A utility-based bicycle speed choice model with time and energy factors
- 146 Downloads
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.
KeywordsBicycles 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.
- 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). https://doi.org/10.1016/j.enconman.2014.12.043 CrossRefGoogle Scholar
- 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
- Glass, S., Dwyer, G.B.: American College of Sports Medicine: ACSM’s Metabolic Calculations Handbook. Lippincott Williams and Wilkins, Baltimore (2007)Google Scholar
- 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
- 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
- Mohring, H.: Urban highway investments. In: Dorfman, R. (ed.) Measuring Benefits of Government Investment. Brookings Institution, Washington, D.C. (1965)Google Scholar
- Navin, F.P.D.: Bicycle traffic flow characteristics: experimental results and comparisons. ITE J. 64, 31–37 (1994)Google Scholar
- NYCeWheels: The two sides of BionX: Throttle and pedal-assist. http://www.nycewheels.com/bionx-electric-assist-bike.html
- Prindle, D.: No sweat: Pedaling around Portland with an electric bike from Bosch (2015). http://www.digitaltrends.com/cool-tech/bosch-ebike-systems-hands-on/
- 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
- Wilson, D.G.: Bicycling Science. MIT Press, Cambridge (2004)Google Scholar