Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)

1. Unravelling Urban Traffic
Flows
From new insights to advanced solutions… a work in progress
Prof. dr. Serge Hoogendoorn
1

2. AMSTERDAM INSTITUTE FOR
ADVANCED METROPOLITAN SOLUTIONS
TU DELFT, WAGENINGEN UR, MIT
ACCENTURE, ALLIANDER, AMSTERDAM
SMART CITY, CISCO, CITY OF BOSTON,
ESA, IBM, KPN, SHELL, TNO, WAAG SOCIETY,
WATERNET
CITY METABOLISM: URBAN FLOWS
WATER-ENERGY-WASTE-FOOD-DATA-PEOPLE
2
CIRCULAR
CITY
VITAL CITY
CONNECTED
CITY
Circular economy
Water, energy, food, waste
Smart infrastructures
Urban big data
Internet of Everything
Digital fabrication
Smart mobility
Resilient, clean and healthy
urban environment
Blue-green infrastructures
Social & responsible design
Proposition: using the cityas a living lab to exploreimpact and find possibilitiesof these (and other) trendson mobility and other
sectors…

3. 3
AMSTERDAM INSTITUTE FOR
ADVANCED METROPOLITAN SOLUTIONS
TU DELFT, WAGENINGEN UR, MIT
ACCENTURE, ALLIANDER, AMSTERDAM
SMART CITY, CISCO, CITY OF BOSTON,
ESA, IBM, KPN, SHELL, TNO, WAAG SOCIETY,
WATERNET
AMBITIONS
An internationally renowned, public-private institution in the area
of metropolitan solutions that in 2022 has …
… 200-250 talented students participating in a new MSc …
… 100-150 researchers working on discovering, developing and implementing
metropolitan solutions …
… EUR 25-35 million annual budget for research and valorization …
… 30-50 public and private partners participating ...
… 500-1,000 publications, 10-15 spin-outs and 30-70 start-ups generated
between 2013 and 2022 …
… an excellent position for continued value creation in the next 20 years.

4. Entering the urban age
• Urbanisation is a global trend: more
people live in cities than ever!
• City regions become focal points of the
world economy in terms of output,
productivity, decision making power,
innovation power
• Requirement for success: internal
connectivity (within city or city region)
and external connectivity (airport,
ports): importance of accessibility
4

5. Challenges…
• Accessibility is a major issue in many
cities (Amsterdam, Melbourne)
• Most delays are experienced in cities
(not on freeways!), yet freeways have
received much attention in the past…
• At the same time, (re-) urbanisation
opens up many new alleyways for
sustainable mobility (active modes,
seamless multi-modal transport,
shared mobility, autonomous driving)
• So what do we see as key themes?

6. Challenges…
• Accessibility is a major issue in many
cities (Amsterdam, Melbourne)
• Most delays are experienced in cities
(not on freeways!), yet freeways have
received much attention in the past…
• At the same time, (re-) urbanisation
opens up many new alleyways for
sustainable mobility (active modes,
seamless multi-modal transport,
shared mobility, autonomous driving)
• So what do we see as key themes?

7. Relevant research domains for mobility theme
Research domains relevant to urban
transportation systems and mobility
involve (but not excluded to):
• Slow (or rather) active traffic modes
(pedestrians, crowds, bikes)
• Coordinated & cooperative traffic control,
management and information
• Automation & self-driving vehicles
• Resilient public transport systems and
sustainable multi-modal transport
• Urban distribution and city logistics
7

8. Trends in mode share in Amsterdam area
• Since 1990’s car use has been on
the decline in Amsterdam
• Cycling and walking are main
modes of transport in city
• Big impacts on emissions (4-12%
reduction), as well as accessibility
and health
• But these positive trends also has
some negative (but interesting)
impacts…

9. Side-effects of increasing active mode shares…
Bike congestion causing delays
and hindrance
Overcrowding during events and regular
situations also due to tourists
Overcrowded public transport hubs
Not-so-seamless public transport
Bike parking problems & orphan bikes
Bike congestion causing delays and
dangerous behaviour at intersections

10. The ALLEGRO programme
unrAvelLing sLow modE travelinG and tRaffic:
with innOvative data to a new transportation and traffic theory for
pedestrians and bicycles”
• 4.2 million AUD personal grant with a focus on developing theory (from an
application oriented perspective) sponsored by the ERC and AMS
• Relevant elements of the project:
• Development of components for “living” data & simulation laboratory building on two decades of
experience in pedestrian monitoring, theory and simulation
• Outreach to cities by means of “solution-oriented” projects (“the AMS part”), e.g. event planning
framework, design and crowd management strategies, etc.
• Looking for talented PhD students!

