Unravelingactivemodetrafficandtransportation

Towards a Theory of Pedestrian and Bikes Traffic and Travel Prof. dr. Serge Hoogendoorn

1

Overviewoftalk…

• Some stats on Dutch active mode mobility

• The ALLEGRO precursor: pedestrian and crowd modelling and management research at TU Delft

• The ALLEGRO project: outlook, overview and first results

2

Anyideawhothisis?

ButalsoinTheNetherlands,weneedtobereal!

4

Orwhothisis?

DutchPrimeMinisterMarkRutteonhiswaytomeetUSpresidentObama(allegedly…)

5

Itisamatterofimage…

Iraniandelegationfelt“ashamed”whenPMRuttearrivedathisappointmentbybike…

6Dutchcycling:notjustforthe“strongandfearless”

Modesharesforbikeandfeet…

• In terms of number of trips, bike + walking share is high

• Share of cycling / walking in distance travelled is however relatively low…

• But… bike is very often used as access / egress mode (40% of train trips on homeside; 11% at activity side + extensive use of PT-bike)

• What about the travel purposes of using the bike or walking?

7

Travelmotivesforcyclistsandpedestrians

Travelrangebikeande-bike

• Average observed travel ranges for bikes = 3.5 kilometers; for e-bike range = 5.5 km• Variation is large and dependent on age & trip purpose (commuter trips are shorter) • Acceptable distance bike is around 7.5 km; for e-bike around 15 km

• No data on walking…• Note that many of the trips in cities

are below 8 km (around 70% in NL)• Also note that from an urban

planning perspective, strategies could be aimed at increasing this number further (e.g. by mixing functions)

Shows (to an extent)

potential of (e-) cycling in a

city given that cycling can

be made sufficiently

attractive

0 10 20 30 40 50 60 70 80 90 100

0,1 tot 0,5 km0,5 tot 1,0 km1,0 tot 2,5 km2,5 tot 3,7 km3,7 tot 5,0 km5,0 tot 7,5 km7,5 tot 10 km10 tot 15 km15 tot 20 km20 tot 30 km30 tot 40 km40 tot 50 km

50 km of meer

Cumulative % of trips

Dis

tanc

e cl

ass

Sowhatmakesanactivemodetrip‘attractive’

• Well, that is not yet fully clear: different studies (using different models, types of data, etc.) provide different perspectives

• In general travellers trade-off of different factors when choosing to cycle or walk / when choosing a particular route

• Comprehensive theory of active mode travel behaviour based on observed travel behaviour is however still lacking, but key to design and effective interventions

10

Trip purpose

Personal chars. Distance Traveltime

Safety Scenery

Grade Crowdedness

Intersectiondelay Signage

Interact.fastmodes Weatherprotection

Weather Directness

Helmetrequired Attractions

Attitude

Sowhyhasactivemodemobilitybeensosuccessful

• Multiple factors have made Dutch cycling (and walking) successful:- Cycling culture and image- Highly connected bicycle and walking networks - Good infrastructure (separated) and facilities (e.g. for parking) - Good education (at school / driving lessons)- Traffic and insurance laws - Prioritisation of active modes in specific parts of cities

• Because of these factors, walking and cycling are efficient and safe and therefore attractive modes of transports / parts of a multi-modal trip

• Benefits include reduced congestion levels, improved liveability and health• Maintaining increasing active mode shares is high on the agenda: recent measures

involve infrastructure improvements, push / pull measures, bike share schemes, and ITS11

Examplesofinfrastructureimprovements

12

• Special infrastructure such as the ‘cycle street’ (fietsstraat; cars as guests) and ‘cycle freeway’

• PlusNet Bike: ‘coarse’ network with bike priority to complement fine grained network

Cycle‘highway’

Cycle‘street’

Examplesofinfrastructureimprovements

• PlusNetBike:‘coarse’networkwithbikeprioritytocomplementfinegrainednetwork

• Improvingbicycleparkingfacilities

• Specialinfrastructuresuchasthe‘cyclestreet’(fietsstraat;carsasguests)and‘cyclehighway’

ThePT-bike(OV-fiets)bynumbers…

• Introduced in 2003• OV-fiets: 400 EUR a piece

(purchase): CHEAP! • Available at 277 locations

(railway and metro stations)• 177.000 subscribers• 8500 bicycles• 1,900,000 trips a year• Cost: 3,35 € per (return) trip,

10 € annual subscription fee

Typicalbikeincentives(BeterBenutten)

• Simply saying that cycling is “better” often does not work (public campaigns): targeted measures are!

