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In this short presentation, we will provide some recent developments in the field of crowd monitoring, modelling and management. We will illustrate these by showing various projects that we are involved in, including the SmartStation project, and the different events organised in and around the city of Amsterdam (including the Europride, SAIL, etc.). In the talk, we will discuss the different components of the system and the methods and technology involved in these. We focus on advanced data collection techniques, the use of social media data, data fusion and the advanced macroscopic modelling required for this. Also, we will show examples of interventions that have been tested, showing how these systems are used in practise.

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- 2. 2 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
- 3. 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
- 4. ITS For Crowds Intelligent Crowd Management Prof. dr. Serge Hoogendoorn 4
- 5. 5 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
- 6. 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
- 7. 7 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?
- 8. 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
- 9. Example dashboard outcomes • Newly developed algorithm to distinguish between occupancy time and walking time • Other examples show volumes and OD flows • Results used for real-time intervention, but also for planning of SAIL 2020 (simulation studies) 0 5 10 15 20 25 30 11 12 13 14 15 16 17 18 19 verblijftijd looptijd 1988 1881 4760 4958 2202 1435 6172 59994765 4761 4508 3806 3315 2509 1752 3774 4061 2629 1359 2654 2139 1211 1439 2209 1638 2581 31102465 3067 2760
- 10. 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
- 11. 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
- 12. 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! 12
- 13. 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
- 14. e: mysteryland music festival Mysteryland pilot 14
- 15. 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? 15 1. Introduction This memo aims at connecting the microscopic modelling principles underlying the social-forces model to identify a macroscopic ﬂow model capturing interactions amongst pedestrians. To this end, we use the anisotropic version of the social-forces model pre- sented by Helbing to derive equilibrium relations for the speed and the direction, given the desired walking speed and direction, and the speed and direction changes due to interactions. 2. Microscopic foundations We start with the anisotropic model of Helbing that describes the acceleration of pedestrian i as inﬂuence by opponents j: (1) ~ai = ~v0 i ~vi ⌧i Ai X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing from pedestrian i to j; ij denotes the angle between the direction of i and the postion of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will be introduced later. In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction for which this occurs is given by: (2) ~vi = ~v0 i ⌧iAi X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x) Level of anisotropy reﬂected by this parameter ~vi ~v0 i ~ai ~nij ~xi ~xj
- 16. • Simple model shows plausible self- organised phenomena • Model also shows flow breakdown in case of overloading • Similar model has been successfully used for planning of SAIL, but it is questionable if for real-time purposes such a model would be useful, e.g. due to complexity • Coarser models proposed so far turn out to have limited predictive validity, and are unable to reproduce self-organised patterns • Develop continuum model based on game-theoretical model NOMAD… Microscopic models aretoo computationallycomplex for real-timeapplication and lack niceanalytical properties…
