. https://donnees.montreal.ca/dataset/voirie-actif

Cohen, D. A., McKenzie, T. L., Sehgal, A., Williamson, S., Golinelli, D., & Lurie, N. (2007). Contribution of public parks to physical activity. American Journal of Public Health, 97(3), 509–514. https://doi.org/10.2105/AJPH.2005.072447

De Meester, F., Van Dyck, D., De Bourdeaudhuij, I., Deforche, B., & Cardon, G. (2013). Does the perception of neighborhood built environmental attributes influence active transport in adolescents? International Journal of Behavioral Nutrition and Physical Activity, 10(1), 38. https://doi.org/10.1186/1479-5868-10-38

Delbosc, A., & Currie, G. (2018). Accessibility and exclusion related to well being. In M. Friman, D. Ettema, & L. E. Olsson (Eds.), Quality of life and daily travel (pp. 57–69). Springer International Publishing. https://doi.org/10.1007/978-3-319-76623-2_4

DMTI Spatial Inc. (2020). Enhanced points of interest (EPOI) [Dataset]. DMTI Spatial Inc.

Dong, X., Wang, L., Du, S., Qian, B., & Wang, J. (2025). Non-linear relationship between built environment and non-motorized travel efficiency under the traffic micro-circulation model. PLOS ONE, 20(1), e0314050. https://doi.org/10.1371/journal.pone.0314050

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer New York. https://doi.org/10.1007/978-1-4419-0118-7

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766

Fotheringham, A. S., & Wong, D. W. S. (1991). The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A: Economy and Space, 23(7), 1025–1044. https://doi.org/10.1068/a231025

Frank, L. (1994). Impacts of mixed use and density on utilization of three modes of travel: Single-occupant vehicle, transit, walking. Transportation Research Record, 1466, 44–52.

Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J., & Saelens, B. E. (2005). Linking objectively measured physical activity with objectively measured urban form. American Journal of Preventive Medicine, 28(2), 117–125. https://doi.org/10.1016/j.amepre.2004.11.001

Guo, C., Jiang, Y., Qiao, R., Zhao, J., Weng, J., & Chen, Y. (2023). The nonlinear relationship between the active travel behavior of older adults and built environments: A comparison between an inner-city area and a suburban area. Sustainable Cities and Society, 99, 104961. https://doi.org/10.1016/j.scs.2023.104961

Hallal, P. C., Bauman, A. E., Heath, G. W., Kohl, H. W., Lee, I.-M., & Pratt, M. (2012). Physical activity: More of the same is not enough. The Lancet, 380(9838), 190–191. https://doi.org/10.1016/S0140-6736(12)61027-7

Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity. American Journal of Preventive Medicine, 23(2), 64–73. https://doi.org/10.1016/S0749-3797(02)00475-0

Hermann, Z., Pentek, M., Gulacsi, L., Nemeth, I. A. K., & Zrubka, Z. (2022). Measuring the acceptability of EQ-5D-3L health states for different ages: A new adaptive survey methodology. European Journal of Health Economics, 23(7), 1243–1255. https://doi.org/10.1007/s10198-021-01424-8

Kurz, C. F. (2017). Tweedie distributions for fitting semicontinuous health care utilization cost data. BMC Medical Research Methodology, 17(1), 171. https://doi.org/10.1186/s12874-017-0445-y

Lambert, D. M., Brown, J. P., & Florax, R. J. G. M. (2010). A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application. Regional Science and Urban Economics, 40(4), 241–252. https://doi.org/10.1016/j.regsciurbeco.2010.04.001

Leslie, E., Saelens, B., Frank, L., Owen, N., Bauman, A., Coffee, N., & Hugo, G. (2005). Residents’ perceptions of walkability attributes in objectively different neighbourhoods: A pilot study. Health & Place, 11(3), 227–236. https://doi.org/10.1016/j.healthplace.2004.05.005

Liu, J., Wang, B., & Xiao, L. (2021). Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach. Journal of Transport Geography, 92, 103034. https://doi.org/10.1016/j.jtrangeo.2021.103034

Loo, B. P. Y., & Wang, B. (2018). Factors associated with home-based e-working and e-shopping in Nanjing, China. Transportation, 45(2), 365–384. https://doi.org/10.1007/s11116-017-9792-0

Manoj, M., & Verma, A. (2016). Effect of built environment measures on trip distance and mode choice decision of non-workers from a city of a developing country, India. Transportation Research Part D: Transport and Environment, 46, 351–364. https://doi.org/10.1016/j.trd.2016.04.013

McCormack, G. R., & Shiell, A. (2011). In search of causality: A systematic review of the relationship between the built environment and physical activity among adults. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 125. https://doi.org/10.1186/1479-5868-8-125

Mitra, R., & Buliung, R. N. (2012). Built environment correlates of active school transportation: Neighborhood and the modifiable areal unit problem. Journal of Transport Geography, 20(1), 51–61. https://doi.org/10.1016/j.jtrangeo.2011.07.009