• Som examples in Beter Benutten:- Discount purchasing (e-) bike, bike maintenance,

insurance- Financial compensation for bicycle use per km cycled- Free trial (e-) bike- Gamification: colleagues compete alone or in teams

against each other for most cycled km's.- Park & bike facilities at outskirts of cities- Use of trendy bikes (e.g. wooden bikes Zuidas)

• E-bike is becoming more important in proposed measures14

Insum…

• Potential for active mode mobility in Australian cities appears high (travel distances, potential role in multi-modal trips)

• Possible benefits including health, liveability, and congestion levels, but good insights in impacts and ROI are needed

• Perception of cycling by general public: - Reducing “the sport in bicycle transport”- Improving safety, comfort and ease of use- Making cycling hip, change the

demographic! - Also: attitude of car-drivers

• Different (push, pull, marketing, infrastructure) interventions are possible15

Changingtheimageofthebicycle?Trendybikes!

• First3Dprintedbike,developedbyTUDelftaspartofaIndustrialDesignstudentproject

• Bikesaregettingsmarteraswell:GPSequippedsmartbikeconnectingtosmartphone

• Whichotherinnovationscanweexpect?

VanMoofSmartBikes

TrendsinmodeshareinAmsterdamarea

• Combination of (policy) interventions, planning decisions, and trends have lead to considerable mode share changes

• Average number of bike trips in The Netherlands has increased (9% since 2004)

• Closer look at (e.g.) Amsterdam mode shares showing trends over past years: cycling and walking are main modes of transport

• Big impacts on emissions (4-12% reduction), as well as accessibility and health

• But these positive trends also has some ‘negative’ (but interesting) side effects…

Side-effectsofincreasingactivemodeshares…

Bikecongestioncausingdelaysandhindrance

Overcrowdingduringeventsandregularsituationsalsoduetotourists

Overcrowdedpublictransporthubs

Not-so-seamlesspublictransport

Bikeparkingproblems&orphanbikes

Bikecongestioncausingdelaysandriskybehaviouratintersections

Limitstotrafficandtransportationmodels

• Proposition: active modes are not represented adequately in our current models

• This hampers answering questions about impacts of investments and interventions:

- What are the benefits of investing in walking and cycling infrastructure?

- What are the impacts of push measures, making certain areas less attractive for cars

- How cost-efficient are investments in parking facilities near stations?

• Impacts refer to e.g. modal shift, on accessibility, pollution, health, etc…

Limitstotrafficandtransportationmodels

• Why can’t we use our regular models? - Level of detail in (planning) models often

insufficient (large zones) for short-distance trips, networks used are too coarse, data for calibration / validation are lacking

- Although some concepts carry over (e.g. fundamental diagram), behaviour of pedestrians and cyclists is fundamentally different from cars and turns out to be rather complex…

• Dedicated theory and models are required both for operations and for travel behaviour!

• Are these currently available? Well…

Whyisourknowledgelimited?

• Traffic and Transportation Theory for pedestrians and even more so for cyclists is still young!

• Why? In our field, DATA is key in the development of theory and models

• Theory for active modes has suffered from the lack of data…

• Collecting representative data of sufficient detail is a / the key challenge in active mode modelling!

• Some examples of different data collection exercises that we have performed…

21

Understanding transport begins and ends with data

Let’sstartwiththepedestrians…

Pedestrian&CrowdResearch

The ALLEGRO Precursor Prof. dr. Serge Hoogendoorn

23

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?

