unraveling urban traffic flows

UnravellingUrbanTrafficFlows

From new insights to advanced solutions… a work in progress Prof. dr. Serge Hoogendoorn

1

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 economyWater, energy, food, waste

Smart infrastructures

Urban big dataInternet of Everything

Digital fabricationSmart mobility

Resilient, clean and healthy urban environmentBlue-green infrastructuresSocial & responsible design

Proposition: using the city as a living lab to explore impact and find possibilities of these (and other) trends on mobility and other sectors…

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.

Enteringtheurbanage

• 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

Challenges…

• Accessibilityisamajorissueinmanycities(Amsterdam,Melbourne)

• Mostdelaysareexperiencedincities(notonfreeways!),yetfreewayshavereceivedmuchattentioninthepast…

• Atthesametime,(re-)urbanisationopensupmanynewalleywaysforsustainablemobility(activemodes,seamlessmulti-modaltransport,sharedmobility,autonomousdriving)

• Sowhatdoweseeaskeythemes?

Relevantresearchdomainsformobilitytheme

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 logistics7

TrendsinmodeshareinAmsterdamarea

• 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…

Side-effectsofincreasingactivemodeshares…

Bikecongestioncausingdelaysandhindrance

Overcrowdingduringeventsandregularsituationsalsoduetotourists

Overcrowdedpublictransporthubs

Not-so-seamlesspublictransport

Bikeparkingproblems&orphanbikes

Bikecongestioncausingdelaysanddangerousbehaviouratintersections

TheALLEGROprogramme

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!

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 BehaviourTraffic Flow Operations

Route Choice and Activity

Scheduling Theory

Planning and design guidelines

Organisation of large-scale

events

Data Insights

Tools

Models Impacts

Network Knowledge Acquisition (learning)

Factors determining route choice

12Engineering the future city.

Today’stalk

• Focusonactivemodesinparticularonpedestrianandcrowds

• UseSAILeventasthedrivingexampletoillustratevariousconceptsinmonitoringandmanagement

• SAILprojectentaileddevelopmentofacrowdmanagementdecisionsupportsystem

13

SAIL?

• Biggest(andfree)publiceventintheNederland,organisedevery5yearssince1975

• OrganisedaroundtheIJhaven,Amsterdam

• Thistimearound600tallshipsweresailingin

• Around2,3millionnationalandinternationalvisitors

14

Engineeringchallenges foreventsorregularsituations…• Canweforacertaineventpredictifasafetyorthroughputissuewilloccur?

• Canwedevelopmethodstosupportorganisation,planninganddesign?

• Canwedevelopapproachestoreal-timemanagelargepedestrianflowssafelyandefficiently?

• Canweensurethatallofthesearerobustagainsunforeseencircumstances?

Deepknowledgeofcrowddynamicsisessentialtoanswerthesequestions!

Pedestrianflowoperations…

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

Npeopleinarea

Doorcapacity:C

N

C


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:


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 cause0 1 2 3 4 5 6 7 8

1

2

3

4

5

6 Looprichting

X-positie (in m)

Y-positie (in m) Walking direction

X-position (in m)

Y-po

sitio

n (in

m)

• 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

• Insightinmorecomplexsituations

• Real-lifesituationsin(public)spacesoftenmorecomplex

• Limitedempiricalknowledgeonmulti-directionalflowsmotivatedfirstwalkerexperimentsin2002

• Worldpremiere,manyhavefollowed!

• Resultedinauniquemicroscopicdataset

Firstinsightsintoimportanceofself-organisationinpedestrianflows

Fascinatingself-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

Fascinatingself-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-directionalexperiment

Studyingself-organisationduringrockconcertLowlands…


So with this wonderful

self-organisation, why do

we need to worry about

crowds at all?

22

Increaseinfrictionresultinginarcformationbyincreasingpressurefrombehind(force-

Pedestriancapacitydropandfaster-is-slowereffect• Capacitydropalsooccursinpedestrianflow

• Faster=slowereffect

• Pedestrianexperiments(TUDresden,TUDelft)haverevealedthatoutflowreducessubstantiallywhenevacueestrytoexitroomasquicklyaspossible(rushing)

• Capacityreductioniscausedbyfrictionandarc-formationinfrontofdoorduetoincreasedpressure

• Capacityreductioncausessevereincreasesinevacuationtimes

Intermezzo: given our understanding of the causes of the faster is slower effect, can you think of a solution?