11. Active Mode
UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and fusion toolbox
Social-media
data analytics
AM-UML app
Simulation
platform
Walking and
Cycling
Behaviour
Traffic Flow
Operations
Route Choice and
Activity
Scheduling Theory
Planning anddesign guidelines
Organisation of
large-scale
events
Data Insights
Tools
Models Impacts
Network Knowledge Acquisition (learning)
Factors
determining
route choice

12. 12
Engineering the future city.
Today’s talk
• Focus on active modes
in particular on
pedestrian and crowds
• Use SAIL event as the
driving example to
illustrate various
concepts in monitoring
and management
• SAIL project entailed
development of a crowd
management decision
support system

13. 13
SAIL?
• Biggest (and free)
public event in the
Nederland, organised
every 5 years since 1975
• Organised around the
IJhaven, Amsterdam
• This time around 600
tallships were sailing in
• Around 2,3 million
national and
international visitors

14. 14
Engineering challenges
for events or regular
situations…
• Can we for a certain event predict if a
safety or throughput issue will occur?
• Can we develop methods to support
organisation, planning and design?
• Can we develop approaches to real-time
manage large pedestrian flows safely and
efficiently?
• Can we ensure that all of these are robust
agains unforeseen circumstances?
Deep knowledge of crowd dynamics is
essential to answer these questions!

15. Pedestrian flow operations…
Simple case example: how long does it take to
evacuatie a room?
• Consider a room of N people
• Suppose that the (only) exit has capacity of C Peds/hour
• Use a simple queuing model to compute duration T
• How long does the evacuation take?
• Capacity of the door is very important
• Which factors determine capacity?
15
T =
N
C
N people in area
Door capacity: C
N
C

16. Pedestrian flow operations…
Simple case example: how long does it take to
evacuatie a room?
• Wat determines capacity?
• Experimental research on behalf of Dutch Ministry of
Housing
• Experiments under different circumstances and
composition of flow
• Empirical basis to express the capacity of a door (per meter width, per second) as a
function of the considered factors:

17. Pedestrian flow operations…
Simple case example: how long does it take to
evacuatie a room?
• Wat determines capacity?
• Open door (90 degrees) yields a capacity reduction of 7%
• Detailed analysis of paths (by tracking of) pedestrian
reveals cause
0 1 2 3 4 5 6 7 8
1
2
3
4
5
6 Looprichting
X-positie (in m)
Y
-
p
o
s
i
t
i
e
(
i
n
m
) Walking direction
X-position (in m)
Y-position(inm)
• Pedestrians appear to walk very close together (short headways)
for a very short period of time (only at side where there is no door)
• Importance of detailed research in microscopic behaviour to
understand phenomena…

18. 18
• Insight in more complex
situations
• Real-life situations in (public)
spaces often more complex
• Limited empirical knowledge
on multi-directional flows
motivated first walker
experiments in 2002
• Worldpremiere, many have
followed!
• Resulted in a unique
microscopic dataset
First insights into importance
of self-organisation in
pedestrian flows

19. Fascinating self-organisation
• Example efficient self-organisation dynamic walking lanes in bi-directional flow
• High efficiency in terms of capacity and observed walking speeds
• Experiments by Hermes group show similar results as TU Delft experiments,
but at higher densities
19

20. Fascinating self-organisation
• Relatively small efficiency loss (around
7% capacity reduction), depending on
flow composition (direction split)
• Same applies to crossing flows: self-
organised diagonal patterns turn out to
be very efficient
• Other types of self-organised
phenomena occur as well (e.g. viscous
fingering)
• Phenomena also occur in the field…
20
Bi-directional experiment

21. Studying self-organisation during rock concert Lowlands…
Pedestrian flow operations…
So with this wonderful
self-organisation, why do
we need to worry about
crowds at all?

