Prof. dr. ir. Serge P. Hoogendoorn Technische Universiteit Delft, AMS, Arane

Praktijkrelevantie TRAIL PhD studiesOnderzoek in beweging

Objective of talk…• Discuss relevant recently completed PhD

theses in a more or less coherent way: - Integrated Traffic Management (road-side and in-car)

- Evacuation and crowd management

• Show relation to running projects if needed…

• Show how our research focus is changing in the direction of Smart Urban Mobility and what key research areas are…

Some of our key topics: • Smart urban mobility

• Living labs, big data, data fusion

• Transition from road-side to in-car, cooperative systems

• Resilience of transport networks

Group: • 37 (+ 4) PhD students, 5 PDs, 5 UDs,

2 A/Profs, 1 AvL, 1 HGL…

Research domains: • Traffic operations, control,

management; ITS

• Active mode traffic and transportation (incl. crowd management)

• Public Transport Operations and Control

• Dynamic Rail Traffic Management

• Traffic safety and transport security

Who are we?

Traffic phenomena

• Consider a situation with high density traffic

• Speeds and distance headways are homogeneous

• Follow the situation over time…

• What happens?

Kai Yuan

Moving jam example• Wide moving jam (in Dutch: ’filegolf’)

• Occurs ‘spontaneously’ in unstable flow and is hence hard to predict

• Once present, a wide moving jam… - Has very predictable dynamics (moves at -18 km/h)

- May trigger new bottlenecks

- Increases un-safety, pollution, and fuel consumption

• Important fact: traffic congestion reduces capacity, but little empirical evidence on how much!

Wide moving jam A12

Capacity drop empirics• Picture shows relation between

speed in the queue upstream and the size of the capacity drop

• Result implies that extent of capacity drop is dependent on the type of congestion, e.g.: - A standing queue has a capacity drop

of 15-25%

- A wide moving jams yields a capacity drop of 30%

Kai Yuan

How to resolve?• Detect the wide moving jam

• Determine its severity (number of ‘excess’ vehicles to be ‘removed’ by limiting inflow)

• Determine if there is space to temporarily store vehicles that are witheld to flow into jam

• If solvable (available space > severity), implement control strategy

• Monitor to check if jam is resolved

Goof vd Weg

Isolated approach using VSL• SPECIALIST algorithm developed by TU Delft on behalf of RWS

• Fixed speed limit deployed over variable roadway stretch: SPECIALIST computes length that is required to remove excess vehicles

• After tuning, we had 2.8 activations per day resolving jam in 72% cases…

Goof vd Weg

Specialist in-car?

• COperative Speed Control ALgorithm developed by TU Delft / Berkeley

• Combination of in-car / roadside (or in-car only) variable speed limits

• Could be the ‘brains’ of more technically focussed tests

Goof vd Weg

SPECIALIST / COSCAL show how

using smart algorithms, road-side

systems and in-car systems could

be deployed effectively!

But: effectiveness is still limited:

• System only activates in 50%

of all WMJ cases?

• If activated, the WMJ not

resolved in 28% of the

activations?

Improving effectiveness?1. Increase # activations by increasing control space:

support variable speed limits by using other control measures, e.g. ramp metering: coordination

2. Increase % of WMJ resolved upon activation by correctly determine control task (vehicles ‘too many’ in moving jam) and available control space: improved state estimation

3. Only deploy in case moving jam: improving diagnostics

• Note that all these improvements equally apply to PPA fase 1 and are effectuated in phase 2 (and 3)

Increasing control space• COSCAL v2 integrates VSL and ramp metering

• Use of ramp as buffer to support VSL control approach making it much more effective

• Shows need for coordination of measures

• Extension to multiple on-ramps, intersection controllers, etc. (similar to PPA)

• But: requires insight into storage space on ramps / intersections and relation with bottleneck

Moving jam Relative space of Slave buffer = relative space of Master buffer

Goof vd Weg

Improve state estimation

• Improving queue estimates by fusing TomTom FCD data with intersection data using radar as ground truth

• Can we determine queue lengths from FCD data? How much will the estimates improve?