HowoldDutchtraditionsmayactuallybeofsomeuse…

24

Break-downofefficientself-organisation• Whenconditionsbecometoocrowded(densitylargerthancriticaldensity),efficientself-organisation‘breaksdown’causing

• Flowperformance(effectivecapacity)decreasessubstantially,potentiallycausingmoreproblemsasdemandstaysatsamelevel

• Importanceof‘keepingthingsflowing’,i.e.keepingdensityatsubcriticallevelmaintainingefficientandsmoothflowoperations

• Hassevereimplicationsonthenetworklevel

ANewPhaseinPedestrianFlowOperations

• 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

Whycrowdmanagementisnecessary!

Efficientself-organisation

Faster=slowereffect

Blockadesandturbulence

“Thereareseriouslimitationstotheself-organisingabilities ofpedestrianflowoperations”

Reducedproductionofpedestriannetwork

Whycrowdmanagementisnecessary!

• 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

CrowdManagementforEvents• Uniquepilotwithcrowdmanagementsystemforlargescale,outdoorevent

• FunctionalarchitectureofSAIL2015crowdmanagementsystems

• Phase1focussedonmonitoringanddiagnostics(datacollection,numberofvisitors,densities,walkingspeeds,determininglevelsofserviceandpotentiallydangeroussituations)

• Phase2focussesonpredictionanddecisionsupportforcrowdmanagementmeasuredeployment(model-basedprediction,interventiondecisionsupport)

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?

TrackingSAILvisitorsusingGPSdevices

CentralStation

WalkingandchoicebehaviourofSAILvisitors onthe22ndofAugust

Veemkade

Sumatrakade

Exampledashboardoutcomes

• 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

Verblijf) en+looptijden+Veemkade

verblijftijd looptijd

1988

1881

4760

4958

2202

1435

6172

59994765 4761

4508

3806

3315

2509

17523774

4061

2629

13592654

21391211

1439

2209

1638

2581

311024653067

2760

Exampledashboardoutcomes

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

32

UrbanMobilityLabAmsterdam• AMSproject

• Multi-modaldataplatformtounravelmulti-modeltrafficpatterns

• ExampleapplicationexampleduringtripleeventinArenaarea

• ShowspotentialforuseofUMLincrowdmanagement(demandprediction)andinmorecomprehensivemulti-modaltransportationandtrafficmanagementsystem

Freewayandurbanarterialdata Datafromparkinggaragesinandaroundeventarea

Chipcardpublictransportdata Pedestriancountsfromvideo

Loops FCD GSM Surveys Emissionsand energy Chip card

dataTwitterRoad works

maintenance

PT schedules updates

Events, incidents, accidentsDemographic

data

REAL-TIME INFORMATION OFF-LINE MOBILITY INFORMATION

MOBILITY SERVICES SHORT-CYCLIC ASSESSMENT

LONG-TERMPATTERNS

UML DATABASE

Status infrastructure weather News, informationVecom data

Existing (open) data platforms

DATA FUSION, PROCESSING & DIAGNOSTICS TOOLBOX

For SAIL, microscopic simulation was used for planning the event… How do these models work?

Modellingforplanning

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

FROM MICROSCOPIC TO MACROSCOPIC INTERACTIONMODELING

SERGE P. HOOGENDOORN

1. Introduction

This memo aims at connecting the microscopic modelling principles underlying thesocial-forces model to identify a macroscopic flow model capturing interactions amongstpedestrians. 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, giventhe desired walking speed and direction, and the speed and direction changes due tointeractions.

2. Microscopic foundations

We start with the anisotropic model of Helbing that describes the acceleration ofpedestrian i as influence by opponents j:

(1) ~ai

=~v0i

� ~vi

⌧i

�Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆

where Rij

denotes the distance between pedestrians i and j, ~nij

the unit vector pointingfrom pedestrian i to j; �

ij

denotes the angle between the direction of i and the postionof j; ~v

i

denotes the velocity. The other terms are all parameters of the model, that willbe introduced later.

