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    Break-down of efficient self- organisation When conditions become too crowded

    (density larger than critical density), efficient self-organisation breaks down

    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

  • 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 P-NFD, hysteresis, and impact of spatial variation (forthcoming ISTTT paper) Qnetwork(,) = Qlocal()

    v0

    jam

    2

  • ITS For Crowds

    Intelligent Crowd Management Prof. dr. Serge Hoogendoorn

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    Engineering challenges for events or regular situations Can we for a certain design or 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 support safe and efficient real-time management of (large) pedestrian flows?

    Presentation will go into recent developments in the field op real-time crowd management support with key elements: real-time monitoring & prediction

  • WiFi/BT data on Utrecht Central Station

    Managing Station Pedestrian Flows Dutch railway (ProRail and NS) with

    support of TU Delft have been working on SmartStation concept

    Multi-level data collection system Detailed density collection at pinch

    points (e.g. platforms) WiFi / BlueTooth at station level Combination with Chipcard data

    provides comprehensive monitoring information for ex-post assessment and real-time interventions

    Trajectory data from one of the platforms

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    Monitoring and predicting active traffic in cities (for regular and event conditions) Unique pilots with crowd management system

    for large scale, outdoor event Functional architecture of SAIL 2015 crowd

    management systems, also used for Europride, Mysteryland, Kingsday

    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?

  • Example of tracking data collected during SAIL 2015

    Additional data fro counting cameras, Wifi trackers, etc., provide comprehensive real-time picture of situation during event

    Plans to use this as a basis for the Amsterdam Smart Tourist dashboard

  • 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

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  • 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

  • Example dashboard outcomes

    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-

    Buitenlandse toeristen

    Inwoners Amsterdam

  • Social media data based count reproduction

    Is it possible to reconstruct counts from social-media data?

    Compare different methods to see which represents measurements of density using WiFi/BT

    Time-space averaging provides poor results

    Speed and flow based methods look very promising!

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  • Mysteryland pilot

    Data collection via dedicated Mysteryland app (light and heavy version)

    Use of geofencing to ask participants about experiences (rating) and intentions (which stage to visit next) allowing us to test crowd sourcing

    Combination with social-media data allows looking for cross-correlations in data sets as well as data enrichment

    But app allows us also to provide information to visitors on routes, and guide them to less crowded areas 13

  • Case: mysteryland music festival

    Mysteryland pilot

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  • 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?

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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

    Rij

    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

    Rij

    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) AZZ

    ~y2(~x)

    exp

    ||~y ~x||

    B

    + (1 )1 + cosxy(~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) cosxy

    (~v) =~v

    ||~v|| ~y ~x

    ||~y ~x||1

    Level of anisotropy reflected by this parameter

    ~vi

    ~v0i

    ~ai

    ~nij~xi

    ~xj

  • 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 are too computationally complex for real-time application and lack nice analytical properties

  • Modelling for 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 demanding

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    FROM MICROSCOPIC TO MACROSCOPIC INTERACTIONMODELING

    S