• Determine relation bottleneck and buffer based on FCD data to determine effectiveness of butter in real-timeCourtesy of FileRadar / Arane

s106

on-ramp

A10

Improved diagnostics• Use mix of traditional detection and FCD to

improve freeway state estimates

• Improved estimation allows determining surplus of vehicles in moving jam (head, tail, density jam) better

• Furthermore: development of advanced classification strategies pilot INM Melbourne using image processing techniques (using pattern recognition)

• Correct classification leads to better control

Paul van Erp

Reducing hardware

• When can we reduce the dependency on inductive loops for freeways and for urban networks?

• Will this be different for different functions (queue tail protection, ramp metering)?

• What will it mean for the way we control traffic: current ramp-metering requires flows, but we can also meter with only speed information…

• Need to rethink traffic management!1700

1800

1900

2000

2100

2200

2300

2400

VVU

(vtg

-u)

No metering, capdrop 500 vtg/h Metering + queue protection

10% queue underestimation 10% queue overestimation

• Joint research with TNO and RWS into ‘value of data’

• Reveals impact of data quality on controller effectiveness

• Example showing queue estimation for ramp metering

Gerdien Klunder

Jams @ sags• 50% congestion in Japan originate at sags

• Empirical analysis on behalf of Toyota showed that changes in car-following behaviour is the main cause

• Instability of flow causes start-stop waves to be emitted from sag area

• Substantial reduction of capacity results

• Resolving them yield substantial improvement in throughput!

Bernat Goni-Ros

Jams @ sags resolution• Use of speed limits to meter the inflow

into the sag (via in-car or road-side)

• Density in sag remains stable so wide moving jams do not occur

• Increase on throughput of 7%, reduction of total delays of 30%!

• Approach assumes road-side VSL or 100% penetration of in-car VSL

• What if only few vehicles are equipped?

Bernat Goni-Ros

Jams @ sags resolution• Model Predictive Control approach:

- Predict the Total Delay of the system as a function of the trajectory of the controlled vehicle (e.g. vehicle 75)

- Try to find the trajectory that minimises this total delay

• Non-trivial primary (DADA) and supporting strategies

• Substantial improvements can be achieved by controlling a few vehicles

Bernat Goni-Ros

Jams @ sags resolution• Model Predictive Control approach:

- Predict the Total Delay of the system as a function of the trajectory of the controlled vehicle (e.g. vehicle 75)

- Try to find the trajectory that minimises this total delay

• Non-trivial primary (DADA) and supporting strategies

• Substantial improvements can be achieved by controlling a few vehicles

Bernat Goni-Ros

Jams @ sags resolution• Substantial improvements can be achieved by controlling only a few vehicles!

Bernat Goni-Ros

Looking at network level

• Local phenomena have network level impacts

N U M B E R O F V E H I C L E S I N N E T WO R K

EX

IT R

AT

ES

C R I T I C A L A C C U M U L A T I O N

Network level impacts• Empirical analyses has revealed differences between networks

• Discussion on what causes differences (driving behaviour, travel behaviour, network topology, traffic control)

• Work on impacts of busses, crossing pedestrians, cyclists

• We have been investigating spatial density variation…

Network level impacts

P(N,σ)= α ⋅N ⋅ 1− NN

max

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⎝⎜⎞

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⎝⎜⎜

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N U M B E R O F V E H I C L E S I N N E T WO R K

EX

IT R

AT

ES

• Impact of even distribution of traffic over network

• Empirical research shows how even distribution of density over network leads to substantial increase in network performance

• Improvements up to 30% in throughput seem possible!

• Traffic control can help

Control heuristics

• Modified backpressure technique with local coordination only causes “optimal” emergent (self-organised) control patterns

• Example shows difference between traditional control approach (TA) and modified backpressure (BP)

• For lower loads, TA performs better

• For higher loads, BP performance is much higher (40% in case of 2000 ver in network)

TA

BP

• Field-test ready BP outperform traditional approach for higher network loads

Kai Yuan

Phase 2• No predictions but timely reaction

• Only control for bottlenecks than can be resolved

• Careful consideration of buffers to be used

• Currently further improving monitoring (data fusion)

• Assessment shows positive impacts (100-250 veh-h savings per peak)

• Larger than expected?

