In this presentation, we present some examples of the main outcomes of the ALLEGRO project so far. The talks starts with showing how active mode traffic can play a major role given that cities are getting denser.

1. Urban space is a scarce commodity…
We need space to live, work, recreate, study,
but also to move around in our ever-denser cities
How we move around has a major impact on the amount of
space the transportation system takes up

2. A short round of expert elicitation…
What is the average share of urban space devoted to the car?
a) Less than 20%
b) Between 20 and 45%
c) Between 45 and 65%
d) More than 65%

3. The battle for urban space!
The modes we choose to move around has a major impact mobility spatial
footprint, but also on our health, well-being, climate, etc.
It is clear that active modes can play a major role in making cities liveable, either as
a main mode of transport or as first-mile-last-mile option for mass PT
Trends like e-bikes, bike sharing and leasing provide novel opportunities…
Car
50 km/h, driver only
Car
Parked
Tram
50 passangers
Cyclist
15 km/h
Bicycle
Parked
Pedestrian
Walking
Pedestrian
Standing140 m2
20 m2
7 m2
5 m2
2 m2
2 m2
0.5 m2

4. A bicycle is not a two-wheeled car…
And a pedestrian is not a cyclist who lost his bike…
Aiming to make our societies less car-dependent, we are in dire need of
dedicated theory, models and tools to support policy making, design,
planning, and control to improve walkability and cyclability of cities
My proposition: science has not yet delivered the necessary insights and
tools (e.g. empirical insights, theory, models, guidelines)

5. A vast array of scientific challenges…
Active modes are delightfully complex!
The fact that active modes move in 2D opens up a range of novel
challenges, behaviourally and mathematically (ask Ludo!)
Interactions are multi-directional and complex

6. A vast array of scientific challenges…
Active modes are delightfully complex!
Walking and cycling do generally not only result in disutility of travel
Benefits to health and well-being are valued and may yield substitution with
other activities (extent in which depends on many factors)

7. ALLEGRO
ERC Advanced Grant (November 2015)
ALLEGRO provides new behavioural insights, novel theory,
and models for active modes at all behaviour levels
In doing so, we support control, planning and design
by providing these insights, methods and tools

8. Traffic Operations
Route Choice
Mode and activity
choice
Wayfinding,
exploring, learning
Control, Planning
and Design
methods, tools and
applications

9. ALLEGRO
With innOvative data to a new transportation and traffic
theory for pedestrians and bicycles
My second proposition: the science of active mode
mobility has been hampered by a serious lack of data
Innovative data collection is one of ALLEGRO’s
cornerstoner forming the basis of our theory, modeling, etc.

10. Field data collection
Video, WiFi / Bluetooth, Social Data
Revealed preference route choice, wayfinding data
Incl. collaboration with MoBike, and The Student Hotel
VR and simulators
Pedestrian way finding through buildings
Short-run and long-run household travel dynamics
MPN longitudinal survey active mode “specials”

11. Controlled experiments
Most comprehensive cycling experiments performed so far
providing novel microscopic and macropscopic insights
Microscopic data (trajectories) for 25 different scenarios,
including bottlenecks, crossings, merges, mixed biketypes, etc.

12. Controlled experiments
Most comprehensive cycling experiments performed so far
providing novel microscopic and macropscopic insights
Microscopic data (trajectories) for 25 different scenarios,
including bottlenecks, crossings, merges, mixed biketypes, etc.

13. Some first results…
Study reveals empirical relation between
width w of cycle path and capacity
Characteristics of staggered patterns (zipper
effect) inside bottleneck determine capacity
No clear lane regime but complex interaction
of longitudinal ‘following’ and lateral
distance keeping
*) Fact: capacity bicycle flow is ~8 times higher than a car flow!
C = 1710 + 4248 ⋅ w

14. Bicycle capacity drop
Via our experiments we established the
capacity drop for bicycle flows
Once queuing occurs (e.g. at intersection),
capacity reduces with 23%
Finding is extremely relevant for cycling
infrastructure and controller design

15. Patatzak
Trapezium-shaped bicycle lane
Capacity drop could explain why design
appears to work very well
Work on queue-location choice will further
support intersection design

16. Data-inspired effort-based modelling
Generalisation of principle-of-least-effort approach (game-theory) results in
model qualitatively reproducing observed phenomena (self-organisation)
Results where satisfactory, yet new data-driven approaches are proposed to
improve predictive validity
Out of time?

17. Traffic Operations
Route Choice
Mode and activity
choice
Wayfinding and
exploring
Control, Planning
and Design tools
and applications

18. Wayfinding and exploring CEG
Using VR to study exploration and wayfinding throughout buildings
Special attention to evacuation
Validation using drills at CEG

19. LUCY knows tourists
GPS equipped Student Hotel bikes
Let’s stop here…

20. Traffic Operations
Route Choice
Mode and activity
choice
Wayfinding and
exploring
Control, Planning
and Design tools
and applications

21. Who are active mode users?
In collaboration with
M. de Haas (KIM)
LCCA travel patterns reveals existence of traveller archetypes
Car-oriented
people (27%)
Public-
transport
users (9%)
Immobiles
(18%)
Car and bike
users (13%)
Bike-oriented
people (12%)
E-bike users
(9%)
Car-walk
(12%)
26.5 24.6 26.5
25.4 24.9 25.6 26.8

22. Cofouding factors…
Controlling for confounding factors yields:
• Strong positive relation #trips per day /
distance per bike and objective and
subjective health
• Positive relation #trips per day / distance
per foot and objective health
• Negative relation #trips per day / distance
per car and objective and subjective health
• For e-bike, no relation was found…
• Causality researched using
advanced modelling (RI-CLPM)
In collaboration with
M. de Haas (KIM)

23. Using mixed-logic to determine factors influencing active mode choice
Factors influencing active mode choice
Company car
or PT subs.
Trip purpose
Built
environment
Season
Travel Time
(high variance!)

24. Traffic Operations
Route Choice
Mode and activity
choice
Wayfinding and
exploring
Control, Planning
and Design tools
and applications

25. Cooperative
Bicycle Control
• Application of model-based
stochastic control
• Optimise trade-off between
missing green phase and
consuming energy for
traffic responsive control
• Inform rider using on-board
device (alt. road-side sign)
• Chance to catch green
phase +65%
• Energy consumption -30%

26. Some of our achievements so far
Data collection and estimation techniques and novel empirical insights
Empircally underpinned models for active mode traffic flow operations on a
microscopic and macroscopic level
Insights into main determinants of / models for mode, activity and route choice
Better empirical understanding of (differences in) wayfinding-styles and learning
Novel methods for management of active mode facilities, as well as for design,
and planning (e.g. novel bike-sharing strategies, planning models)