Analyzing Energy Consumption to Enhance Bicycle Travel Costs and Infrastructure Planning
This study explores the relationship between energy consumption and travel costs for cyclists, utilizing a model that incorporates gender, age, athleticism, and various environmental factors. Key findings indicate that understanding these variables can help planners design better cycling infrastructure, minimizing data collection costs while enriching behavior models. By classifying cyclists based on power levels and applying slope-speed-power relationships, this research aims to improve travel time and enhance the overall cycling experience in urban environments. For inquiries, contact Olena Tokmylenko at otokmyl@clemson.edu.
Analyzing Energy Consumption to Enhance Bicycle Travel Costs and Infrastructure Planning
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Presentation Transcript
Bicycle Trip Assignment: Energy Consumption as Travel Cost Variable OlenaTokmylenko MCRP candidate 2013 Clemson University
What we think people experience What people actually experience
Model Structure gender age athleticism slope riders mass wind resistance Physiological conditions etc. distance speed power
Wingate Anaerobic Test Classification of Peak Power and Anaerobic Capacity for Female and Male NCAA Division I Collegiate Athletes
Human Power • Aerobic Capacity vs Anaerobic capacity • Functional Threshold Power • Critical Power
Bicycling Power Where
Characteristics of five types of bicycle and rider Source: “Bicycling Science” David G. Wilson
Constant parameters Metric Measurement System U.S. Measurement System
Types of bicyclists Utilitarian Recreational
Model Assumptions • Utilitarian cyclists • Different level of skills with a stress to average • Decision is made and origins and destinations are known
Model Structure gender age athleticism slope riders mass wind resistance Physiological conditions etc. distance speed power
Conclusion • One of the most important factor that affect bicycling power expenditure can be addressed by planners while designing infrastructure • The results of the model can minimize the cost of data collection and enrich behavior models • The effective planning based on travel time and energy expenditure can provide better experience to the cyclists
Next Steps • Propose classes of cyclist based on their power level • Apply slope-speed-power relationship to the road network to determine travel time • Measure energy expenditure of the riders • Test the model on real city network
Questions? For questions or propositions contact : OlenaTokmylenko otokmyl@clemson.edu