Implementation of

lessons learnt from

phase 1 Ramon Landman

Assessing impacts• Level of variability in congestion patterns due to

uncertainty in demand and supply very large • Not including variability in ex-ante assessment

leads to substantial underestimation of impacts of interventions (ITS and infrastructure!)

• Case study shows bias between 26% and 200%

• Traffic management more effective than we think!

• Development of efficient model framework to include uncertainty

Simeon Calvert

MPC approach to INM• Determine current state of system

• Make prediction of future states, conditional on candidate control signal

• Determine the predicted performance of that candidate control signal

• Determine control signal that optimises objective

• Redo the procedure once new traffic state estimate comes available

Goof vd Weg

MPC approach to INM• Model Predictive Control approaches for INM:

from specific strategies to a generic solution

• No real-life applications of MPC yet due to complexity of controller (computation time)

• Novel formulation of control problem allows real-time application for reasonable network sizes

Goof vd Weg

Model-based evacuation optimisation• Sheet van Adam en vooral belang van gedrag hierin…

• Maar ook: sheet met resultaten van Mignon / Everscape resultaten

Application to evacuations• Development of models to assess

impact of disaster

• Model-based optimisation of evacuation instructions increases effectiveness dramatically (from 42000 to 81000)

• Importance of understanding and anticipating on compliance of evacuees to instructions

Understanding behaviour• Use of virtual reality to gain

additional insight into behaviour of people (during evacuation)

• Research shows impact of disaster, importance of good instructions, and extent of herding

• Evidence for existence of classes: leaders, followers and blockers (latent class analyses)

Mignon vd Berg

• Experiments to study coping strategies and task execution abilities ‘under stress’

• Participants needed to finish puzzles and then evacuate

• Clear indications of herding in experiments

• Example shows how this lead to chaos!

Herding in controlled experiments

Erica Kinkel

31

Engineeringchallenges foreventsorregularsituations…• Canweforacertaineventpredictifasafetyorthroughputissuewilloccur?

• Canwedevelopmethodstosupportorganisation,planninganddesign?

• Canwedevelopapproachestoreal-timemanagelargepedestrianflowssafelyandefficiently?

• Canweensurethatallofthesearerobustagainsunforeseencircumstances?

Duisburg 2010• Situations where large groups gather

occur more frequent

• Events, overcrowded stations, evacuation of sport stadiums (e.g. in case of terrorist attack)

• How can we support the operational management as well as the organisation of events, design of buildings, etc.?

• Prevent disasters such as Loveparade?

Dorine Duives

Transport Safety

• Duisburg disaster shows how complex interplay of decisions and processes at different levels lead to eventual disaster

• Lack of knowledge about risks, unpredictable system behaviour that stems from hidden properties

• How to prepare for a risk you do not know?

• Citadel principle: move from thinking about “what could happen” to what you for sure “do not want to happen”

Page 30

Figure 16 (Helbing & Mukerji, 2012) Crowd disaster causal map from Loveparade Duisburg 2010.

2.2.7 To what extent are crowd pressures dangerous? The pressures form the determining factor for injuries and fatalities occurring during crowd disasters. These pressures cause injuries and death of people. Crowd pressures are related to the density of the crowd. These forces can be mathematically described by the average pedestrian density times velocity variance of body movements around the average velocity of the crowd (Helbing, et al., 2007).

Often high crowd pressures occur whenever traffic flow has become unstable, which is the case in crowd turbulence, a minor disruption can create a short jam which is very dense and slow-moving

Breakdown of efficient

self-organisation leads to

fast deterioration of situation

Wim Beukenkamp

Needforoperationalsupport(crowdmanagement)

Efficientself-organisation

Faster=slowereffect

Blockadesandturbulence

“Thereareseriouslimitationstotheself-organisingabilities ofpedestrianflowoperations”

Reducedproductionofpedestriannetwork

35

SAIL2015

• Biggest(andfree)publiceventintheNederland,organisedevery5yearssince1975

• OrganisedaroundtheIJhaven,Amsterdam

• Thistimearound600tallshipsweresailingin

• Around2,3millionnationalandinternationalvisitors

Modellingforplanning?

