In many countries, cities are expanding in terms of size, number residents and visitors, etc. The resulting increase in concentration of people, with their mobility needs, causes major traffic and transportation problems in and around our cities. Next to the economic impacts due to delay and unreliability of travel time, concerns regarding safety and security, emissions and sustainability become more and more urgent. ITS (Intelligent Transportation Systems) hold the potential to reduce these issues. In the past decade, we have been more and more successful in making better use of the available infrastructure by using traditional ITS measures. As we will show in this talk, key to this success has been in achieving a profound understanding of what are the key phenomena that characterise network traffic flows, and designing solutions that capitalise on this. The playing field is however rapidly changing. For one, we see a transition from road-side to in-car technology in terms of sensing and actuation. This provides great opportunities, but making best use of these is not trivial and requires a paradigm shift in the way we think about managing traffic flows where collaboration between the old stakeholders (e.g. road authorities) and the new stakeholders (e.g. companies like Google, and TomTom) becomes increasingly important. This will be illustrated in this talk by some examples showing how we can put the transition to in-car traffic management to use, both in terms of making optimal use of the new data sources and the use of the car as an actuator. With respect to the latter, we will see that even for low penetration levels, which will occur in the transition phase towards a more highly automated traffic stream, considerable impacts can be achieved if we adequately consider the non-automated vehicles. Furthermore, it requires vehicles to be able to communicate and cooperate with each other. These two elements are two of the five steps that was identified in the transition towards a fully automated system. The final part of the talk will deal with the other steps that are deemed important to understand which of the scenarios in a urban self-driving future will unfold. These pertain to the interaction between man and machine, the need and willingness to invest in separate infrastructure in city, and whether automated car can co-exist with other (active) travel modes. With respect to the latter, we will also consider what ITS can mean for the other modes of travel.

1. Prof. dr. ir. Serge P. Hoogendoorn
Technische Universiteit Delft, AMS, Arane
Future of Traffic Management and ITS
Putting the ‘I’ in ITS

2. Societal urgency Regional
Traffic Management
• Urbanisation is a global trend leading to
higher concentration of people and their
movements
• Accessibility is a major issue in many car-
centric cities and appears to worsen further
in coming decades
• 2/3rd of traffic accidents occurs within city
boundaries
• High impact (traffic-related) emissions and
noise (people live near roads…)
• Urban space is very scarce, so building new
infrastructure is generally not easy / cheap
• Focus of today’s lecture is on improving
utilisation of existing infrastructure
by smart management of traffic flows
26%
38%
33%

3. Why does it make sense to
manage traffic flows?

4. • Wide Moving Jam (often also called: start-stop
wave - in Dutch: ‘filegolf’)
• Occurs ‘spontaneously’ in unstable flow; its
occurrence is hence very hard to predict
• Once present, a wide moving jam…
- Reduces road capacity substantially
- Has very predictable dynamics (moves at
18 km/h in opposite direct of traffic)
- Increases un-safety, pollution, and fuel consumption
• In some cases, the trigger is more clear…
Traffic stability & Moving jams

5. Jams @ sags
• 50% congestion in Japan originate at sags
• Empirical analysis on behalf of Toyota
showed that changes in car-following
behaviour is the main cause
• Instability of flow causes start-stop waves to
be emitted from sag area
• Substantial reduction of capacity results
• Resolving them yield substantial
improvement in throughput!

6. To make matters worse…
• Recent empirical work shows relation between
speed in jam and jam outflow
• Relation shows that outflow of wide moving jams
in 30% lower than (free) road capacity
• Congestion leads to ‘more congestion’…
- Capacity reduction leads to higher delays
- WFMs trigger new standing queues
- WMFs occur start in standing queues
• But also congestion spill-back strongly
reduces throughput
Start-stop wave reduces
capacity by 30%
Standing queue reduces
capacity by 15-25%

7. • Next to reduced
capacity in case of
standing queues
or wide-moving
jams, severe
reduction of
throughput may
be cased by spill
back
• Example shows
how spill back
from on-ramp
reduces outflow of
network
substantially…
• Spillback occurs at
off-ramps, urban
arterials, etc.
Spillback causes
245 minutes of
avoidable travel
delays!

8. The Hype: NFD’s
Yokohama
San
Francisco
Nairobi
0 50 100 150 200 250
Density
0
1000
2000
3000
4000
5000
6000
7000
8000
Flow
MFD data v2
Amsterdam?
• NFD: Network
Fundamental Diagram
• Describes average throughput
of network als a function of
network load (e.g. average
density)
• Provides insights into
functioning of networks and a
way to cross-compare networks
(see illustration)
• Important characteristics:
tipping point (critical density)
from which point onward the
production reduces or even
becomes zero
• Why the big differences
between networks?