22. 22
Increase in friction resulting in arc formation
by increasing pressure from behind (force-
Pedestrian capacity drop and
faster-is-slower effect
• Capacity drop also occurs in pedestrian flow
• Faster = slower effect
• Pedestrian experiments (TU Dresden, TU
Delft) have revealed that outflow reduces
substantially when evacuees try to exit room
as quickly as possible (rushing)
• Capacity reduction is caused by friction and
arc-formation in front of door due to
increased pressure
• Capacity reduction causes severe increases in
evacuation times
Intermezzo: given ourunderstanding of thecauses of the faster isslower effect, can youthink of a solution?

23. How old Dutch traditions may actually be of some use…

24. 24
Break-down of efficient self-
organisation
• When conditions become too crowded
(density larger than critical density), efficient
self-organisation ‘breaks down’ causing
• Flow performance (effective capacity)
decreases substantially, potentially causing
more problems as demand stays at same level
• Importance of ‘keeping things flowing’, i.e.
keeping density at subcritical level
maintaining efficient and smooth flow
operations
• Has severe implications on the network level

25. A New Phase in Pedestrian Flow Operations
• When densities become
very large (> 6 P/m2) new
phase emerges coined
turbulence
• Characterised by extreme
high densities and
pressure exerted by the
other pedestrians
• High probabilities of
asphyxiation

26. Why crowd management is necessary!
Efficient self-
organisation
Faster = slower
effect
Blockades and
turbulence
“There are serious limitations to the self-organising abilities
of pedestrian flow operations”
Reduced production of pedestrian network

27. Why crowd management is necessary!
• Pedestrian Network Fundamental Diagram shows
relation between number of pedestrians in area
• P-NFD shows reduced performance of network
flow operations in case
of overloading causes by
various phenomena such
as faster-is-slower effect
and self-organisation
breaking down
• Current work focusses on
theory of P-NFD
27

28. 28
Crowd Management for Events
• Unique pilot with crowd management system
for large scale, outdoor event
• Functional architecture of SAIL 2015 crowd
management systems
• Phase 1 focussed on monitoring and
diagnostics (data collection, number of
visitors, densities, walking speeds,
determining levels of service and potentially
dangerous situations)
• Phase 2 focusses on prediction and decision
support for crowd management measure
deployment (model-based prediction,
intervention decision support)
Data
fusion and
state estimation:
hoe many people
are there and how
fast do they
move?
Social-media
analyser: who are
the visitors and what
are they talking
about?
Bottleneck
inspector: wat
are potential
problem
locations?
State
predictor: what
will the situation
look like in 15
minutes?
Route
estimator:
which routes
are people
using?
Activity
estimator:
what are
people
doing?
Intervening:
do we need to
apply certain
measures and
how?

29. Tracking SAIL visitors using GPS devices
Central Station
Walking and choice behaviour of SAIL visitors
on the 22nd of August
Veemkade
Sumatrakade

30. Example dashboard outcomes
• Newly developed algorithm to distinguish between
occupancy time and walking time
• Other examples show volumes and OD flows
• Results used for real-time intervention, but also for
planning of SAIL 2020 (simulation studies)
0
5
10
15
20
25
30
11 12 13 14 15 16 17 18 19
verblijftijd looptijd
1988
1881
4760
4958
2202
1435
6172
59994765
4761
4508
3806
3315
2509
1752
3774
4061
2629
1359
2654
2139
1211
1439
2209
1638
2581
31102465
3067
2760

31. Example dashboard outcomes
• Social media analytics show potential of using information as an additional
source of information for real-time intervention and for planning purposes

32. 32
Urban Mobility
Lab Amsterdam
• AMS project
• Multi-modal data
platform to unravel
multi-model traffic
patterns
• Example application
example during triple
event in Arena area
• Shows potential for
use of UML in crowd
management
(demand prediction)
and in more
comprehensive
multi-modal
transportation and
traffic management
system
Freeway and urban arterial data Data from parking garages in and around event area
Chipcard public transport data Pedestrian counts from video
Loops FCD
GSM
Surveys Emissions
and energy Chip card
data
TwitterRoad works
maintenance
PT schedules
updates
Events,
incidents,
accidentsDemographic
data
REAL-TIME
INFORMATION
OFF-LINE MOBILITY INFORMATION
MOBILITY SERVICES
SHORT-CYCLIC
ASSESSMENT
LONG-TERM
PATTERNS
UML DATABASE
Status infrastructure weather News, information
Vecom data
Existing (open)
data platforms
DATA FUSION, PROCESSING & DIAGNOSTICS TOOLBOX
For SAIL, microscopicsimulation was used forplanning the event…How do these models
work?