- 17. 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 17 sented by Helbing to derive equilibrium relations for the speed and the direction, given the desired walking speed and direction, and the speed and direction changes due to interactions. 2. Microscopic foundations We start with the anisotropic model of Helbing that describes the acceleration of pedestrian i as inﬂuence by opponents j: (1) ~ai = ~v0 i ~vi ⌧i Ai X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing from pedestrian i to j; ij denotes the angle between the direction of i and the postion of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will be introduced later. In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction for which this occurs is given by: (2) ~vi = ~v0 i ⌧iAi X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x) denote the density, to be interpreted as the probability that a pedestrian is present on location ~x at time instant t. Let us assume that all parameters are the same for all pedestrian in the ﬂow, e.g. ⌧i = ⌧. We then get: (3) ZZ ✓ ||~y ~x|| ◆ ✓ 1 + cos xy(~v) ◆ ~y ~x We start with the anisotropic model of Helbing that describes the acceleration of pedestrian i as inﬂuence by opponents j: (1) ~ai = ~v0 i ~vi ⌧i Ai X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing from pedestrian i to j; ij denotes the angle between the direction of i and the postion of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will be introduced later. In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction for which this occurs is given by: (2) ~vi = ~v0 i ⌧iAi X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x) denote the density, to be interpreted as the probability that a pedestrian is present on location ~x at time instant t. Let us assume that all parameters are the same for all pedestrian in the ﬂow, e.g. ⌧i = ⌧. We then get: (3) ~v = ~v0 (~x) ⌧A ZZ ~y2⌦(~x) exp ✓ ||~y ~x|| B ◆ ✓ + (1 ) 1 + cos xy(~v) 2 ◆ ~y ~x ||~y ~x|| ⇢(t, ~y)d~y Here, ⌦(~x) denotes the area around the considered point ~x for which we determine the interactions. Note that: pedestrian i as inﬂuence by opponents j: (1) ~ai = ~v0 i ~vi ⌧i Ai X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing from pedestrian i to j; ij denotes the angle between the direction of i and the postion of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will be introduced later. In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction for which this occurs is given by: (2) ~vi = ~v0 i ⌧iAi X j exp Rij Bi · ~nij · ✓ i + (1 i) 1 + cos ij 2 ◆ Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x) denote the density, to be interpreted as the probability that a pedestrian is present on location ~x at time instant t. Let us assume that all parameters are the same for all pedestrian in the ﬂow, e.g. ⌧i = ⌧. We then get: (3) ~v = ~v0 (~x) ⌧A ZZ ~y2⌦(~x) exp ✓ ||~y ~x|| B ◆ ✓ + (1 ) 1 + cos xy(~v) 2 ◆ ~y ~x ||~y ~x|| ⇢(t, ~y)d~y Here, ⌦(~x) denotes the area around the considered point ~x for which we determine the interactions. Note that: (4) cos xy(~v) = ~v ||~v|| · ~y ~x ||~y ~x||
- 18. Modelling for real-time predictions • Taylor series approximation: yields a closed-form expression for the equilibrium velocity , which is given by the equilibrium speed and direction: with: • Check behaviour of model by looking at isotropic flow ( ) and homogeneous flow conditions ( ) • Include conservation of pedestrian relation gives a complete model… 18 2 SERGE P. HOOGENDOORN From this expression, we can ﬁnd both the equilibrium speed and the equilibrium direc- tion, which in turn can be used in the macroscopic model. We can think of approximating this expression, by using the following linear approx- imation of the density around ~x: (5) ⇢(t, ~y) = ⇢(t, ~x) + (~y ~x) · r⇢(t, ~x) + O(||~y ~x||2 ) Using this expression into Eq. (3) yields: (6) ~v = ~v0 (~x) ~↵(~v)⇢(t, ~x) (~v)r⇢(t, ~x) with ↵(~v) and (~v) deﬁned respectively by: (7) ~↵(~v) = ⌧A ZZ ~y2⌦(~x) exp ✓ ||~y ~x|| B ◆ ✓ + (1 ) 1 + cos xy(~v) 2 ◆ ~y ~x ||~y ~x|| d~y and (8) (~v) = ⌧A ZZ ~y2⌦(~x) exp ✓ ||~y ~x|| B ◆ ✓ + (1 ) 1 + cos xy(~v) 2 ◆ ||~y ~x||d~y To investigate the behaviour of these integrals, we have numerically approximated FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODELING 3 Furthermore, we see that for ~↵, we ﬁnd: (10) ~↵(~v) = ↵0 · ~v ||~v|| (Can we determine this directly from the integrals?) From Eq. (6), with ~v = ~e · V we can derive: (11) V = ||~v0 0 · r⇢|| ↵0⇢ and (12) ~e = ~v0 0 · r⇢ V + ↵0⇢ = ~v0 0 · r⇢ ||~v0 0 · r⇢|| Note that the direction does not depend on ↵0, which implies that the magnitude of the density itself has no e↵ect on the direction, while the gradient of the density does inﬂuence the direction. 