9. Network level impacts
AV E R A G E D E N S I T Y I N N E T WO R K
EXITRATES
• Generalised NFD shows
impact of uneven
distribution of traffic over
network
• Empirical research shows
how a more even
distribution of density over
network leads to substantial
increase in network
performance
• Improvements up to 30% in
throughput seem possible!
• g-NFD can be derived from
underlying FDs (example
shows Greenshields)
• Also holds for other
networks, including
pedestrians! Qnetwork(⇢, ) = Qlocal(⇢)
v0
⇢j
2

10. 10
Prevent blockades, e.g. by increasing queue
outflow, or separating flows in different
directions / use of reservoirs
Distribute traffic over available
infrastructure for instance by means of
guidance or intersection controllers (BP)
Increase throughput in particular at pinch
points in the network…
Limit the inflow (gating) ensuring that
number of vehicles / pedestrians stays below
critical value (NFD)!
Traffic
Management by
First Principles
• Insights into the causes of
operations deterioration
have led to the
development of simple but
useful principles of traffic
control and management
• Principles have formed
basis for designing traffic
control and management
plans, altering network
desing, etc.
Applications are not
restricted to car traffic…

11. General idea: temporarily limit the inflow into the WMJ
using reduced speed limits upstream of jam…
1. Detect the wide moving jam using inductive loops
2. Determine its severity (number of ‘excess’ vehicles to be
‘removed’ by limiting inflow)
3. Determine if there is space to temporarily store vehicles that
are witheld to flow into jam
4. If solvable (available space > severity), implement control
strategy
5. Monitor to check if jam is resolved
Back to the wide-
moving jam…

12. x x x
t
Some traffic engineering background
• How to effectively limit inflow using speeds: difference between local
deployment and instantaneous deployment
• Which of the schemes is (most) effective?
Tplatoon
Tplatoon
Tplatoon
no limit local limit instant. limit
t t

13. Isolated approach using VSL
• SPECIALIST algorithm developed by TU Delft on behalf of RWS
• Fixed speed limit deployed over variable roadway stretch: SPECIALIST
computes length that is required to remove excess vehicles
• After tuning, we had 2.8 activations per day resolving jam in 72% cases…
Succes?
Saves approx.
700k AUD
annually
Improvements?
Why not moreactivations? Why no“only” 72% solved?

14. Improving effectiveness?
1. Increase # activations by increasing control
space: support variable speed limits by using other
control measures, e.g. ramp metering: coordination
2. Increase % of WMJ resolved upon activation by
correctly determine control task (vehicles ‘too many’
in moving jam) and available control space:
improved state estimation
3. Only deploy in case moving jam: improving
diagnostics

15. Increasing control
space
• COSCAL v2 integrates VSL and ramp metering
• Use of ramp as buffer to support VSL control
approach making it much more effective
• Shows need for coordination of measures
• Extension to multiple on-ramps, intersection
controllers, etc.
• Requires insight into storage space on ramps /
intersections and relation with bottleneck
Moving jam Relative space of Slave
buffer = relative space of
Master buffer

16. Car as sensor
• Floating Car Data to detect WMF (for queue
tail warning application, AID)
• Use of Flitsmeister app to collect data
(increased polling in pilot area)
• Comparison shows that in many instances,
FCD is more timely and accurate than loop
data
• Allows improved differentiation between
types of congestion
• Works at moderate penetration levels
(4-10%)
• Courtesy of RWS (Marco Schreuder)Loop-based signal
between loops FCD is more timely

17. Queue estimation
• Estimation of queue lengths (nr. vehicles in
queue) from loops isimportant for traffic
control and management purposes, but hard
problem in traffic engineering
• Determining queue lengths is difficult due to
measurement errors inflow and outflow (drift)
• Use of TomTom FCD data (sparse) providing
segment travel times
• Machine learning using structure based on
problem characteristics
• Machine learning approach outperformed
traditional model-based approaches
• Example shows queue estimates for s106
urban arterial
• Quality sufficient for traffic management
• Also without induction loops, queue estimates
approach very reasonable
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18. Pilot Amsterdam
• First practical pilot with coordination
applied on a network level
• The golden triangle pulled it off: road
authority, industry and academia
• Pilot showed that coordination - if
done correctly - greatly increases
effectiveness of traffic management
• Assessment shows positive impacts
(100-250 veh-h savings per peak)
equal to up to 1 million AUD / year
• In era of Google and Teslas,
when do we stop investing in
road-side technology?

