Understanding the effects of individual attitudes and residential neighborhood types on university commuters’ bicycling decisions

Yujin Park, Gulsah Akar


This study investigates the effects of individual perceptions and residential neighborhoods on university commuters’ bicycling decisions using the 2015 Ohio State University Travel Pattern Survey data. We generate eight attitudinal/perceptual components based on the 26 bicycling-related questions that capture detailed perceptions of commuters toward bicycling, neighborhood environments, and residential location choice. We create distinct neighborhood typologies combining land use and socioeconomic characteristics, including population, employment, housing and intersection densities, housing types, median age of housing stock, and median household income. Probit regression models are estimated to assess the effects of sociodemographic, attitudinal/perceptual components and neighborhood types while accounting for the residential self-selection effect. Results show that people residing in different neighborhood types reveal significant attitudinal differences in terms of their conditional willingness to bicycle, and evaluation of bicycle friendliness of neighborhoods and routes. We find that bicyclists are more likely to live in neighborhoods that they perceive as having good-quality for bicycling in terms of access to bicycle facilities and lower traffic levels. Results also show the significant association of neighborhood types with bicycle commuting outcomes. People from medium-density, mixed-use, and suburban single-family neighborhoods are less likely to commute by bicycle as compared to those from high-density, mixed-use neighborhoods.


Bicycle commuting; personal attitudes; neighborhood typology; residential land-use; residential self-selection; university commuters

Full Text:



Akar, G., Chen, N., & Gordon, S. I. (2016). Influence of neighborhood types on trip distances: Spatial error models for Central Ohio. International Journal of Sustainable Transportation, 10(3), 284–293.

Akar, G., Flynn, C., & Namgung, M. (2012). Travel choices and links to transportation demand management: Case study at Ohio State University. Transportation Research Record: Journal of the Transportation Research Board, 2319, 77–85.

Akar, G., Fischer, N., & Namgung, M. (2013). Bicycling choice and gender case study: The Ohio State University. International Journal of Sustainable Transportation, 7(5), 347–365.

Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical Methods, 6(9), 2812–2831.

Bopp, M., Kaczynski, A., & Wittman, P. (2011). Active commuting patterns at a large, Midwestern college campus. Journal of American College Health, 59(7), 605–611.

Buehler, R., & Pucher, J. (2012). Cycling to work in 90 large American cities: New evidence on the role of bike paths and lanes. Transportation, 39(2), 409–432.

Cao, X., Mokhtarian, P. L., & Handy, S. L. (2009). Examining the impacts of residential self-selection on travel behavior: A focus on empirical findings. Transport Reviews, 29(3), 359–395.

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.

Chatterjee, K., Sherwin, H., & Jain, J. (2013). Triggers for changes in cycling: The role of life events and modifications to the external environment. Journal of Transport Geography, 30, 183–193.

Cole-Hunter, T., Donaire-Gonzalez, D., Curto, A., Ambros, A., Valentin, A., Garcia-Aymerich, J., … Nieuwenhuijsen, M. (2015). Objective correlates and determinants of bicycle commuting propensity in an urban environment. Transportation Research Part D: Transport and Environment, 40, 132–143.

Daley, M., & Rissel, C. (2011). Perspectives and images of cycling as a barrier or facilitator of cycling. Transport Policy, 18(1), 211–216.

Damant-Sirois, G., & El-Geneidy, A. M. (2015). Who cycles more? Determining cycling frequency through a segmentation approach in Montreal, Canada. Transportation Research Part A: Policy and Practice, 77, 113–125.

Dill, J., Mohr, C., & Ma, L. (2014). How can psychological theory help cities increase walking and bicycling? Journal of the American Planning Association, 80(1), 36–51.

Dill, J., & Voros, K. (2007). Factors affecting bicycling demand: Initial survey findings from the Portland, Oregon, region. Transportation Research Record: Journal of the Transportation Research Board, 2031, 9–17.

DuMouchel, W. H., & Duncan, G. J. (1983). Using sample survey weights in multiple regression analyses of stratified samples. Journal of the American Statistical Association, 78(383), 535–543.

Ettema, D., & Nieuwenhuis, R. (2017). Residential self-selection and travel behavior: What are the effects of attitudes, reasons for location choice and the built environment? Journal of Transport Geography, 59, 146–155.

Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294.

Fernández-Heredia, Á., Monzón, A., & Jara-Díaz, S. (2014). Understanding cyclists’ perceptions, keys for a successful bicycle promotion. Transportation Research Part A: Policy and Practice, 63, 1–11.

Gatersleben, B., & Appleton, K. M. (2007). Contemplating cycling to work: Attitudes and perceptions in different stages of change. Transportation Research Part A: Policy and Practice, 41(4), 302–312.

George, D., & Mallery, P. (2003). SPSS for windows step by step: A simple guide and reference, 4th edition. Boston: Allyn & Bacon.

Handy, S., Cao, X., & Mokhtarian, P. L. (2006). Self-selection in the relationship between the built environment and walking: Empirical evidence from Northern California. Journal of the American Planning Association, 72(1), 55–74.

Harding, C., Patterson, Z., Miranda-Moreno, L. F., & Zahabi, S. A. H. (2012). Modeling the effect of land use on activity spaces. Transportation Research Record, 2323(1), 67–74.

Heinen, E., Maat, K., & Van Wee, B. (2011). The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transportation Research Part D: Transport and Environment, 16(2), 102–109.

Kaufman, L., & Rousseeuw, P. J. (1990). Partitioning around medoids. Finding groups in data: An introduction to cluster analysis (pp.68-125). Hoboken, NJ: John Wiley and Sons.

Kim, D., Ko, J., & Park, Y. (2015). Factors affecting electric vehicle sharing program participants’ attitudes about car ownership and program participation. Transportation Research Part D: Transport and Environment, 36, 96–106.

Kusumastuti, D., & Nicholson, A. (2017). Mixed-use development in Christchurch, New Zealand: Do you want to live there? Urban Studies, 55(12), 2682–2702

LaMondia, J., & Duthie, J. (2012). Analysis of factors influencing bicycle-vehicle interactions on urban roadways by ordered probit regression. Transportation Research Record: Journal of the Transportation Research Board, 2314, 81–88.

Li, Z., Wang, W., Yang, C., & Ragland, D. R. (2013). Bicycle commuting market analysis using attitudinal market segmentation approach. Transportation Research Part A: Policy and Practice, 47, 5668.

Ma, L., & Dill, J. (2015). Associations between the objective and perceived built environment and bicycling for transportation. Journal of Transport & Health, 2(2), 248–255.

Ma. L., & Dill. J. (2016). Do people’s perceptions of neighborhood bikeability match “reality”? Journal of Transport and Land-Use, 10(1), 291–308.

Maldonado-Hinarejos, R., Sivakumar, A., & Polak, J. W. (2014). Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: A hybrid choice modelling approach. Transportation, 41(6), 1287–1304.

Manaugh, K., Miranda-Moreno, L. F., & El-Geneidy, A. M. (2010). The effect of neighborhood characteristics, accessibility, home–work location, and demographics on commuting distances. Transportation, 37(4), 627–646.

Marshall, W., & Garrick, N. (2010) Effect of street network design on walking and biking. Transportation Research Record: Journal of the Transportation Research Board, 2198, 103–115.

McFadden, D. L. (1984). Econometric analysis of qualitative response models. Handbook of Econometrics, 2, 1395–1457.

Motoaki, Y., & Daziano, R. A. (2015). A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand. Transportation Research Part A: Policy and Practice, 75, 217–230.

Muñoz, B., Monzon, A., & López, E. (2016). Transition to a cyclable city: Latent variables affecting bicycle commuting. Transportation Research Part A: Policy and Practice, 84, 4–17.

Oakil, A. T. M., Ettema, D., Arentze, T., & Timmermans, H. (2016). Bicycle commuting in the Netherlands: An analysis of modal shift and its dependence on life cycle and mobility events. International Journal of Sustainable Transportation, 10(4), 376–384.

Oliva, I., Galilea, P., & Hurtubia, R. (2018). Identifying cycling-inducing neighborhoods: A latent class approach. International Journal of Sustainable Transportation, 12(10), 701–713.

Piatkowski, D. P., & Marshall, W. E. (2015). Not all prospective bicyclists are created equal: The role of attitudes, socio-demographics, and the built environment in bicycle commuting. Travel Behavior and Society, 2(3), 166–173.

