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Unraveling	active	mode	
traffic	and	transportation
Towards a Theory of Pedestrian and Bikes Traffic and Travel

Prof. dr. ...
Overview	of	talk…
• Some stats on Dutch
active mode mobility

• The ALLEGRO
precursor: pedestrian
and crowd modelling
and ...
Any	idea	who	this	is?
But	also	in	The	Netherlands,	we	need	to	be	real!
4
Or	who	this	is?	
Dutch	Prime	Minister	Mark	Rutte	on	his	way	to	
meet	US...
5
It	is	a	matter	of	image…
Iranian	delegation	felt	“ashamed”	when	PM	Rutte	
arrived	at	his	appointment	by	bike…
6
Dutch	cycling:	not	just	for	the	“strong	and	fearless”
Mode	shares	for	bike	and	feet…
• In terms of number of trips,
bike + walking share is high

• Share of cycling / walking i...
Travel	motives	for	cyclists	and	pedestrians
Travel	range	bike	and	e-bike
• Average observed travel ranges for bikes = 3.5 kilometers; for e-bike range = 5.5 km

• Var...
So	what	makes	an	active	mode	trip	‘attractive’
• Well, that is not yet fully clear:
different studies (using different
mod...
So	why	has	active	mode	mobility	been	so	successful	
• Multiple factors have made Dutch cycling (and walking) successful:

...
Examples	of	infrastructure	improvements
12
• Special infrastructure such as
the ‘cycle street’ (fietsstraat;
cars as guest...
The	PT-bike	(OV-fiets)	by	numbers…
• Introduced in 2003

• OV-fiets: 400 EUR a piece
(purchase): CHEAP! 

• Available at 2...
Typical	bike	incentives	(Beter	Benutten)
• Simply saying that cycling is “better” often does not
work (public campaigns): ...
In	sum…
• Potential for active mode mobility in Australian cities appears high (travel
distances, potential role in multi-...
Changing	the	image	
of	the	bicycle?	Trendy	
bikes!		
• First	3D	printed	bike,	
developed	by	TU	Delft	
as	part	of	a	Industr...
Trends	in	mode	share	in	Amsterdam	area
• Combination of (policy) interventions,
planning decisions, and trends have lead t...
Side-effects	of	increasing	active	mode	shares…
Bike	congestion	causing	delays	
and	hindrance
Overcrowding	during	events	an...
Limits	to	traffic	and	transportation	models
• Proposition: active modes are not
represented adequately in our current mode...
Limits	to	traffic	and	transportation	models
• Why can’t we use our regular models? 

- Level of detail in (planning) model...
Why	is	our	knowledge	limited?
• Traffic and Transportation Theory for pedestrians
and even more so for cyclists is still y...
Let’s	start	with	the	pedestrians…
Pedestrian	&	

Crowd	Research	
The ALLEGRO Precursor 

Prof. dr. Serge Hoogendoorn
23
Pedestrian	flow	operations…
Simple case example: how long does it take to
evacuatie a room?
• Consider a room of N people
...
Important	insights	from	data	analysis…
Simple case example: how long does it take to
evacuatie a room?
• Wat determines ca...
26
• Insight	in	more	complex	
situations	
• Real-life	situations	in	(public)	
spaces	often	more	complex	
• Limited	empiric...
27
Discovery	of	self-organisation	doing	walker	experiments
Is there also self-
organisation in 

bicycle flow?
Fascinating	self-organisation
• Relatively small efficiency loss (around
7% capacity reduction), depending on
flow composi...
Studying	self-organisation	during	rock	concert	Lowlands…
Pedestrian	flow	operations…
So with this wonderful
self-organisat...
30
Increase	in	friction	resulting	in	arc	formation	
by	increasing	pressure	from	behind	(force-
Pedestrian	capacity	drop	an...
How	old	Dutch	traditions	may	actually	be	of	some	use…
32
Break-down	of	efficient	self-	
organisation	
• When	conditions	become	too	crowded	
(density	larger	than	critical	densit...
Why	crowd	management	is	necessary!
Efficient	self-
organisation
Faster	=	slower	
effect
Blockades	and	
turbulence
“There	are	...
34
How to model self-
organisation?
A	bit	of	theory…
• We build a mathematical model on hypothesis of the “pedestrian economicus”
assuming that pedestrians ai...
• Collected	data	has	formed	basis	for	
development	of	microscopic	
simulation		model	NOMAD	
• Model	provides	adequate	esti...
• Pedestrian	flow	
models	are	quite	
commonplace	
• Although	not	
thoroughly	validated,	
application	for	
planning	purpose...
39
Prevent blockades by separating flows in
different directions / use of reservoirs
Distribute traffic over available
inf...
Using	insights	for	design	and	management
Separate	ingoing	
and	outgoing	flows Gates	limit	inflow	to	
mosque	and	Mutaaf
Pilgr...
41
Engineering the future city.
Towards	a	crowd	
monitoring	and	
management	
dashboard:	SAIL	2015	
• Biggest	(and	free)	pu...
42
Crowd	Monitoring	(and	
Management)	for	Events	
• Unique	pilot	with	crowd	management	system	
for	large	scale,	outdoor	ev...
Active	Mode	Urban	Mobility	Lab

Crowd	Monitoring	Dashboard	for	events	(SAIL,	EuroPride,	…)	
• GPS data (e.g. using apps)

...
Active	Mode	Urban	Mobility	Lab

Crowd	Monitoring	Dashboard	for	events	(SAIL,	EuroPride,	…)	
• GPS data (e.g. using apps)

...
Possible	data	sources?	