24

T =N

C

Npeopleinarea

Doorcapacity:C

N

C

Importantinsightsfromdataanalysis…

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:

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• Insightinmorecomplexsituations

• Real-lifesituationsin(public)spacesoftenmorecomplex

• Limitedempiricalknowledgeonmulti-directionalflowsmotivatedfirstwalkerexperimentsin2002

• Worldpremiere,manyhavefollowed!

• Resultedinauniquemicroscopicdataset

Firstinsightsintoimportanceofself-organisationinpedestrianflows

27

Discoveryofself-organisationdoingwalkerexperiments

Is there also self-

organisation in

bicycle flow?

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…

28

Bi-directionalexperiment

Studyingself-organisationduringrockconcertLowlands…

Pedestrianflowoperations…

So with this wonderful

self-organisation, why do

we need to worry about

crowds at all?

30

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…

32

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

• Flowperformance(effectivecapacity)decreasessubstantially,potentiallycausingmoreproblemsasdemandstaysatsamelevel

• Importanceof‘keepingthingsflowing’,i.e.keepingdensityatsubcriticallevelmaintainingefficientandsmoothflowoperations

• Hassevereimplicationsonthenetworklevel

Whycrowdmanagementisnecessary!

Efficientself-organisation

Faster=slowereffect

Blockadesandturbulence

“Thereareseriouslimitationstotheself-organisingabilities ofpedestrianflowoperations”

Reducedproductionofpedestriannetwork

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How to model self-

organisation?

Abitoftheory…

• We build a mathematical model on hypothesis of the “pedestrian economicus” assuming that pedestrians aim to minimise predicted effort (cost) of walking, defined by:

- Straying from desired direction and speed- Walking close to other pedestrians (irrespective of direction!)- Frequently slowing down and accelerating

• Pedestrians predict behaviour of others and may communicate• The resulting (simple!) model calculates acceleration of a ped:

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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

• CollecteddatahasformedbasisfordevelopmentofmicroscopicsimulationmodelNOMAD

• Modelprovidesadequateestimatesofbottleneckcapacities

• Modelshowsplausibleself-organisedphenomena,suchasthebi-directionallanes

• Itallowsstudyingtheconditionsunderwhichefficientself-organisationoccurs…

• Modelpredictsflowbreakdownwhendemandaretoohigh

• Itshowshowself-organisationislimitedbyheterogeneityinflow

• Pedestrianflowmodelsarequitecommonplace

• Althoughnotthoroughlyvalidated,applicationforplanningpurposes(e.g.SAIL)occurquiteoften

• Inparticularrouteandactivitychoiceremainsachallengingprocesstocorrectlydescribe

• Canwealsodevelopsuchmodelsforbicycleflowoperations?

39

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

What about dynamic

interventions?

41Engineering the future city.

Towardsacrowdmonitoringandmanagementdashboard:SAIL2015• Biggest(andfree)publiceventintheNederland,organisedevery5yearssince1975

• OrganisedaroundtheIJhaven,Amsterdam

• Thistimearound600tallshipsweresailingin

• Around2,3millionnationalandinternationalvisitors

• SAILprojectentaileddevelopmentofacrowdmanagementdecisionsupportsystem

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CrowdMonitoring(andManagement)forEvents• 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?

ActiveModeUrbanMobilityLab CrowdMonitoringDashboardforevents(SAIL,EuroPride,…)

• GPS data (e.g. using apps)• Linguistic analyses social media (sentiments)• Social media analytics (personal characteristics)• Wifi / Bluetooth trackers / counting cameras• Crowdsourcing / surveying

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311024653067

2760

ActiveModeUrbanMobilityLab CrowdMonitoringDashboardforevents(SAIL,EuroPride,…)

• GPS data (e.g. using apps)• Linguistic analyses social media (sentiments)• Social media analytics (personal characteristics)• Wifi / Bluetooth trackers / counting cameras• Crowdsourcing / surveying

Possibledatasources?Tappingintosocialmediadata

• Social-media data provides information we have not really tapped into yet

• Example data:- user gender, age,

individual city roles

- venues visited

- topics and tags

- sentiment

- spatio-temporal distribution

ExampleSocial-MediaanalysisduringSAIL2015

Newinsightsinvisitorbehaviourduringevents…

46

• Data collection at events (e.g.: SAIL and Mysteryland) provides new insights into activity / route choices

• Examples event route choice: - Data collected during SAIL

showed factors determining choice for route (e.g. crowdedness, attraction, etc.)

