A utility-based bicycle speed choice model with time and energy factors

  • Alexander Bigazzi
  • Robin Lindsey


This paper presents a utility-based behavioral model of bicycle speed choice. A mathematical framework is developed with travel time, energy expenditure, and control factors. Observational speed data are used to calibrate the model and estimate marginal rates of substitution between energy expenditure and travel time. The model is validated by applying it to predict speed changes on pedal-assist electric bicycles. This paper lays a foundation for further development of operational active travel speed and joint speed-route choice models, which can lead to more sensitive and behaviorally-grounded operations, microsimulation, and mode choice models. In addition, the findings have implications for modeling the effects of emerging bicycle technologies. Further research is needed to calibrate the model for a broad population of travelers.


Bicycles Electric bicycles Energy expenditure Speed choice Safety Utility maximization 



We are grateful to three anonymous reviewers for useful comments. Financial support from the Social Sciences and Humanities Research Council of Canada (Grants 435-2014-2050 and 430-2016-00019) is gratefully acknowledged. Some of the findings reported in this article were originally presented at the Transportation Research Board Annual Meeting.


  1. Bai, L., Liu, P., Chen, Y., Zhang, X., Wang, W.: Comparative analysis of the safety effects of electric bikes at signalized intersections. Transp. Res. Part D Transp. Environ. 20, 48–54 (2013). CrossRefGoogle Scholar
  2. Baptista, P., Pina, A., Duarte, G., Rolim, C., Pereira, G., Silva, C., Farias, T.: From on-road trial evaluation of electric and conventional bicycles to comparison with other urban transport modes: case study in the city of Lisbon, Portugal. Energy Convers. Manag. 92, 10–18 (2015). CrossRefGoogle Scholar
  3. Bernardi, S., Krizek, K.J., Rupi, F.: Quantifying the role of disturbances and speeds on separated bicycle facilities. J. Transp. Land Use 9, 1 (2015). Google Scholar
  4. Bigazzi, A.Y., Figliozzi, M.A.: Dynamic ventilation and power output of urban bicyclists. Transp. Res. Rec. J. Transp. Res. Board 2520, 52–60 (2015). CrossRefGoogle Scholar
  5. Börjesson, M., Eliasson, J.: The value of time and external benefits in bicycle appraisal. Transp. Res. Part A Policy Pract. 46, 673–683 (2012). CrossRefGoogle Scholar
  6. Broach, J., Dill, J., Gliebe, J.: Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transp. Res. Part A Policy Pract. 46, 1730–1740 (2012). CrossRefGoogle Scholar
  7. Cherry, C., Cervero, R.: Use characteristics and mode choice behavior of electric bike users in China. Transp. Policy 14, 247–257 (2007). CrossRefGoogle Scholar
  8. El-Geneidy, A.M., Krizek, K.J., Iacono, M.J.: Predicting bicycle travel speeds along different facilities using GPS data: a proof-of-concept model. In: Presented at the Transportation Research Board 86th Annual Meeting (2007)Google Scholar
  9. Fyhri, A., Bjørnskau, T., Backer-Grøndahl, A.: Bicycle helmets: A case of risk compensation? Transp. Res. Part F Traffic Psychol. Behav. 15, 612–624 (2012). CrossRefGoogle Scholar
  10. Gatersleben, B., Appleton, K.M.: Contemplating cycling to work: attitudes and perceptions in different stages of change. Transp. Res. Part A Policy Pract. 41, 302–312 (2007). CrossRefGoogle Scholar
  11. Glass, S., Dwyer, G.B.: American College of Sports Medicine: ACSM’s Metabolic Calculations Handbook. Lippincott Williams and Wilkins, Baltimore (2007)Google Scholar
  12. Hatfield, J., Prabhakharan, P.: An investigation of behaviour and attitudes relevant to the user safety of pedestrian/cyclist shared paths. Transp. Res. Part F Traffic Psychol. Behav. 40, 35–47 (2016). CrossRefGoogle Scholar
  13. Hediyeh, H., Sayed, T., Zaki, M.H., Mori, G.: Pedestrian gait analysis using automated computer vision techniques. Transp. Transp. Sci. 10, 214–232 (2014). Google Scholar
  14. Heinen, E., van Wee, B., Maat, K.: Commuting by bicycle: an overview of the literature. Transp. Rev. 30, 59–96 (2010). CrossRefGoogle Scholar
  15. Hood, J., Sall, E., Charlton, B.: A GPS-based bicycle route choice model for San Francisco, California. Transp. Lett. Int. J. Transp. Res. 3, 63–75 (2011). CrossRefGoogle Scholar
  16. Ishaque, M.M., Noland, R.B.: Behavioural issues in pedestrian speed choice and street crossing behaviour: a review. Transp. Rev. 28, 61–85 (2008). CrossRefGoogle Scholar