In assuming equilibrium conditions, we generally have ~ai

= 0. The speed / directionfor which this occurs is given by:

(2) ~vi

= ~v0i

� ⌧i

Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�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 onlocation ~x at time instant t. Let us assume that all parameters are the same for allpedestrian 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 theinteractions. Note that:

(4) cos�xy

(~v) =~v

||~v|| ·~y � ~x

||~y � ~x||1

Level of anisotropy reflected by this parameter

~vi

~v0i

~ai

~nij

~xi

~xj

• Simplemodelshowsplausibleself-organisedphenomena

• Modelalsoshowsflowbreakdownincaseofoverloading

• SimilarmodelhasbeensuccessfullyusedforplanningofSAIL,butitisquestionableifforreal-timepurposessuchamodelwouldbeuseful,e.g.duetocomplexity

• Coarsermodelsproposedsofarturnouttohavelimitedpredictivevalidity,andareunabletoreproduceself-organisedpatterns

• Developcontinuummodelbasedongame-theoreticalmodelNOMAD…

Microscopic models are too computationally complex for real-time application and lack nice analytical properties…

Modellingforplanningandreal-timepredictions

• 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



1. Introduction




(1) ~ai

=~v0i

� ~vi

⌧i

�Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆

where Rij



ij


i




(2) ~vi

= ~v0i

� ⌧i

Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆


i


~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


(4) cos�xy

(~v) =~v

||~v|| ·~y � ~x

||~y � ~x||1



1. Introduction




(1) ~ai

=~v0i

� ~vi

⌧i

�Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆

where Rij



ij


i




(2) ~vi

= ~v0i

� ⌧i

Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆


i


~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


(4) cos�xy

(~v) =~v

||~v|| ·~y � ~x

||~y � ~x||1



1. Introduction




(1) ~ai

=~v0i

� ~vi

⌧i

�Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆

where Rij



ij


i




(2) ~vi

= ~v0i

� ⌧i

Ai

X

j

exp

�R

ij

Bi

�· ~n

ij

·✓�i

+ (1� �i

)1 + cos�

ij

2

◆


i


~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


(4) cos�xy

(~v) =~v

||~v|| ·~y � ~x

||~y � ~x||1

Modellingforplanningandreal-timepredictions

• 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 approximatedthem. To this end, we have chosen ~v( ) = V · (cos , sin ), for = 0...2⇡. Fig. 1 showsthe results from this approximation.

0 1 2 3 4 5 6 7−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

0.25

angle

valu

e

α

1

α2

β

Figure 1. Numerical approximation of ~↵(~v) and �(~v).

For the figure, we can clearly see that � is independent on ~v, i.e.

(9) �(~v) = �0

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 ofthe 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⇢

i.e. we see a linear relation between speed and density. For the direction ~e, we then get:

(14) ~e =~v0

V + ↵0⇢= ~e0

In other words, in homogeneous density conditions the direction of the pedestrians isequal to the desired direction.

2.2. Isotropic walking behaviour. Let us also note that in case � = 1 (isotropicflow), and assuming that ⌦ is symmetric around ~x, we get:

(15) ~↵(~v) = ⌧A

ZZ

~y2⌦(~x)

exp

✓� ||~y � ~x||

B

◆~y � ~x

||~y � ~x||d~y = ~0

which means ↵0 = 0. In this case, we have:

(16) V = ||~v0 � �0 ·r⇢||

This expression shows that in this case, the speed is only dependent on the densitygradient. If a pedestrian walks into a region in which the density is increasing, the speedwill be less than the desired speed; and vice versa. Also note that in case of homogenousconditions, the speed will be constant and equal to the free speed. Note that this isconsistent with the results from Hoogendoorn, ISTTT-2003.

For the direction, we find:

(17) ~e =~v0 � �0 ·r⇢

||~v0 � �0 ·r⇢||

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 ofthe 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⇢

i.e. we see a linear relation between speed and density. For the direction ~e, we then get:

(14) ~e =~v0

V + ↵0⇢= ~e0

In other words, in homogeneous density conditions the direction of the pedestrians isequal to the desired direction.

2.2. Isotropic walking behaviour. Let us also note that in case � = 1 (isotropicflow), and assuming that ⌦ is symmetric around ~x, we get:

(15) ~↵(~v) = ⌧A

ZZ

~y2⌦(~x)

exp

✓� ||~y � ~x||

B

◆~y � ~x

||~y � ~x||d~y = ~0

which means ↵0 = 0. In this case, we have:

(16) V = ||~v0 � �0 ·r⇢||

This expression shows that in this case, the speed is only dependent on the densitygradient. If a pedestrian walks into a region in which the density is increasing, the speedwill be less than the desired speed; and vice versa. Also note that in case of homogenousconditions, the speed will be constant and equal to the free speed. Note that this isconsistent with the results from Hoogendoorn, ISTTT-2003.

