Estimating Bicycle Demand of a Small Community

  • Seungkyu RyuEmail author
  • Jacqueline Su
  • Anthony Chen
  • Keechoo Choi
Transportation Engineering


Currently, there is a growing movement in the urban planning and transportation sectors advocating for the creation of sustainable and livable communities. Since these communities focus on the promotion of public health and the protection of environmental resources, it comes to no surprise that cycling is experiencing increasing popularity as an alternative mode of transportation. With anticipated increases in cycling mode share, there is a need to account for cycling in future transportation networks by estimating bicycle demand. Thus, the objective of this paper is to present a procedure for estimating bicycle trips in smaller communities with limited resources. A case study at the Utah State University campus in Logan, Utah is conducted to demonstrate the applicability of the bicycle demand estimation procedure. The case study involves data collection, initial bicycle origin-destination (O-D) estimation using a gravity model, and adjustment of the original bicycle O-D matrix using a path flow estimator with an in-depth analysis into the differences between observed and estimated data.


bicycle demand bicycle counts cyclist route choice path flow estimator small community livable communities university campus 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akar, G. and Clifton, K. J. (2009). “Influence of individual perceptions and bicycle infrastructure on decision to bike.” Transportation Research Record, Vol. 2140, pp. 165–171.CrossRefGoogle Scholar
  2. Aultman-Hall, L., Hall, F., and Baetz, B. (1997). “Analysis of bicycle commuter routes using geographic information systems implications for bicycle planning.” Transportation Research Record, Vol. 1578, pp. 102–110.CrossRefGoogle Scholar
  3. Bell, M. G. H., Shield, C. M., Busch, F., and Kruse, G. (1997). “A stochastic user equilibrium path flow estimator.” Transportation Research Part C, Vol. 5, Nos. 3–4, pp. 197–210.CrossRefGoogle Scholar
  4. Broach, J., Gliebe, J., and Dill, J. (2011). “Bicycle route choice model developed using revealed preference GPS data.” Proc., 90th Annual Meeting of the Transportation Research Board, TRB, Washington D.C., USA.Google Scholar
  5. Cache Metropolitan Planning Organization (CMPO) (2014). “2035 Regional transportation plan.” [Accessed on March 20, 2014].Google Scholar
  6. Chen, A., Chootinan, P., and Recker, W. (2005). “Examining the quality of synthetic origin-destination trip table estimated by path flow estimator.” Journal of Transportation Engineering, Vol. 131, No. 7, pp. 506–513, DOI: Scholar
  7. Chen, A., Chootinan, P., and Recker, W. (2009). “Norm approximation method for handling traffic count inconsistencies in path flow estimator.” Transportation Research Part B, Vol. 43, Nos. 8–9, pp. 852–872, DOI: Scholar
  8. Chen, A., Ryu, S., and Chootinan, P. (2010). “L-norm path flow estimator for handling traffic count inconsistencies: Formulation and solution algorithm.” Journal of Transportation Engineering, Vol. 136, No. 6, pp. 565–575, DOI: Scholar
  9. Chootinan, P., Chen, A., and Recke, W. (2005). “Improved path flow estimator for estimating origin-destination trip tables.” Transportation Research Record, Vol. 1923, pp. 9–17.CrossRefGoogle Scholar
  10. Dill, J. and Carr, T. (2003). “Bicycle commuting and facilities in major US cities: If you build them, commuters will use them.” Transportation Research Record, Vol. 1828, No. 1, pp. 311–316, DOI: Scholar
  11. Ehrgott, M., Wang, J., Raith, A., and van Houtte, A. (2012). “A bi-objective cyclist route choice model.” Transportation Research Part A, Vol. 46, No. 4, pp. 652–663, DOI: Scholar
  12. Eom, J., Stone, J., and Ghosh, S. (2009). “Daily activity patterns of university students.” Journal of Urban Planning and Development, Vol. 135, No. 4, pp. 141–149, DOI: Scholar
  13. Evans, S. P. (1976). “Derivation and analysis of some models for combining trip distribution and assignment.” Transportation Research, Vol. 10, No. 1, pp. 37–57, DOI: Scholar