33. Modelling for planning
Application of differential game theory:
• Pedestrians minimise predicted walking cost, due
to straying from intended path, being too close to
others / obstacles and effort, yielding:
• Simplified model is similar to Social Forces model of Helbing
Face validity?
• Model results in reasonable macroscopic flow characteristics (capacity
values and fundamental diagram)
• What about self-organisation?
33
This memo aims at connecting the microscopic modelling principles underlying the
social-forces model to identify a macroscopic flow model capturing interactions amongst
pedestrians. To this end, we use the anisotropic version of the social-forces model pre-
sented by Helbing to derive equilibrium relations for the speed and the direction, given
the desired walking speed and direction, and the speed and direction changes due to
interactions.
2. Microscopic foundations
We start with the anisotropic model of Helbing that describes the acceleration of
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Level of anisotropy
reflected by this
parameter
~vi
~v0
i
~ai
~nij
~xi
~xj

34. • Simple model shows plausible self-
organised phenomena
• Model also shows flow breakdown
in case of overloading
• Similar model has been successfully
used for planning of SAIL, but it is
questionable if for real-time
purposes such a model would be
useful, e.g. due to complexity
• Coarser models proposed so far turn
out to have limited predictive
validity, and are unable to
reproduce self-organised patterns
• Develop continuum model based on
game-theoretical model NOMAD…
Microscopic models aretoo computationallycomplex for real-timeapplication and lack niceanalytical properties…

35. Modelling for planning and real-time predictions
• NOMAD / Social-forces model as starting point:
• Equilibrium relation stemming from model (ai = 0):
• Interpret density as the ‘probability’ of a pedestrian being present, which gives a macroscopic equilibrium
relation (expected velocity), which equals:
• Combine with conservation of pedestrian equation yields complete model, but numerical integration is
computationally very intensive
35
sented by Helbing to derive equilibrium relations for the speed and the direction, given
the desired walking speed and direction, and the speed and direction changes due to
interactions.
2. Microscopic foundations
We start with the anisotropic model of Helbing that describes the acceleration of
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
denote the density, to be interpreted as the probability that a pedestrian is present on
location ~x at time instant t. Let us assume that all parameters are the same for all
pedestrian in the flow, e.g. ⌧i = ⌧. We then get:
(3) ZZ ✓
||~y ~x||
◆ ✓
1 + cos xy(~v)
◆
~y ~x
We start with the anisotropic model of Helbing that describes the acceleration of
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
denote the density, to be interpreted as the probability that a pedestrian is present on
location ~x at time instant t. Let us assume that all parameters are the same for all
pedestrian in the flow, e.g. ⌧i = ⌧. We then get:
(3)
~v = ~v0
(~x) ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
~y ~x
||~y ~x||
⇢(t, ~y)d~y
Here, ⌦(~x) denotes the area around the considered point ~x for which we determine the
interactions. Note that:
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp

Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
denote the density, to be interpreted as the probability that a pedestrian is present on
location ~x at time instant t. Let us assume that all parameters are the same for all
pedestrian in the flow, e.g. ⌧i = ⌧. We then get:
(3)
~v = ~v0
(~x) ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
~y ~x
||~y ~x||
⇢(t, ~y)d~y
Here, ⌦(~x) denotes the area around the considered point ~x for which we determine the
interactions. Note that:
(4) cos xy(~v) =
~v
||~v||
·
~y ~x
||~y ~x||

