LinkedIn emplea cookies para mejorar la funcionalidad y el rendimiento de nuestro sitio web, así como para ofrecer publicidad relevante. Si continúas navegando por ese sitio web, aceptas el uso de cookies. Consulta nuestras Condiciones de uso y nuestra Política de privacidad para más información.

LinkedIn emplea cookies para mejorar la funcionalidad y el rendimiento de nuestro sitio web, así como para ofrecer publicidad relevante. Si continúas navegando por ese sitio web, aceptas el uso de cookies. Consulta nuestra Política de privacidad y nuestras Condiciones de uso para más información.

Se ha denunciado esta presentación.

¿Recomiendas esta presentación? ¿Por qué no compartes?

- Development of a Pedestrian Demand ... by TREC at PSU 699 views
- Modeling Pedestrian and Crowd Behav... by Giuseppe Vizzari 892 views
- Adaptive pedestrian behaviour for t... by Giuseppe Vizzari 1011 views
- Simulation of complex systems: the ... by Giuseppe Vizzari 1247 views
- Pedestrian modelling by Serge Hoogendoorn 1103 views
- Differential game theory for Traffi... by Serge Hoogendoorn 566 views

888 visualizaciones

Publicado el

Publicado en:
Ciencias

Sin descargas

Visualizaciones totales

888

En SlideShare

0

De insertados

0

Número de insertados

28

Compartido

0

Descargas

288

Comentarios

0

Recomendaciones

1

No insertados

No hay notas en la diapositiva.