2.1. Homogeneous ﬂow conditions. Note that in case of homogeneous conditions, FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODELING 3 Furthermore, we see that for ~↵, we ﬁnd: (10) ~↵(~v) = ↵0 · ~v ||~v|| (Can we determine this directly from the integrals?) From Eq. (6), with ~v = ~e · V we can derive: (11) V = ||~v0 0 · r⇢|| ↵0⇢ and (12) ~e = ~v0 0 · r⇢ V + ↵0⇢ = ~v0 0 · r⇢ ||~v0 0 · r⇢|| Note that the direction does not depend on ↵0, which implies that the magnitude of the density itself has no e↵ect on the direction, while the gradient of the density does inﬂuence the direction. 2.1. Homogeneous ﬂow conditions. Note that in case of homogeneous conditions, i.e. r⇢ = ~0, Eq. (11) simpliﬁes to (13) V = ||~v0|| ↵0⇢ = V 0 ↵0⇢ α0 = πτ AB2 (1− λ) and β0 = 2πτ AB3 (1+ λ) 4.1. Analysis of model properties Let us ﬁrst take a look at expressions (14) and (15) describing the equilibrium290 speed and direction. Notice ﬁrst that the direction does not depend on ↵0, which implies that the magnitude of the density itself has no e↵ect, and that only the gradient of the density does inﬂuence the direction. We will now discuss some other properties, ﬁrst by considering a homogeneous ﬂow (r⇢ = ~0), and then by considering an isotropic ﬂow ( = 1) and an anisotropic ﬂow ( = 0).295 4.1.1. Homogeneous ﬂow conditions Note that in case of homogeneous conditions, i.e. r⇢ = ~0, Eq. (14) simpliﬁes sions (14) and (15) describing the equilibrium at the direction does not depend on ↵0, which density itself has no e↵ect, and that only the nce the direction. We will now discuss some ng a homogeneous ﬂow (r⇢ = ~0), and then = 1) and an anisotropic ﬂow ( = 0). ns us conditions, i.e. r⇢ = ~0, Eq. (14) simpliﬁes ↵0⇢ = V 0 ↵0⇢ (16) ! v = ! e ⋅V
- 19. 19 Macroscopic model yields plausible results… • First macroscopic model able to reproduce self-organised patterns (lane formation, diagonal stripes) • Self-organisation breaks downs in case of overloading • Continuum model seems to inherit properties of the microscopic model underlying it • Forms solid basis for real-time prediction module in dashboard • First trials in model-based optimisation and use of model for state-estimation are promising
- 20. Predicting flow operations in Laussanne • Work performed by Flurin Haenseler showing how LOS can be predicted by using macroscopic model • Example shows LOS in one of the corridors connecting platforms in the station • Flow model + route choice model used for Origin- Destination matrix estimation using counts and time-table information
- 21. Towards active interventions • Models can be used to improve design • Off-line model-based optimisation of signage / evacuation instructions (see example) • Crowd Monitoring Dashboard Amsterdam has been used by crowd managers for real-time changes in circulation plan (making some of the routes uni-directional) • Other measures (in particular in stations) include load balancing (changing time table, stop position of train, track assignment), but also gating application • Current research focusses on these type of interventions 21 x−axis (m) y−axis(m) Densities at t = 25.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 y−axis(m) 1 1 2 2 3 3 4 4 5 x−axis (m) y−axis(m) Densities at t = 50.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 x−axis (m) y−axis(m) Densities at t = 125.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 No redistribution of queues (initial iteration) x−axis (m) y−axis(m) Densities at t = 25.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 x−axis (m) y−axis(m) Densities at t = 125.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 x−axis (m) y−axis(m) Densities at t = 50.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 x−axis (m) y−axis(m) Densities at t = 125.1 s 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 No redistribution of queues (initial iteration) Distribution of queues considering reduced speeds
- 22. Recall the P-MFD? Ensuring that the number of pedestrians in a railway stations states below critical level by means of gating…
- 23. The ALLEGRO programme unrAvelLing sLow modE travelinG and tRaffic: with innOvative data to a new transportation and traffic theory for pedestrians and bicycles” • 4.1 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 “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, active mode operations dashboard, etc. • Team is complete (9 PhD and 4 PD + 8 supervisors / support staff )
- 24. 25 Questions?