• NOMADmodelbasedondifferentialgametheory(PedestrianEconomicus,principleofleasteffort)

• Modelshowsplausibleself-organisedphenomena,aswellasflowbreakdownincaseofoverloading

• Calibratedusingdatamultipleevents

• Manyapplicationspossible,includingeventplanning,stationdesign,etc.

• However,itisquestionableifmodelspredictivelyvalid?

• Applywithcare…orwithknowledge!

Mario Campanella

37

Largescaleandreal-timeapplications?

• Firstmacroscopicmodelabletoreproduceself-organisedpatterns(laneformation,diagonalstripes)

• Self-organisationbreaksdownsincaseofoverloading

• Continuummodelseemstoinheritpropertiesofthemicroscopicmodelunderlyingit

• Applicationsalsotolargescalesituations(largestations,etc.)

• Formssolidbasisforreal-timepredictionmodule

Dorine Duives

38

Real-timesupportforSAIL

• 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?

CentralStation

WalkingandchoicebehaviourofSAILvisitors onthe22ndofAugust

Veemkade

Sumatrakade

TrackingSAILvisitorsusingGPSdevices

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

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verblijftijd looptijd

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2760

Exampledashboardoutcomes

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

Exampledashboardoutcomes

• Sentiment analysis allows gaining insight into locations where people tweet about crowdedness conditions

• More generally, focus is on use of social (media) data (in conjunction with other data sources) to unravel urban transportation flows

• First phase of active mode mobility lab (part of UML)

Druk

Vol

Gedrang

Bomvol

Boordevol

Afgeladen

Volgepakt

Crowded

Busy

Jam

Jam-packed

Buitenlandse toeristen

Inwoners Amsterdam

43

UML:OpenMulti-modalDataPlatform• Dataplatformtounravelmulti-modaltrafficpatterns

• ExampleapplicationexampleduringtripleeventinArenaarea

• Pilotshowspotentialofsystemformulti-modalinformationandguidanceduringevents

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

Engineering the future city.

Datacollection&modellingfortime-tabledesign

• Use of (open) data and modelling for determining transfer resistance, time-table synchronisation (minimisation of transfer waiting times), improving enriched real-time information to passengers, platform reallocation, etc.

Daniel Sparing Jeroen vd Heuvel

Voorbeeld inzicht in loopstromen richting perrons vanaf HC (instappers) - Situatie voor verbouwing…

• Real-time data collection in stations (including platforms) using SmartStation concept

• Current research looks also at transfer, access, egress

TRANSFORMS

• Better understand transfer dynamics in multi-level public transport networks

• Develop passenger-oriented tools for measuring and modelling passenger interchange activities

• Develop innovative methods for operating robust services

• Develop, apply and evaluate transfer strategies in case studies

45

Demand-responsivepublictransport

• SCRIPTS programme together with Radboud and TUE

• Two TUD PhD projects focus on Supply side (route choice, network design)

• Various pilots planned to test different concepts

• Sponsored within NWO SURF call

46

01.Demand 02.Supply

• Systemsoperations• Networkdesign

• Dynamics•

04.Livinglabs 03..Governance

• Feedback• Newpilots• Communication•Monitoring /evaluation

• Businessmodels• Stakeholder strategies• Robustdecisionmaking

• Implementationstrategies

SCRIPTS

• LatentdemandforPTservices• Responsetosolutions• Dynamics

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.• Team is complete (9 PhD and 4 PD + support staff)

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 and design guidelines

Organisation of large-scale

events

Data Insights

Tools

Models Impacts

Network Knowledge Acquisition (learning)

Factors determining route choice

Mode choice, access and

egress

49Engineering the future city.

AmsterdamInstituteofAdvancedMetropolitanSolutons• Totacklethese(andother)bigcityissues,AmsterdamsponsoredfoundationofAMS

• CollaborationbetweenMIT,TUD,WURandindustrypartnerswithmunicipalityofAmsterdamasmain‘client’

• Annualbudget30millionEUR

• Learningbydoing:thecityasalivinglab!

