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Challenges in
modelling
• Increasing urbanisation
• Growing attractiveness of cycling
• Higher mode share
• Yet, little scientific attention
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• Complex behavioural dimensions
• More sensitive to environment • Knowledge on cyclist
behaviour
• Development of theories and models to describe behaviour
Background
III. Knowledge acquisition
II. Activity scheduling Route and destination choice
I. Walking and cycling behaviour Subjective
choice set
Intended routes Desired walking speeds
Experience, survey knowledge Traffic conditions, levels-of-service P h ys ic al n etw or k in fr as tr u ctu re landmarks waypoints landmarks routes geometry, infra char. network information route conditions real-time info and guidance perception ICT
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• Size: 1.90 x 0.75m
Literature on cycling
• Speeds depends on
• Bicycle facility type (off vs on street) • Gender (2.5 km/h)
• Presence of a barrier (with 18.2 km/h, without 13.9 km/h)
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Literature on cycling
Literature on cycling
Navin, 1994 Homburger, 1976
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Empirical facts of cycling
First day Second day
Composition 812 bicycles 28 mopeds (3.3%) 497 bicycles 23 mopeds (4.2%) Flow 736 b/h/m 522 b/h/m Density 0.04 b/m2 0.03 b/m2 Speed 18.4 km/h 17 km/h Saturation flow 2494 b/h Jam density 0.36 b/m2
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Empirical facts of cycling
Model Data
Capacity [b/h/m] 2380 2494 Free flow speed [km/h] 18.5 17.9 Critical density [b/m2] 0.14
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• Numerous studies on bicycle operation and path design
• Bicycle flow theory is in its infancy
• First steps in establishing theory and empirically
underpinned models
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• CA model
• CA model
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Different modelling approaches • Microscopic • CA • Social forces • Game theory • Optimal control • Macroscopic • Continuum models
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• Aim to optimise behaviour
• Continuous monitoring of the system
• Anisotropic particles
• Predicting behaviour of others • Limited prediction capabilities • Follow optimal trajectory to
destination
• Perform limited effort in control
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• Control
• Putting power on the pedals
• Acceleration 𝑎
• Turning the steer
• Change in steering angle 𝜔
• Dynamics
• 𝑣 =𝑎 • 𝜑 =𝜔
• 𝑥 =𝑣∙cos𝜑 • 𝑦 =𝑣∙sin𝜑
Optimal control
• Find the control where cycling behaviour is desired, and preferable optimal
Identify cycling ‘costs’
• Straying from the optimal path
• 𝐿↑𝑠𝑡𝑟𝑎𝑦 =1/2 𝑐↓1↑𝑣 (𝑣↓𝑐 −𝑣↓0 )↑2 +1/2 𝑐↓1↑𝜑 (𝜑↓𝑐 − 𝜑↓0 )↑2
• Maintain current speed and cycling direction
• 𝐿↑𝑒𝑓𝑓 =1/2 (𝑐↓2↑𝑎 𝑎↑2 −𝑐↓2↑𝜔 𝜔↑2 )
• Proximity costs
• 𝐿↑𝑝𝑟𝑜𝑥 =𝑐↓3 ∑𝑏↑▒𝑒𝑥𝑝(−‖𝑟↓𝑐 −𝑟↓𝑏 ‖/𝑣↓𝑐 ∙𝑅↓0 )
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Modelling bicycle flows
0 10 20 30 0 20 40 60 80 100 x time (s) x (m ) 0 10 20 30 -1 -0.5 0 0.5 1 y time (s) y (m ) 0 10 20 30 2.5 3 3.5 4 4.5 5 v time (s) sp e e d ( m /s ) 0 10 20 30 -1 -0.5 0 0.5 1 phi time (s) ph i (r a d) Destination
• Limited knowledge on cyclist behaviour
• Even less on cyclist models • On all behavioural levels
• Cycling behaviour • Activity scheduling • Route choice
• Knowledge acquisitions