- Data Mysteryland showed relation destination choice and “music taste” (latent class)

• Support planning & operations

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

Real-time personalised guidance

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

Real-time personalised guidance

Unravelingactivemodetrafficandtransportation

The ALLEGRO programme Prof. dr. Serge Hoogendoorn

49

TheALLEGROprogramme

unrAvelLing sLow modE travelinG and tRaffic: with innOvative data to a new transportation and traffic theory for

pedestrians and bicycles”

• 2.9 million EUR 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.• Building on years of experiments in pedestrian flow research done at Transport & Planning

Newdatasourcesallowclearerinsights…

• In 2015, the “Fietstelweek” was held providing GPS information for over 50.000 participants

• Estimation of choice models allowing quantification of determinants of route choice

• Important factors turn out to be:- Distance (and travel time)- Number of intersections / km (1 intersection = up to 500 m)- Route overlap (showing evidence of recourse)- Scenery, separate infrastructure (but to lesser extent)

• Trade-off between distance / intersections changes over day (distance more important in morning peak)

• Advanced modelling paradigms seem necessary to capture different attitudes (e.g. latent class models)

51

Travellersknowledgeofthenetwork?

• Estimation results turn out to be sensitive to choice set generation

• Key is in understanding:- which route options people know (subjective

choice set) including learning / memory decay- what the characteristics of these alternatives

are (survey knowledge)• Pilot shows distortion in distance and direction

and how it is affected by objective distance, trip frequency, how often location is visited

• E.g.: people on average overestimate distance; variation between people is huge!

• Implications for modelling / predictions! 52

TheStudentHotelproject

• Provideslonger-termhousingtostudents(e.g.inAmsterdam,TheHague,Rotterdam,Eindhoven,Groningen)

• ProvidesguestswithGPSequippedbike• Trackingstudentswillprovideroutechoicedataandinformationonhowcyclingpatternschanges:- Whichroutesdopeopleactuallyknowanduse?- Howdoes(so-calledsurvey)knowledgechangeovertime(includingdistanceandperceptiondistortion)

• Duringstay,multipleinterventionsaredonetochangestudentsattitudetowardssustainability:willthischangetheirattitudetowardscycling?

Pedestrianandcycleflowoperations

• Controlled experiments allow ‘setting the stage’ such that desired conditions are met

• Relatively easy to process video and derive very detailed (microscopic) data

• First walker experiments done by TU Delft showed key phenomena in pedestrian flow and allowed determining key flow characteristics (e.g. capacity and its determinants, self-organisation)

• Recently, unique cycling experiments where conducted to understand cycling behaviour (including interactions)

54

Pedestrianandcycleflowoperations

• Application of advanced video analysis software allows collecting detailed field data

• Data provides insight into pedestrian and cycle flow operations occurring “in the field”

• First results include capacity estimation by looking at cycle-following behaviour (so-called composite headway models)

• Tracking cyclist from video allows us to understand individual behaviour (speed choice, interactions, queuing at intersections, etc.)

• Combination with data from controlled experiments allows model development, calibration and validation

55

Exampleapplication:testingsharedspaceconcepts…

56

-60 -40 -20 0 20 40 60x (m)

-30

-20

-10

0

10

20

30

y (m

)

25 30 35 40 45 50 55 60 65 70 75time (s)

0

0.5

1

effic

ienc

y (-)

• Simulation results are plausible! E.g. reasonable capacity values, fundamental diagram, etc.