• UrbanMobility(andLogistics)asoneofthekeyissues

• DevelopingavisiononSmartSustainableUrbanMobility

Changeinresearchfocus…

TowardsSmartUrbanPersonalMobility

50Anticipatory

decentralisedcontrolDemandresponsivetransportservices

Urbanactivemodemobility

Cooperativesystemsanddriverautomation

UrbanTrafficandTransportdata

51

Q&A

Whatdowewantourcitiestobelike?

Whythechangeoffocus?• Urbanisationisaglobaltrend!

• Accessibilityisamajorissueinmanycar-centriccitiesandappearspersistent

• Mostdelaysareexperiencedonarterials(notonfreeways!),speedsmaydropofftobelow20km/hduringpeak…

• Urbanspaceisveryscarce,sobuildingnewinfrastructureisgenerallynotstraightforward

• 2/3rdoftrafficaccidentsoccurswithincityboundaries

• Highimpact(traffic-related)emissionsandnoise(peoplelivenearroads…)

• Potentialchangeisthere:insomecities,lowoperationalspeedofcarsincombinationofpull/pushmeasureshasleadtomodalshifts…

ChangingmodalsharesinTheNetherlands

• Cycling and walking have become main modes of transport in Amsterdam (and many other Dutch cities)

• For Amsterdam: big impacts on emissions (4-12% reduction), as well as on noise, accessibility and health

• Popularity of rail has increased as well (because of?): for many rail trips, cycling is used for access and egress

• But these positive trends also has some negative (but interesting) impacts…

Unexpectedsideeffectsofactivemodemobility…

Bikecongestioncausingdelaysandhindrance

Overcrowdingduringeventsandregularsituationsalsoduetotourists

Overcrowdedpublictransporthubs

Not-so-seamlesspublictransport

Bikeparkingproblems&orphanbikes

Bikecongestioncausingdelaysanddangerousbehaviouratintersections

TowardsavisiononSmartUrbanMobility

• Ingredients of a vision…• What are expected main trends (next to spatial trends)?

- trends affecting mobility demand (demographic changes, socio-economic developments)

- trends affecting supply transport modes and services (e.g. technological trends, innovations)

- trends that affect aims and requirements• Analysis and confrontation trends: are current issues

resolved? Do we see new issues? Are they in line with current en future policy objectives? Should we accelerate / decelerate certain developments?

• What is a feasible and desirable situation and how do we get there? What are the roles of various actors?

• A first step (EU strategic agendas + brainstorm)…55

Trendsaffectingurbanmobility

56

Urban

Transport

Supply

Demand for

Urban

Mobility

Services

Big data revolution

Hypermobility

Electrification

Vehicle automation

New organisational concepts

Sharing economy

Hyperconnectivity

e-Society

Societal diversity

Ageing society

Migration

Objectives

and

Requirements

Climate change and natural disasters

Disruptions in energy supply

Disruptions in ICT systems

Security & resilience

Sustainability

Liveability

Health as a major driver

Terrorism and man-made disasters

Accessibility

Safety

health

safety

energy

congestion

emissions

economy

socialequity

Contoursofavision:Integrated&hyper-connectedurbanmobility

• Using key technological trends (big data, hyper-connectivity), social trends (e.g. attitude towards (car-) ownership), and changing objectives / requirements regarding urban mobility…

• Uni-modal urban transport system not likely to achieve identified objectives / requirements (in particular: health, sustainability, liveability)

• We believe we should foster transition to a less car-centric urban mobility system, with pillars:1. Seamless integration of mobility services,

“prioritising” sustainable and healthy modes2. Flexible / efficient use infrastructure & space3. Requiring open urban multi-modal data platform

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Whatdoesseamlessintegrationofservicesentail?

• Transfer / access / egress resistance is high (1 transfer ~ 17 min travel time)

• Reduction essential to make a multi-modal trip compete with car: - Seamless transfer between appropriate

modes, also in terms of infrastructure - Seamless payment schemes- Accurate personalised multi-modal real-

time info and advice giving fair information about all alternatives

• Important role of (shared) active modes for shorter (legs of multi-modal) trips

• Role autonomous vehicles as a mobility service (driverless Uber)?

Idea not a new per se, but becomes feasible with availability of new and improved BIG data sources and methods to fuse them…