Tapping	into	social	media	data
• Social-media data
provides information
we have not really
tapped...
New	insights	in	visitor	behaviour	during	events…
46
• Data collection at events (e.g.:
SAIL and Mysteryland) provides
new ...
Active Mode 

UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and ...
Active Mode 

UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and ...
Unraveling	active	mode	
traffic	and	transportation
The ALLEGRO programme

Prof. dr. Serge Hoogendoorn
49
The	ALLEGRO	programme
unrAvelLing sLow modE travelinG and tRaffic: 

with innOvative data to a new transportation and traf...
New	data	sources	allow	clearer	insights…
• In 2015, the “Fietstelweek” was held providing GPS
information for over 50.000 ...
Travellers	knowledge	of	the	network?
• Estimation results turn out to be sensitive to
choice set generation 

• Key is in ...
The	Student	Hotel	project
• Provides	longer-term	housing	to	students	(e.g.	in	
Amsterdam,	The	Hague,	Rotterdam,	Eindhoven,...
Pedestrian	and	cycle	flow	operations
• Controlled experiments allow ‘setting the stage’ such
that desired conditions are m...
Pedestrian	and	cycle	flow	operations
• Application of advanced video analysis software allows
collecting detailed field da...
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 4...
Interaction	other	modes	requiring	better	models
57
• Driving automation gaining lots of attention,
but focus appears to be...
Factors	adding	to	complexity	in	active	mode	mobility
• Large number of possible attributes (distance,
separate infra, safe...
Active Mode 

UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and ...
From	simple	design	guidelines…
Separate	ingoing	
and	outgoing	flows Gates	limit	inflow	to	
mosque	and	Mutaaf
Pilgrims	are	gu...
61
To	advanced	predictive	control	
systems…	
• SAIL	2015	and	Europride	2016	(dashboard)	
• Mystery	land	2016	(CrowdSourcin...
Design	support	tools	for	Active	Mode	networks
• Set up tools and
guidelines to support
network and infra
design based on…
...
Successful	shared-space	implementation
63
Example	shared-space	region	

Amsterdam	Central	Station Shared	space	in	Melbourn...
64
Example	shared-space	region	

Amsterdam	Central	Station
Design	promoting	safe	behaviour?
Active	Mode	Traffic	
Management?	
• Joint	work	of	TU	Delft	and	TNO	
showed	potential	of	combining	
speed	advice	(e.g.	via	...
Active Mode 

UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and ...
67
Q&A	
Providing	theory	supporting	active	transport	planning
Bike	safety	by	numbers…
68
• Cycling is relatively safe (in NL: about 200
deaths each year) although increase in safety
ha...
Traffic	safety	by	numbers
• Increase accidents 9% in 2015; strong difference male and female…
69
Active transport workshop hoogendoorn
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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. 1. Unraveling active mode traffic and transportation Towards a Theory of Pedestrian and Bikes Traffic and Travel
 Prof. dr. Serge Hoogendoorn 1
  2. 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
  3. 3. Any idea who this is?
  4. 4. But also in The Netherlands, we need to be real! 4 Or who this is? Dutch Prime Minister Mark Rutte on his way to meet US president Obama (allegedly…)
  5. 5. 5 It is a matter of image… Iranian delegation felt “ashamed” when PM Rutte arrived at his appointment by bike…
  6. 6. 6 Dutch cycling: not just for the “strong and fearless”
  7. 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
  8. 8. Travel motives for cyclists and pedestrians
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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…
  18. 18. 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 risky behaviour at intersections
  19. 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. 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. 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
  22. 22. Let’s start with the pedestrians…
  23. 23. Pedestrian & 
 Crowd Research The ALLEGRO Precursor 
 Prof. dr. Serge Hoogendoorn 23
  24. 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. 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. 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. 27 Discovery of self-organisation doing walker experiments Is there also self- organisation in 
 bicycle flow?
  28. 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. 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. 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?
  31. 31. How old Dutch traditions may actually be of some use…
  32. 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
  33. 33. 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
  34. 34. 34 How to model self- organisation?
  35. 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. 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. 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?
  38. 38. 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
  39. 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 What about dynamic interventions?
  40. 40. 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
  41. 41. 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?
  42. 42. 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
  43. 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
  44. 44. 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
  45. 45. 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
  46. 46. 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
  47. 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. 48. Unraveling active mode traffic and transportation The ALLEGRO programme
 Prof. dr. Serge Hoogendoorn 49
  49. 49. 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
  50. 50. 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
  51. 51. 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
  52. 52. 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?
  53. 53. 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
  54. 54. 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
  55. 55. 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)
  56. 56. 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
  57. 57. 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
  58. 58. 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
  59. 59. From simple design guidelines… 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
  60. 60. 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?
  61. 61. 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
  62. 62. 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…
  63. 63. 64 Example shared-space region 
 Amsterdam Central Station Design promoting safe behaviour?
  64. 64. 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
  65. 65. 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
  66. 66. 67 Q&A Providing theory supporting active transport planning
  67. 67. 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)
  68. 68. Traffic safety by numbers • Increase accidents 9% in 2015; strong difference male and female… 69

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