bicycle trip assignment: energy consumption as travel cost variable olena tokmylenko mcrp candidate...
TRANSCRIPT
Bicycle Trip Assignment:Energy Consumption as Travel Cost Variable
Olena TokmylenkoMCRP candidate 2013Clemson University
Source: Los Angeles Bicycle Plan
What we think people experience
What people actually experience
Model Structure
Optimal route
Travel Time
Energy Expenditu
re
speed
Physiological
conditions
athleticism
gender age
distance
sloperiders
masswind resistan
ce
etc.
power
Level of proficiency
Group “A”Advanced or experienced
Group “B”Basic or less confident
Group “C”Children
Wingate Anaerobic Test Classification of Peak Power and Anaerobic Capacity for Female and Male NCAA Division I Collegiate Athletes
Human PowerAerobic Capacity vs Anaerobic
capacity Functional Threshold PowerCritical Power
power critical )duration
1 capacity work (anaerobic Power Sustained
Bicycling Power
VCsmgVVKW RwAw )]()([ 2
Where
Characteristics of five types of bicycle and rider
Roadster (Utility) bicycle
Sports bicycle
Road racing bicycle
Frontage area, A (m²)
0.5 0.4 0.33
Drag coefficient, 1.2 1 0.9
Bicycle mass (kg) 15 11 9
Rider mass (kg) 77 75 75
Rolling resistance coefficient,
0.008 0.004 0.003
Force of rolling resistance, (N)
7.218 3.374 2.471
Aerodynamic drag factor, (kg/m)
0.368 0.245 0.182
Source: “Bicycling Science” David G. Wilson
Constant parameters
Velocity, m/s
Rider’s Mass, kg
Slope,% Headwind Velocity, m/s
5 70 0 3
Velocity, mi/h
Rider’s Mass, lb
Slope,% Headwind Velocity, mi/h
~ 11 ~155 0 ~7
U.S. Measurement System
Metric Measurement System
Bicycling Power
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
200
400
600
800
1000
1200
1400
velocity, m/s
pow
er,
watt
50 60 70 80 90 100
110
120
0
200
400
600
800
1000
1200
1400
mass, kg
pow
er,
watt
0% 2% 4% 6% 8%10%
12%
14%
16%
18%
20%
22%
24%
0
200
400
600
800
1000
1200
1400
slope
pow
er,
watt
00.511.522.533.544.555.566.577.588.590
200
400
600
800
1000
1200
1400
wind velocity, m/s
pow
er,
watt
Types of bicyclists
Utilitarian
Recreational
Model AssumptionsUtilitarian cyclistsDifferent level of skills with a
stress to averageDecision is made and origins and
destinations are known
Model Structure
Optimal route
Travel Time
Energy Expenditu
re
speed
Physiological
conditions
athleticism
gender age
distance
sloperiders
masswind resistan
ce
etc.
power
ConclusionOne 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 StepsPropose classes of cyclist based
on their power levelApply 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