  17. Jiang, R., Hu, M.-B., Wu, Q.-S., Song, W.-G.: Traffic dynamics of bicycle flow: experiment and modeling. Transp. Sci. 41, 998–1008 (2016)Google Scholar
  18. Jin, S., Qu, X., Zhou, D., Xu, C., Ma, D., Wang, D.: Estimating cycleway capacity and bicycle equivalent unit for electric bicycles. Transp. Res. Part A Policy Pract. 77, 225–248 (2015). CrossRefGoogle Scholar
  19. Landis, B., Petritsch, T., Huang, H., Do, A.: Characteristics of emerging road and trail users and their safety. Transp. Res. Rec. J. Transp. Res. Board 1878, 131–139 (2004). CrossRefGoogle Scholar
  20. Langford, B., Chen, J., Cherry, C.: Risky riding: naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders. Accid Anal Prev 82, 220–226 (2015). CrossRefGoogle Scholar
  21. Ma, X., Luo, D.: Modeling cyclist acceleration process for bicycle traffic simulation using naturalistic data. Transp. Res. Part F Traffic Psychol. Behav. 40, 130–144 (2016). CrossRefGoogle Scholar
  22. MacFarland, W.F., Chui, M.: The value of travel time: New elements developed using a speed choice model. Transp. Res. Rec. 1116, 15–21 (1987)Google Scholar
  23. Martin, J.C., Milliken, D.L., Cobb, J.E., McFadden, K.L., Coggan, A.R.: Validation of a mathematical model for road cycling power. J. Appl. Biomech. 14, 276–291 (1998)CrossRefGoogle Scholar
  24. Minetti, A.E., Boldrini, L., Brusamolin, L., Zamparo, P., McKee, T.: A feedback-controlled treadmill (treadmill-on-demand) and the spontaneous speed of walking and running in humans. J. Appl. Physiol. 95, 838–843 (2003). CrossRefGoogle Scholar
  25. Mohring, H.: Urban highway investments. In: Dorfman, R. (ed.) Measuring Benefits of Government Investment. Brookings Institution, Washington, D.C. (1965)Google Scholar
  26. Mokhtarian, P.L., Salomon, I., Singer, M.E.: What moves us? An interdisciplinary exploration of reasons for traveling. Transport Reviews. 35, 250–274 (2015). CrossRefGoogle Scholar
  27. Navin, F.P.D.: Bicycle traffic flow characteristics: experimental results and comparisons. ITE J. 64, 31–37 (1994)Google Scholar
  28. NYCeWheels: The two sides of BionX: Throttle and pedal-assist.
  29. Olds, T.S.: Modelling human locomotion: applications to cycling. Sports Med. 31, 497–509 (2001)CrossRefGoogle Scholar
  30. Parkin, J., Rotheram, J.: Design speeds and acceleration characteristics of bicycle traffic for use in planning, design and appraisal. Transp. Policy 17, 335–341 (2010). CrossRefGoogle Scholar
  31. Prindle, D.: No sweat: Pedaling around Portland with an electric bike from Bosch (2015).
  32. Sener, I.N., Eluru, N., Bhat, C.R.: An analysis of bicycle route choice preferences in Texas. US Transp. 36, 511–539 (2009). CrossRefGoogle Scholar
  33. Silva, A.M.C.B., da Cunha, J.R.R., da Silva, J.P.C.: Estimation of pedestrian walking speeds on footways. Proc. Inst. Civil Eng. Munic. Eng. 167, 32–43 (2014). Google Scholar
  34. Small, K.A.: Valuation of travel time. Econ. Transp. 1, 2–14 (2012). CrossRefGoogle Scholar
  35. Strauss, J., Miranda-Moreno, L.F., Morency, P.: Speed, travel time, and delay for intersections and road segments in Montreal using cyclist smartphone GPS data. In: Presented at the Transportation Research Board 95th Annual Meeting (2016)Google Scholar
  36. Twaddle, H., Grigoropoulos, G.: Modeling the speed, acceleration, and deceleration of bicyclists for microscopic traffic simulation. Transp. Res. Rec. J. Transp. Res. Board 2587, 8–16 (2016). CrossRefGoogle Scholar
  37. Twaddle, H., Schendzielorz, T., Fakler, O.: Bicycles in urban areas. Transp. Res. Rec. J. Transp. Res. Board 2434, 140–146 (2014). CrossRefGoogle Scholar
  38. Verhoef, E.T., Rouwendal, J.: A behavioural model of traffic congestion: endogenizing speed choice, traffic safety and time losses. J. Urban Econ. 56, 408–434 (2004). CrossRefGoogle Scholar
  39. Wilson, D.G.: Bicycling Science. MIT Press, Cambridge (2004)Google Scholar
  40. Zhang, S., Ren, G., Yang, R.: Simulation model of speed–density characteristics for mixed bicycle flow: comparison between cellular automata model and gas dynamics model. Phys. A 392, 5110–5118 (2013). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil Engineering, School of Community and Regional PlanningUniversity of British ColumbiaVancouverCanada
  2. 2.Sauder School of BusinessUniversity of British ColumbiaVancouverCanada

Personalised recommendations