• Forms basis to further our understanding of bicycle flow characteristics…

• What about mixed flows? That is: can we predict under which conditions shared space concepts (mixing pedestrians and cyclist) work or fail?

• Model could predict feature observed in real shared space situations reasonably well (although more analyses are needed)

Interactionothermodesrequiringbettermodels

57

• Driving automation gaining lots of attention, but focus appears to be on freeway applications

• Feasibility automation in dense urban areas:- Sufficient space for own infrastructure if

needed? Can we mix automated and non-automated vehicles?

- Throughput and safety (partial automation)

- Privately owned vehicles or shared services?

• Interaction with vulnerable road users is area of concern from the perspective of efficiency and safety

Factorsaddingtocomplexityinactivemodemobility

• Large number of possible attributes (distance, separate infra, safety, intersections, grade, scenery)

• Context plays huge part in behaviour and operations:- Importance depends on trip purpose, gender,

attitude, mental state- Shape fundamental diagram depends on context

• Complex interactions lead to chaos-like phenomena:- Self-organisation as fundamental concept, but… - Spontaneous flow break-downs occur

• Scratching the surface, but lots of work to be done to unravel this complex behaviour…

• Main Ambition of the ALLEGRO project58

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

Real-time personalised guidance

Fromsimpledesignguidelines…

Separateingoingandoutgoingflows Gateslimitinflowto

mosqueandMutaaf

Pilgrimsareguidedtofirstandsecondflow

Pinchpointsincurrentdesignareremoved

61

Toadvancedpredictivecontrolsystems…• SAIL2015andEuropride2016(dashboard)

• Mysteryland2016(CrowdSourcing)

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?

DesignsupporttoolsforActiveModenetworks

• Set up tools and guidelines to support network and infra design based on…

• Knowledge of attractiveness of walking & cycling routes (demand level)

• Knowledge of operations (levels-of-service) for constituent elements given expected demand levels (supply level)

62

Network+infradesign

Demandmodel

Operationsmodel

Networkstructure

Multi-modallinks

Multi-modalnodes

Level-of-Service

Designmethodology

Successfulshared-spaceimplementation

63Exampleshared-spaceregionAmsterdamCentralStation SharedspaceinMelbourne

Supportandguidelinesforspecificelementsinthedesign…

• SharedspaceconceptappliedsuccessfullyinAmsterdam

• Conceptappearstoworkconditionally:nottoohighdemands,nogrouphasverylowshare

• Heterogeneitylimitsefficiency(“freezingbyheating”)

• Communicationandcooperationamongstparticipantsappearsveryimportant…

64Exampleshared-spaceregionAmsterdamCentralStation

Designpromotingsafebehaviour?

ActiveModeTrafficManagement?• JointworkofTUDelftandTNOshowedpotentialofcombiningspeedadvice(e.g.viaapp,orlights)andgreenwaves(reductionof#stopsof45%)

• Differentexamplesofbiketrafficmanagement,suchasbikeparkinginformationUtrechtanddynamicroutingarepiloted

• Currentworkfocussesonprovidingreal-timeinfoviaapps(tobetestedduringdanceeventMysteryland)

• Potentialforeffectiveapproachesincreaseswithincreasedconnectivity

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

Real-time personalised guidance

67

Q&A

Providingtheorysupportingactivetransportplanning

Bikesafetybynumbers…

68

• Cycling is relatively safe (in NL: about 200 deaths each year) although increase in safety has stagnated in the last decade

• Safety by numbers principle (see figure): cause and effect?

• In general, elderly are at risk (while they cycle more and more)

• Lack of data on e-bike safety makes drawing conclusions difficult, but safety issues for elderly are likely

• Helmets are not obligatory in NL (some controversy here!): limited evidence suggest that they have “modestly positive (-18%) to neutral safety impacts”; high impact on attractiveness (impact health outweighs safety)

Trafficsafetybynumbers

• Increase accidents 9% in 2015; strong difference male and female…

69