36. Modelling for planning and real-time predictions
• Taylor series approximation:
yields a closed-form expression for the equilibrium velocity , which is given by the equilibrium
speed and direction:
with:
• Check behaviour of model by looking at isotropic flow ( ) and homogeneous flow
conditions ( )
• Include conservation of pedestrian relation gives a complete model…
36
2 SERGE P. HOOGENDOORN
From this expression, we can find both the equilibrium speed and the equilibrium direc-
tion, which in turn can be used in the macroscopic model.
We can think of approximating this expression, by using the following linear approx-
imation of the density around ~x:
(5) ⇢(t, ~y) = ⇢(t, ~x) + (~y ~x) · r⇢(t, ~x) + O(||~y ~x||2
)
Using this expression into Eq. (3) yields:
(6) ~v = ~v0
(~x) ~↵(~v)⇢(t, ~x) (~v)r⇢(t, ~x)
with ↵(~v) and (~v) defined respectively by:
(7) ~↵(~v) = ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
~y ~x
||~y ~x||
d~y
and
(8) (~v) = ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
||~y ~x||d~y
To investigate the behaviour of these integrals, we have numerically approximated
them. To this end, we have chosen ~v( ) = V · (cos , sin ), for = 0...2⇡. Fig. 1 shows
FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODELING 3
Furthermore, we see that for ~↵, we find:
(10) ~↵(~v) = ↵0 ·
~v
||~v||
(Can we determine this directly from the integrals?)
From Eq. (6), with ~v = ~e · V we can derive:
(11) V = ||~v0
0 · r⇢|| ↵0⇢
and
(12) ~e =
~v0
0 · r⇢
V + ↵0⇢
=
~v0
0 · r⇢
||~v0
0 · r⇢||
Note that the direction does not depend on ↵0, which implies that the magnitude of
the density itself has no e↵ect on the direction, while the gradient of the density does
influence the direction.
2.1. Homogeneous flow conditions. Note that in case of homogeneous conditions,
FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODELING 3
Furthermore, we see that for ~↵, we find:
(10) ~↵(~v) = ↵0 ·
~v
||~v||
(Can we determine this directly from the integrals?)
From Eq. (6), with ~v = ~e · V we can derive:
(11) V = ||~v0
0 · r⇢|| ↵0⇢
and
(12) ~e =
~v0
0 · r⇢
V + ↵0⇢
=
~v0
0 · r⇢
||~v0
0 · r⇢||
Note that the direction does not depend on ↵0, which implies that the magnitude of
the density itself has no e↵ect on the direction, while the gradient of the density does
influence the direction.
2.1. Homogeneous flow conditions. Note that in case of homogeneous conditions,
i.e. r⇢ = ~0, Eq. (11) simplifies to
(13) V = ||~v0|| ↵0⇢ = V 0
↵0⇢
α0 = πτ AB2
(1− λ) and β0 = 2πτ AB3
(1+ λ)
4.1. Analysis of model properties
Let us first take a look at expressions (14) and (15) describing the equilibrium290
speed and direction. Notice first that the direction does not depend on ↵0, which
implies that the magnitude of the density itself has no e↵ect, and that only the
gradient of the density does influence the direction. We will now discuss some
other properties, first by considering a homogeneous flow (r⇢ = ~0), and then
by considering an isotropic flow ( = 1) and an anisotropic flow ( = 0).295
4.1.1. Homogeneous flow conditions
Note that in case of homogeneous conditions, i.e. r⇢ = ~0, Eq. (14) simplifies
sions (14) and (15) describing the equilibrium
at the direction does not depend on ↵0, which
density itself has no e↵ect, and that only the
nce the direction. We will now discuss some
ng a homogeneous flow (r⇢ = ~0), and then
= 1) and an anisotropic flow ( = 0).
ns
us conditions, i.e. r⇢ = ~0, Eq. (14) simplifies
| ↵0⇢ = V 0
↵0⇢ (16)
!
v =
!
e ⋅V

37. 37
Macroscopic model
yields plausible results…
• First macroscopic model able to
reproduce self-organised patterns
(lane formation, diagonal stripes)
• Self-organisation breaks downs in
case of overloading
• Continuum model seems to
inherit properties of the
microscopic model underlying it
• Forms solid basis for real-time
prediction module in dashboard
• First trials in model-based
optimisation and use of model for
state-estimation are promising

38. 38
Prevent blockades by separating flows in
different directions / use of reservoirs
Distribute traffic over available
infrastructure by means of guidance or
information provision
Increase throughput in particular at pinch
points in the design…
Limit the inflow (gating) ensuring that
number of pedestrians stays below critical
value!
Principles of crowd
management
• Developing crowd
management
interventions using
insights in pedestrian flow
characteristics
• Golden rules (solution
directions) provide
directions in which to think
when considering crowd
management options
Application example during
Al Mataf design