- 1. Unravelling Urban Traffic Flows From new insights to advanced solutions… a work in progress Prof. dr. Serge Hoogendoorn 1
- 2. 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 economy Water, energy, food, waste Smart infrastructures Urban big data Internet of Everything Digital fabrication Smart mobility Resilient, clean and healthy urban environment Blue-green infrastructures Social & responsible design Proposition: using the cityas a living lab to exploreimpact and ﬁnd possibilitiesof these (and other) trendson mobility and other sectors…
- 3. 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.
- 4. Entering the urban age • 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
- 5. Challenges… • Accessibility is a major issue in many cities (Amsterdam, Melbourne) • Most delays are experienced in cities (not on freeways!), yet freeways have received much attention in the past… • At the same time, (re-) urbanisation opens up many new alleyways for sustainable mobility (active modes, seamless multi-modal transport, shared mobility, autonomous driving) • So what do we see as key themes?
- 6. Challenges… • Accessibility is a major issue in many cities (Amsterdam, Melbourne) • Most delays are experienced in cities (not on freeways!), yet freeways have received much attention in the past… • At the same time, (re-) urbanisation opens up many new alleyways for sustainable mobility (active modes, seamless multi-modal transport, shared mobility, autonomous driving) • So what do we see as key themes?
- 7. Relevant research domains for mobility theme 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 logistics 7
- 8. Trends in mode share in Amsterdam area • 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…
- 9. Side-effects of increasing active mode shares… Bike congestion causing delays and hindrance Overcrowding during events and regular situations also due to tourists Overcrowded public transport hubs Not-so-seamless public transport Bike parking problems & orphan bikes Bike congestion causing delays and dangerous behaviour at intersections
- 10. The ALLEGRO programme 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!
- 11. 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 anddesign guidelines Organisation of large-scale events Data Insights Tools Models Impacts Network Knowledge Acquisition (learning) Factors determining route choice
- 12. 12 Engineering the future city. Today’s talk • Focus on active modes in particular on pedestrian and crowds • Use SAIL event as the driving example to illustrate various concepts in monitoring and management • SAIL project entailed development of a crowd management decision support system
- 13. 13 SAIL? • Biggest (and free) public event in the Nederland, organised every 5 years since 1975 • Organised around the IJhaven, Amsterdam • This time around 600 tallships were sailing in • Around 2,3 million national and international visitors
- 14. 14 Engineering challenges for events or regular situations… • Can we for a certain 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 real-time manage large pedestrian flows safely and efficiently? • Can we ensure that all of these are robust agains unforeseen circumstances? Deep knowledge of crowd dynamics is essential to answer these questions!
- 15. Pedestrian flow operations… 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 N people in area Door capacity: C N C
- 16. Pedestrian flow operations… 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:
- 17. Pedestrian flow operations… 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 cause 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 Looprichting X-positie (in m) Y - p o s i t i e ( i n m ) Walking direction X-position (in m) Y-position(inm) • 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. 18 • Insight in more complex situations • Real-life situations in (public) spaces often more complex • Limited empirical knowledge on multi-directional flows motivated first walker experiments in 2002 • Worldpremiere, many have followed! • Resulted in a unique microscopic dataset First insights into importance of self-organisation in pedestrian flows
- 19. Fascinating self-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
- 20. Fascinating self-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-directional experiment
- 21. Studying self-organisation during rock concert Lowlands… Pedestrian flow operations… So with this wonderful self-organisation, why do we need to worry about crowds at all?
- 22. 22 Increase in friction resulting in arc formation by increasing pressure from behind (force- Pedestrian capacity drop and faster-is-slower effect • Capacity drop also occurs in pedestrian flow • Faster = slower effect • Pedestrian experiments (TU Dresden, TU Delft) have revealed that outflow reduces substantially when evacuees try to exit room as quickly as possible (rushing) • Capacity reduction is caused by friction and arc-formation in front of door due to increased pressure • Capacity reduction causes severe increases in evacuation times Intermezzo: given ourunderstanding of thecauses of the faster isslower effect, can youthink of a solution?
- 23. How old Dutch traditions may actually be of some use…
- 24. 24 Break-down of efficient self- organisation • When conditions become too crowded (density larger than critical density), efficient self-organisation ‘breaks down’ causing • 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
- 25. A New Phase in Pedestrian Flow Operations • 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
- 26. Why crowd management is necessary! Eﬃcient self- organisation Faster = slower eﬀect Blockades and turbulence “There are serious limitations to the self-organising abilities of pedestrian ﬂow operations” Reduced production of pedestrian network
- 27. 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 of P-NFD 27
- 28. 28 Crowd Management for Events • Unique pilot with crowd management system for large scale, outdoor event • Functional architecture of SAIL 2015 crowd management systems • 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?
- 29. Tracking SAIL visitors using GPS devices Central Station Walking and choice behaviour of SAIL visitors on the 22nd of August Veemkade Sumatrakade
- 30. 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
- 31. 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
- 32. 32 Urban Mobility Lab Amsterdam • AMS project • Multi-modal data platform to unravel multi-model traffic patterns • Example application example during triple event in Arena area • Shows potential for use of UML in crowd management (demand prediction) and in more comprehensive multi-modal transportation and traffic management system Freeway and urban arterial data Data from parking garages in and around event area Chipcard public transport data Pedestrian counts from video Loops FCD GSM Surveys Emissions and energy Chip card data TwitterRoad works maintenance PT schedules updates Events, incidents, accidentsDemographic data REAL-TIME INFORMATION OFF-LINE MOBILITY INFORMATION MOBILITY SERVICES SHORT-CYCLIC ASSESSMENT LONG-TERM PATTERNS UML DATABASE Status infrastructure weather News, information Vecom data Existing (open) data platforms DATA FUSION, PROCESSING & DIAGNOSTICS TOOLBOX For SAIL, microscopicsimulation was used forplanning the event…How do these models work?
- 33. 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? 33 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 ◆ Level of anisotropy reﬂected by this parameter ~vi ~v0 i ~ai ~nij ~xi ~xj
- 34. • 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…
- 35. Modelling for planning and 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 intensive 35 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||
- 36. Modelling for planning and 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… 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 approximated them. To this end, we have chosen ~v( ) = V · (cos , sin ), for = 0...2⇡. Fig. 1 shows 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
- 37. 37 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
- 38. 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! Principles of crowd management • Developing crowd management interventions using insights in pedestrian flow characteristics • Golden rules (solution directions) provide directions in which to think when considering crowd management options Application example during Al Mataf design
- 39. Using insights for design and management Separate ingoing and outgoing ﬂows Gates limit inﬂow to mosque and Mutaaf Pilgrims are guided to ﬁrst and second ﬂow Pinch points in current design are removed
- 40. Back to SAIL… …Integrated Transport Management concepts for the Amsterdam Area
- 41. 42 Practical Pilot Amsterdam • Unique practical pilot INM • Fully automated coordinated deployment of traffic management measures to improve throughput on A10 West • First phase successful, second phase currently running • Towards traffic management 2.0: integrating road-side and in-car traffic measures for state estimation (data fusion) and actuation (anticipatory traffic management) • Working on Melbourne pilot (Hai Le Vu, Swinburne) http://www.ipam.ucla.edu/programs/workshops/workshop-iv-decision-support-for-trafﬁc/?tab=schedule
- 42. Future of Traffic Management • 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
- 43. Future of Traffic Management • 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 Practical pilot results (VSL) In-car Specialist (5% penetration) Wide-moving jam reduces road capacity with 30%! Without Specialist wide moving jam travels with a ﬁxed speed in the opposite direction of traﬃc Specialist limits the inﬂow into the jam which therefore resolves
- 44. Closing remarks • 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
- 45. More information? • 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

No se han encontrado tableros de recortes públicos para esta diapositiva.

Parece que ya has recortado esta diapositiva en .

Crear un tablero de recortes

Sé el primero en comentar