Pinjari, A., Eluru, N., Bhat, C., Pendyala, R., & Spissu, E. (2008). Joint model of choice of residential neighborhood and bicycle ownership: Accounting for self-selection and unobserved heterogeneity. Transportation Research Record: Journal of the Transportation Research Board, 2082, 17–26.

Porter, A. K., Salvo, D., Perez, A., Reininger, B., & Kohl, H. W. (2018). Intrapersonal and environmental correlates of bicycling in US adults. American Journal of Preventive Medicine, 54(3), 413–418.

Rodriguez, D. A., & Joo, J. (2004). The relationship between non-motorized mode choice and the local physical environment. Transportation Research Part D: Transport and Environment, 9(2), 151–173.

Schoner, J. E., Cao, J., & Levinson, D. M. (2015). Catalysts and magnets: Built environment and bicycle commuting. Journal of Transport Geography, 47, 100–108.

Schwanke, D. (2003). Mixed-use development handbook. Washington, DC: Urban Land Institute.

Sisson, S. B., & Tudor-Locke, C. (2008). Comparison of cyclists’ and motorists’ utilitarian physical activity at an urban university. Preventive Medicine, 46(1), 77–79.

Spotswood, F., Chatterton, T., Tapp, A., & Williams, D. (2015). Analyzing cycling as a social practice: An empirical grounding for behavior change. Transportation Research Part F: Traffic Psychology and Behavior, 29, 22–33.

Steinbach, R., Green, J., Datta, J., & Edwards, P. (2011). Cycling and the city: A case study of how gendered, ethnic and class identities can shape healthy transport choices. Social Science & Medicine, 72(7), 1123–1130.

Twaddle, H., Hall, F., & Bracic, B. (2010). Latent bicycle commuting demand and effects of gender on commuter cycling and accident rates. Transportation Research Record: Journal of the Transportation Research Board, 2190, 28–36.

Verhoeven, H., Simons, D., Van Dyck, D., Van Cauwenberg, J., Clarys, P., De Bourdeaudhuij, I., de Geus, B., Vandelanotte, C., & Deforche, B. (2016). Psychosocial and environmental correlates of walking, cycling, public transport and passive transport to various destinations in Flemish older adolescents. PLoS One, 11(1), e0147128.

Verplanken, B., Walker, I., Davis, A., & Jurasek, M. (2008). Context change and travel mode choice: Combining the habit discontinuity and self-activation hypotheses. Journal of Environmental Psychology, 28(2), 121–127.

Wall, R., Devine-Wright, P., & Mill, G. A. (2007). Comparing and combining theories to explain pro-environmental intentions: The case of commuting-mode choice. Environment and Behavior, 39(6), 731–753.

Wang, C.-H., Akar, G., & Guldmann, J.-M. (2015). Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University. Journal of Transport Geography, 42, 122–130.

Whalen, K. E., Páez, A., & Carrasco, J. A. (2013). Mode choice of university students commuting to school and the role of active travel. Journal of Transport Geography, 31, 132–142.

Willis, D. P., Manaugh, K., & El-Geneidy, A. (2015). Cycling under influence: Summarizing the influence of perceptions, attitudes, habits, and social environments on cycling for transportation. International Journal of Sustainable Transportation, 9(8), 65–579.

Winship, C., & Radbill, L. (1994). Sampling weights and regression analysis. Sociological Methods & Research, 23(2), 230–257.

Winters, M., Brauer, M., Setton, E. M., & Teschke, K. (2010). Built environment influences on healthy transportation choices: Bicycling versus driving. Journal of Urban Health, 87(6), 969–993.

Wuerzer, T., & Mason, S. G. (2015). Cycling willingness: Investigating distance as a dependent variable in cycling behavior among college students. Applied Geography, 60, 95–106.

Zahabi, S. A. H., Chang, A., Miranda-Moreno, L. F., & Patterson, Z. (2016). Exploring the link between the neighborhood typologies, bicycle infrastructure and commuting cycling over time and the potential impact on commuter GHG emissions. Transportation Research Part D: Transport and Environment, 47, 89–103.

DOI: http://dx.doi.org/10.5198/jtlu.2019.1259

Copyright (c) 2019 Yujin Park & Gulsah Akar