40. Using insights for design and management
Separate ingoing
and outgoing flows Gates limit inflow to
mosque and Mutaaf
Pilgrims are guided to
first and second flow
Pinch points in current
design are removed

41. Back to SAIL…
…Integrated Transport Management concepts for the Amsterdam Area

42. 42
Practical Pilot Amsterdam
• Unique practical pilot INM
• Fully automated coordinated
deployment of traffic management
measures to improve throughput on
A10 West
• First phase successful, second phase
currently running
• Towards traffic management 2.0:
integrating road-side and in-car traffic
measures for state estimation (data
fusion) and actuation (anticipatory
traffic management)
• Working on Melbourne pilot (Hai Le
Vu, Swinburne)
http://www.ipam.ucla.edu/programs/workshops/workshop-iv-decision-support-for-traffic/?tab=schedule

43. Future of Traffic Management
• Transition from road-side based to in-car
based traffic management
• Use of car as a sensor and as actuator
• Two examples:
• Anticipatory Traffic Management
• Suppressing wide-moving jams using
individual speed control
• Bi-level game: users get information and
respond to ramp-metering and traffic control
• Example shows how by anticipated user-
response on changing conditions

44. Future of Traffic Management
• Transition from road-side based to in-car
based traffic management
• Use of car as a sensor and as actuator
• Two examples:
• Anticipatory Traffic Management
• Suppressing wide-moving jams using
individual speed control
• SPECIALIST algorithm was designed to
remove wide-moving jams using VSL
• Successful tests (simulation) using vehicles
as actuators even at limited penetration levels
Practical pilot results (VSL) In-car Specialist (5% penetration)
Wide-moving jam reduces road capacity with 30%!
Without Specialist wide moving jam travels with a fixed speed in the opposite direction of traffic
Specialist limits the inflow into the jam which therefore resolves

45. Closing remarks
• Urbanisation yields both new challenges and new opportunities for sustainable
transport and accessibility (e.g. via seamless multi-modal transport) and motivates
focus on Intelligent Urban Mobility under umbrella of Smart City projects such as AMS
• Increasing share of active modes can have major impacts on accessibility,
liveability and health!
• Focus on keeping urban pedestrian and bike safety and comfort at high levels by
means active mode traffic management (e.g. crowd management) offers
unprecedented scientific challenges in data collection, modelling and simulation,
and control and management!
• Co-existence with other future transport concepts such as self-driving vehicles will be
a challenge as will, in particular in dense cities such as Amsterdam
45

46. More information?
• Hoogendoorn, S.P., van Wageningen-Kessels, F., Daamen, W., Duives, D.C., Sarvi, M. Continuum theory for pedestrian
traffic flow: Local route choice modelling and its implications (2015) Transportation Research Part C: Emerging
Technologies, 59, pp. 183-197.
• Van Wageningen-Kessels, F., Leclercq, L., Daamen, W., Hoogendoorn, S.P. The Lagrangian coordinate system and what
it means for two-dimensional crowd flow models (2016) Physica A: Statistical Mechanics and its Applications, 443, pp.
272-285.
• Hoogendoorn, S.P., Van Wageningen-Kessels, F.L.M., Daamen, W., Duives, D.C. Continuum modelling of pedestrian
flows: From microscopic principles to self-organised macroscopic phenomena (2014) Physica A: Statistical Mechanics
and its Applications, 416, pp. 684-694.
• Taale, H., Hoogendoorn, S.P. A framework for real-time integrated and anticipatory traffic management (2013) IEEE
Conference on Intelligent Transportation Systems, Proceedings, ITSC, art. no. 6728272, pp. 449-454.
• Hoogendoorn, S.P., Landman, R., Van Kooten, J., Schreuder, M. Integrated Network Management Amsterdam: Control
approach and test results (2013) IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, art. no.
6728276, pp. 474-479.
• Le, T., Vu, H.L., Nazarathy, Y., Vo, Q.B., Hoogendoorn, S. Linear-quadratic model predictive control for urban traffic
networks (2013) Transportation Research Part C: Emerging Technologies, 36, pp. 498-512. 46