For the direction, we find:

(17) ~e =~v0 � �0 ·r⇢

||~v0 � �0 ·r⇢||

α 0 = πτAB2 (1− λ) and β0 = 2πτAB3(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

to

V = ||~v0||� ↵0⇢ = V 0 � ↵0⇢ (16)

i.e. we see a linear relation between speed and density. The term ↵0 � 0

describes the reduction of the speed with increasing density.300

For the direction ~e, we then get:

~e =~v0

||~v0|| = ~e0 (17)

In other words, in homogeneous density conditions the direction of the pedestri-

ans is equal to the desired direction. Clearly, the gradient of the density yields

pedestrians to divert from their desired direction.

Looking further at the expressions for ↵0 and �0, we can see the influence of305

the various parameters on their size; ↵0 scales linearly with A and ⌧ , meaning

that the influence of the density on the speed increases with increasing values

of A and ⌧ . At the same time, larger values for B imply a reduction of the

influence of the density. Needs to be revised!

The same can be concluded for the influence of the gradient: we see linear310

scaling for A and ⌧ , and reducing influence with larger values of B. This holds

for the equilibrium speed and direction. Needs to be revised!

13

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

to

V = ||~v0||� ↵0⇢ = V 0 � ↵0⇢ (16)

i.e. we see a linear relation between speed and density. The term ↵0 � 0

describes the reduction of the speed with increasing density.300

For the direction ~e, we then get:

~e =~v0

||~v0|| = ~e0 (17)

In other words, in homogeneous density conditions the direction of the pedestri-

ans is equal to the desired direction. Clearly, the gradient of the density yields

pedestrians to divert from their desired direction.

Looking further at the expressions for ↵0 and �0, we can see the influence of305

the various parameters on their size; ↵0 scales linearly with A and ⌧ , meaning

that the influence of the density on the speed increases with increasing values

of A and ⌧ . At the same time, larger values for B imply a reduction of the

influence of the density. Needs to be revised!

The same can be concluded for the influence of the gradient: we see linear310

scaling for A and ⌧ , and reducing influence with larger values of B. This holds

for the equilibrium speed and direction. Needs to be revised!

13

!v = !e ⋅V

37

Macroscopicmodelyieldsplausibleresults…• Firstmacroscopicmodelabletoreproduceself-organisedpatterns(laneformation,diagonalstripes)

• Self-organisationbreaksdownsincaseofoverloading

• Continuummodelseemstoinheritpropertiesofthemicroscopicmodelunderlyingit

• Formssolidbasisforreal-timepredictionmoduleindashboard

• Firsttrialsinmodel-basedoptimisationanduseofmodelforstate-estimationarepromising

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!

Principlesofcrowdmanagement• Developingcrowdmanagementinterventionsusinginsightsinpedestrianflowcharacteristics

• Goldenrules(solutiondirections)providedirectionsinwhichtothinkwhenconsideringcrowdmanagementoptions

ApplicationexampleduringAlMatafdesign

Usinginsightsfordesignandmanagement

Separateingoingandoutgoingflows Gateslimitinflowto

mosqueandMutaaf

Pilgrimsareguidedtofirstandsecondflow

Pinchpointsincurrentdesignareremoved

BacktoSAIL…

…IntegratedTransportManagementconceptsfortheAmsterdamArea

42

PracticalPilotAmsterdam• UniquepracticalpilotINM

• FullyautomatedcoordinateddeploymentoftrafficmanagementmeasurestoimprovethroughputonA10West

• Firstphasesuccessful,secondphasecurrentlyrunning

• Towardstrafficmanagement2.0:integratingroad-sideandin-cartrafficmeasuresforstateestimation(datafusion)andactuation(anticipatorytrafficmanagement)

• WorkingonMelbournepilot(HaiLeVu,Swinburne)

http://www.ipam.ucla.edu/programs/workshops/workshop-iv-decision-support-for-traffic/?tab=schedule




































FutureofTrafficManagement

• 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

Start

FutureofTrafficManagement

• 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

Start

Practicalpilotresults(VSL) In-carSpecialist(5%penetration)

Wide-movingjamreducesroadcapacitywith30%!

WithoutSpecialistwidemovingjamtravelswithafixedspeedintheoppositedirectionoftraffic

Specialistlimitstheinflowintothejamwhichthereforeresolves

Closingremarks

• 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

Moreinformation?

• 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

unraveling urban traffic flows

Science