Active transport workshop hoogendoorn
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Presentation about active mode transport given at the AITPM workshop on active mode mobility. Provides overview of our pedestrian research and the first results of the ALLEGRO project.
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Active transport workshop hoogendoorn
- 1. Unraveling active mode traffic and transportation Towards a Theory of Pedestrian and Bikes Traffic and Travel Prof. dr. Serge Hoogendoorn 1
- 2. Overview of talk… • 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
- 7. Mode shares for bike and feet… • 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
- 9. Travel range bike and e-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 km 0,5 tot 1,0 km 1,0 tot 2,5 km 2,5 tot 3,7 km 3,7 tot 5,0 km 5,0 tot 7,5 km 7,5 tot 10 km 10 tot 15 km 15 tot 20 km 20 tot 30 km 30 tot 40 km 40 tot 50 km 50 km of meer Cumulative % of trips Distanceclass
- 10. So what makes an active mode trip ‘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 Travel time Safety Scenery Grade Crowdedness Intersection delay Signage Interact. fast modes Weather protection Weather Directness Helmet required Attractions Attitude
- 11. So why has active mode mobility been so successful • 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 ITS 11
- 12. Examples of infrastructure improvements 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’ Examples of infrastructure improvements • PlusNet Bike: ‘coarse’ network with bike priority to complement fine grained network • Improving bicycle parking facilities • Special infrastructure such as the ‘cycle street’ (fietsstraat; cars as guests) and ‘cycle highway’
- 13. The PT-bike (OV-fiets) by numbers… • 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
- 14. Typical bike incentives (Beter Benutten) • 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 measures 14
- 15. In sum… • 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 possible 15
- 16. Changing the image of the bicycle? Trendy bikes! • First 3D printed bike, developed by TU Delft as part of a Industrial Design student project • Bikes are getting smarter as well: GPS equipped smart bike connecting to smart phone • Which other innovations can we expect? Van Moof Smart Bikes
- 17. Trends in mode share in Amsterdam area • 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…
- 19. Limits to traffic and transportation models • 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…
- 20. Limits to traffic and transportation models • 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…
- 21. Why is our knowledge limited? • 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
- 23. Pedestrian & Crowd Research The ALLEGRO Precursor Prof. dr. Serge Hoogendoorn 23
- 24. 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? 24 T = N C N people in area Door capacity: C N C
- 25. Important insights from data analysis… 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:
- 26. 26 • 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
- 27. 27 Discovery of self-organisation doing walker experiments Is there also self- organisation in bicycle flow?
- 28. 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… 28 Bi-directional experiment
- 29. 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?
- 30. 30 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?
- 32. 32 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
- 34. 34 How to model self- organisation?
- 35. A bit of theory… • 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: 35 SERGE P. HOOGENDOORN 1. Introduction This memo aims at connecting the microscopic modelling principles under social-forces model to identify a macroscopic flow model capturing interactions pedestrians. To this end, we use the anisotropic version of the social-forces m sented by Helbing to derive equilibrium relations for the speed and the direct the desired walking speed and direction, and the speed and direction chang interactions. 2. Microscopic foundations We start with the anisotropic model of Helbing that describes the accele 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 from pedestrian i to j; ij denotes the angle between the direction of i and th
- 36. • Collected data has formed basis for development of microscopic simulation model NOMAD • Model provides adequate estimates of bottleneck capacities • Model shows plausible self- organised phenomena, such as the bi-directional lanes • It allows studying the conditions under which efficient self- organisation occurs… • Model predicts flow breakdown when demand are too high • It shows how self-organisation is limited by heterogeneity in flow
- 37. • Pedestrian flow models are quite commonplace • Although not thoroughly validated, application for planning purposes (e.g. SAIL) occur quite often • In particular route and activity choice remains a challenging process to correctly describe • Can we also develop such models for bicycle flow operations?
- 39. 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! 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
- 40. 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 What about dynamic interventions?
- 41. 41 Engineering the future city. Towards a crowd monitoring and management dashboard: SAIL 2015 • 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 • SAIL project entailed development of a crowd management decision support system
- 42. 42 Crowd Monitoring (and 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?
- 43. Active Mode Urban Mobility Lab Crowd Monitoring Dashboard for events (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 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
- 44. Active Mode Urban Mobility Lab Crowd Monitoring Dashboard for events (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
- 45. Possible data sources? Tapping into social media data • 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 Example Social-Media analysis during SAIL 2015
- 46. New insights in visitor behaviour during events… 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
- 47. 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 Real-timepersonalised guidance
- 48. 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 Real-timepersonalised guidance
- 49. Unraveling active mode traffic and transportation The ALLEGRO programme Prof. dr. Serge Hoogendoorn 49
- 50. The ALLEGRO programme 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
- 51. New data sources allow clearer insights… • 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
- 52. Travellers knowledge of the network? • 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
- 53. The Student Hotel project • Provides longer-term housing to students (e.g. in Amsterdam, The Hague, Rotterdam, Eindhoven, Groningen) • Provides guests with GPS equipped bike • Tracking students will provide route choice data and information on how cycling patterns changes: - Which routes do people actually know and use? - How does (so-called survey) knowledge change over time (including distance and perception distortion) • During stay, multiple interventions are done to change students attitude towards sustainability: will this change their attitude towards cycling?
- 54. Pedestrian and cycle flow operations • 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
- 55. Pedestrian and cycle flow operations • 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
- 56. Example application: testing shared space concepts… 56 -60 -40 -20 0 20 40 60 x (m) -30 -20 -10 0 10 20 30 y(m) 25 30 35 40 45 50 55 60 65 70 75 time (s) 0 0.5 1 efficiency(-) • 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)
- 57. Interaction other modes requiring better models 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
- 58. Factors adding to complexity in active mode mobility • 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 project 58
- 59. 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 Real-timepersonalised guidance
- 61. 61 To advanced predictive control systems… • SAIL 2015 and Europride 2016 (dashboard) • Mystery land 2016 (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?
- 62. Design support tools for Active Mode networks • 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 + infra design Demand model Operations model Network structure Multi-modal links Multi-modal nodes Level-of- Service Design methodology
- 63. Successful shared-space implementation 63 Example shared-space region Amsterdam Central Station Shared space in Melbourne Support and guidelines for specific elements in the design… • Shared space concept applied successfully in Amsterdam • Concept appears to work conditionally: not too high demands, no group has very low share • Heterogeneity limits efficiency (“freezing by heating”) • Communication and cooperation amongst participants appears very important…
- 65. Active Mode Traffic Management? • Joint work of TU Delft and TNO showed potential of combining speed advice (e.g. via app, or lights) and green waves (reduction of #stops of 45%) • Different examples of bike traffic management, such as bike parking information Utrecht and dynamic routing are piloted • Current work focusses on providing real-time info via apps (to be tested during dance event Mysteryland) • Potential for effective approaches increases with increased connectivity
- 66. 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 Real-timepersonalised guidance
- 68. Bike safety by numbers… 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)
- 69. Traffic safety by numbers • Increase accidents 9% in 2015; strong difference male and female… 69