Murrin, E., Taylor, N., Peralta, L., Dudley, D., Cotton, W., & White, R. L. (2023). Does physical activity mediate the associations between blue space and mental health? A cross-sectional study in Australia. BMC Public Health, 23(1), 203. https://doi.org/10.1186/s12889-023-15101-3

OpenStreetMap contributors. (2023). [Dataset]. openstreetmap.org

Pereira, R. H. M., Saraiva, M., Herszenhut, D., Braga, C. K. V., & Conway, M. W. (2021). r5r: Rapid realistic routing on multimodal transport networks with R5 in R. Findings. https://doi.org/10.32866/001c.21262

Rojas-Rueda, D., De Nazelle, A., Andersen, Z. J., Braun-Fahrländer, C., Bruha, J., Bruhova-Foltynova, H., Desqueyroux, H., Praznoczy, C., Ragettli, M. S., Tainio, M., & Nieuwenhuijsen, M. J. (2016). Health impacts of active transportation in Europe. PLOS ONE, 11(3), e0149990. https://doi.org/10.1371/journal.pone.0149990

Rybarczyk, G. (2018). Toward a spatial understanding of active transportation potential among a university population. International Journal of Sustainable Transportation, 12(9), 625–636. https://doi.org/10.1080/15568318.2017.1422301

Saelens, B. E., Sallis, J. F., Black, J. B., & Chen, D. (2003). Neighborhood-based differences in physical activity: An environment scale evaluation. American Journal of Public Health, 93(9), 1552–1558. https://doi.org/10.2105/AJPH.93.9.1552

Saunders, L. E., Green, J. M., Petticrew, M. P., Steinbach, R., & Roberts, H. (2013). What are the health benefits of active travel? A systematic review of trials and cohort studies. PLOS ONE, 8(8), e69912. https://doi.org/10.1371/journal.pone.0069912

Schulz, A., & Northridge, M. E. (2004). Social determinants of health: Implications for environmental health promotion. Health Education & Behavior, 31(4), 455–471. https://doi.org/10.1177/1090198104265598

Song, Y., Merlin, L., & Rodriguez, D. (2013). Comparing measures of urban land use mix. Computers, Environment and Urban Systems, 42, 1–13. https://doi.org/10.1016/j.compenvurbsys.2013.08.001

Statistics Canada. (2022a). Census of population 2021 [Dataset]. Statistics Canada.

Statistics Canada. (2022b). North American Industry Classification System (NAICS). https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=1369825

Sun, G., Oreskovic, N. M., & Lin, H. (2014). How do changes to the built environment influence walking behaviors? A longitudinal study within a university campus in Hong Kong. International Journal of Health Geographics, 13(1), 28. https://doi.org/10.1186/1476-072X-13-28

Tao, T., Wu, X., Cao, J., Fan, Y., Das, K., & Ramaswami, A. (2023). Exploring the nonlinear relationship between the built environment and active travel in the Twin Cities. Journal of Planning Education and Research, 43(3), 637–652. https://doi.org/10.1177/0739456X20915765

Tavakoli, Z., Waygood, O., Abdollahi, S., & Paez, A. (2024). “Where do children go?”: Exploring children’s daily destinations with children, parents, and experts. Urban Planning, 9, 8478. https://doi.org/10.17645/up.8478

Wali, B., Frank, L. D., Chapman, J. E., & Fox, E. H. (2021). Developing policy thresholds for objectively measured environmental features to support active travel. Transportation Research Part D: Transport and Environment, 90, 102678. https://doi.org/10.1016/j.trd.2020.102678

Wang, J., & Cao, X. (2017). Exploring built environment correlates of walking distance of transit egress in the Twin Cities. Journal of Transport Geography, 64, 132–138. https://doi.org/10.1016/j.jtrangeo.2017.08.013

Wang, X., Liu, Y., Zhu, C., Yao, Y., & Helbich, M. (2022). Associations between the streetscape built environment and walking to school among primary schoolchildren in Beijing, China. Journal of Transport Geography, 99, 103303. https://doi.org/10.1016/j.jtrangeo.2022.103303

Wendel-Vos, W., Droomers, M., Kremers, S., Brug, J., & Van Lenthe, F. (2007). Potential environmental determinants of physical activity in adults: A systematic review. Obesity Reviews, 8(5), 425–440. https://doi.org/10.1111/j.1467-789X.2007.00370.x

Yang, Y., Sasaki, K., Cheng, L., & Liu, X. (2022). Gender differences in active travel among older adults: Non-linear built environment insights. Transportation Research Part D: Transport and Environment, 110, 103405. https://doi.org/10.1016/j.trd.2022.103405

Zhang, X., Lu, H., & Holt, J. B. (2011). Modeling spatial accessibility to parks: A national study. International Journal of Health Geographics, 10(1), 31. https://doi.org/10.1186/1476-072X-10-31