  14. Hopkinson, P. and Wardman, M. (1996). “Evaluating the demand for cycle facilities.” Transport Policy, Vol. 3, No. 4, pp. 241–249, DOI: Scholar
  15. Hunt, J. D. and Abraham, J. E. (2007). “Influences on bicycle use.” Transportation, Vol. 34, No. 4, pp. 453–470, DOI: Scholar
  16. Kang, L. and Fricker, J. (2013). “A bicycle route choice model that incorporate distance and perceived risk.” Proc. 92th Annual Meeting of the Transportation Research Board, TRB, Washington D.C., USA.Google Scholar
  17. Klobucar, M. and Fricker, J. (2007). “Network evaluation tool to improve real and perceived bicycle Safety.” Transportation Research Record, Vol. 2031, pp. 25–33.CrossRefGoogle Scholar
  18. Krizel, G. and Thompson, K. (2009). “Analyzing the effect of bicycle facilities on commute mode share over time.” J. Urban Plann. Dev., Vol. 135, No. 2, pp. 66–73, DOI: Scholar
  19. Kuzmyak, J., Walters, J., Bradley, M., and Kockelman, K. (2014). Estimating bicycling and walking for planning and project development: A guidebook, Report 770, National Cooperative Highway Research Program, TRB, Washington, D.C., USA.CrossRefGoogle Scholar
  20. Lundgren, J. and Patriksson, M. (1998). “The combined distribution and stochastic assignment problem.” Annals of Operations Research, Vol. 82, pp. 309–330.MathSciNetCrossRefzbMATHGoogle Scholar
  21. McKenzie, B. (2012). Modes less traveled bicycling and walking to work in the United States: 2008–2012, ACS-25, 2014, United States Census Bureau, Suitland, MD, USA.Google Scholar
  22. Mekuria, M., Furth, P., and Nixon, H. (2012). Low-stress bicycling and network connectivity, Mineta Transportation Institute, San José, CA, USA.Google Scholar
  23. Quattrone, A. and Vitetta, A. (2011). “Random and fuzzy utility models for road route choice.” Transportation Research Part E, Vol. 47, No. 6, pp. 1126–1139, DOI: Scholar
  24. Ryu, S., Chen, A., Su, J., and Choi, K. (2017). “Two-stage bicycle traffic assignment model.” Journal of Transportation Engineering, Part A: Systems, Vol. 144, No. 2, DOI:
  25. Ryu, S., Chen, A., Zhang, H. M., and Recker, W. (2014). “Path flow estimator for planning application in small communities.” Transportation Research Part A, Vol. 69, pp. 212–242, DOI: Scholar
  26. Ryu, S., Su, J., and Chen, A. (2015). A bicycle network analysis tool for planning applications in small communities, Report UTC-1501, Utah Transportation Center, Logan, UT, USA.Google Scholar
  27. Smith, T. E. (1978). “A cost-efficiency principle of spatial interaction behavior.” Regional Science and Urban Economics, Vol. 8, No. 4, pp. 313–337, DOI: Scholar
  28. Smith, T. E. (1983). “A cost-efficiency approach to the analysis of congested spatial-interaction behavior.” Environment and Planning A, Vol. 15, No. 4, pp. 435–464, DOI: Scholar
  29. Stinson, M. A. and Bhat, C. R. (2003). “Commuter bicyclist route choice: Analysis using a stated preference survey.” Transportation Research Record, Vol. 1828, pp. 107–115.CrossRefGoogle Scholar
  30. Transportation Research Board (2011). Highway capacity manual 2011, TRB, Washington D.C., USA.Google Scholar
  31. Utah State University Parking and Transportation (TRB) (2014). “Transportation survey result.” [Accessed on February 20, 2014].Google Scholar
  32. Wardrop, J. G. (1952). “Some theoretical aspects of road traffic research.” Proceedings of the Institution of Civil Engineers, Vol. 1, No. 3, pp. 325–378, DOI: Scholar

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  • Seungkyu Ryu
    • 1
    Email author
  • Jacqueline Su
    • 2
  • Anthony Chen
    • 3
  • Keechoo Choi
    • 4
  1. 1.TOD-based Sustainable Urban TransportationAjou UniversitySuwonKorea
  2. 2.Dept. of Urban PlanningUniversity of California, Los AngelesLos AngelesUSA
  3. 3.Dept. of Civil and Environmental EngineeringThe Hong Kong Polytechnic UniversityHung HomHong Kong
  4. 4.Dept. of Transportation EngineeringAjou UniversitySuwonKorea

Personalised recommendations