19. A simple quiz
• Case with 2 OD pairs and perfectly informed
traveler from A to B having 2 options
• Intelligent intersection controller optimises
locally capacity use at intersection evenly
(equal delays for both directions)
• Most travellers (85%) choose route 2 under
normal circumstances
• Event (incident) occurs; flow conditions route 2
worsen (from 120 to 80 km/h)
• What happens?
A
C
D
B
Route 1
Route 2
perc. choice route 1
trafficcontroller
total delays
0 0.1 0.2 0.3 0.4
0
0.1
0.2
0.3
0.4
0.5
1
2
3
4
5
6
x 10
5

20. Cooperation road-side & in-car
• Consideration of drivers changing route due to
changes in traffic operations is necessary
• Joint control of intersection and route guidance leads
to System Optimum (SO), but may not be feasible
• In designing traffic control, take into consideration
expected impact the controller actions have on
routing (anticipatory control)
• Requires sending information about drivers route
choice to traffic controller (“V2I”)
• First practical tests (PPA) show technical feasibility,
but not yet network impacts
1
No consideration of
route demand changes
in control
Anticipating
demand
changes

21. Towards car-based traffic management

22. The car as an actuator…
• Can we use the car as an actuator to improve traffic conditions?
• Naive approach: an automated car drivers (potentially?) at a
shorter time headway (either in a platoon or not) and hence the
road capacity increases
• Relatively small increases in capacity (e.g. 2.6% in case of 10%
penetration of vehicles driving witg 25% smaller headway)
• Will this be feasible in mixed traffic?
• Can we do better? Work on behalf of Shell has focussed on the
improvement of traffic flow (for either throughput or emissions)
when penetration levels are still well beyond 50%….

23. MPC for cooperative driving
• Consider state of platoon
• We control acceleration of some veh.
• Predict behaviour non-controlled vehicle
• Idea is to find control functional that minimises some objective function
(delays, fuel, emissions, comfort) describing costs (performance) for entire platoon
• Recompute optimal control function for new time instant
Follower 2 -
Human-driven
vehicle
Follower 1-
Cooperative vehicle
Leader –
Human-driven
vehicle
s1, Δv1s2, Δv2
~x(t) = {ri(t), vi(t)}
i 2 Cui
uj = a 1
⇣ vj
v0
⌘ ✓
s⇤
(vj, vj)
sj
◆2
!
j 2 U
~u[tk,tk+H)
~u⇤
[tk,tk+H) = arg min J(~u[tk, tk + T))
tk+1

24. MPC for cooperative driving
• Impact of C-ACC on
wide-moving jams
• Lower penetration
(e.g. 10%) jams are
resolved effectively
• Possible impact of
cooperative systems
in transition to full
penetration

25. Use of game theory for C-ACC systems
• Differences no control, ACC and C-ACC
• Cost function in C-ACC case describes cost of entire platoon, controlled vehicle
influences behaviour of the vehicles to minimise overal costs
More info: Wang, M., Hoogendoorn, S.P., … (2015). Game theoretic approach for predictive lane-changing and
car-following control (2015) TR-Part C: Emerging Technologies, 58, pp. 73-92.
Any downsides to this approach?
Focus on decentralisation
of control approaches to
deal with complexity!

26. Jams @ sags: resolution
• Similar approach to reduce congestion
caused by sags
• Controlled vehicle does not optimise local
conditions, but tries to stabilise flow so
congestion + cap drop is smaller
• Non-trivial but understandable primary
(DADA) and supporting strategies
result…
• Substantial improvements by controlling
only a few vehicles
More info: Goñi-Ros B. et. al (2016) Optimization of traffic flow at freeway sags by controlling the
acceleration of vehicles equipped with in-car systems, TR-C: Emerging Technologies, 71, 1-18

27. Jams @ sags: resolution
• Substantial improvements can be achieved by controlling only a few vehicles!

28. NIVEAU 0
DELEN IN BLOEI
VAN NIET DELEN
NAAR DELEN
EVOLUTIE VAN DE
PRIVÉAUTO
NIVEAU 1-2
NIVEAU 3-4
NIVEAU 5
Mens en machine Coöperatief rijden Gemengd verkeer Stedelijk dilemma Zelfrijdende stadCooperative driving Mixed traffic Urban dilemma Zelf-driving cityMan-machine
(c) KiM, The Netherlands
Will man and
machine work well
together?
Will cooperative
systems turn out to
be feasible (e.g. due
to privacy, security)
Will we be able to
deal with mixed
traffic?
Will self-driving
vehicles need
separate
infrastructure?
Will self-driving
vehicles interact well
with vulnerable
road users

29. What about the other modes?

30. Issues in dense cities are not limited to cars…
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

31. Example trajectories + local densities on Utrecht platform
Example of possible intervention showing potential
impact of station crowd management
Managing Station
Pedestrian Flows
• Dutch railway (ProRail and NS) with
support of TU Delft have been working
on SmartStation concept
• Multi-level data collection system
• Detailed density collection at pinch
points (e.g. platforms)
• WiFi / BlueTooth at station level
• Combination with Chipcard data
provides comprehensive monitoring
information for ex-post assessment
and real-time interventions

32. Bike Traffic
Management?
• Different examples of bike
traffic management, such as
bike parking information
Utrecht and dynamic routing
are piloted
• Joint work of TU Delft and
TNO showed potential of
combining speed advice (e.g.
via app, or via lights) and
green waves (reduction of
#stops of 45%)
• Potential for effective
approaches increases with
increased connectivity

33. The Dutch alternative to the self-driving car?

34. Change in research focus…
Towards Smart Urban Personal Mobility
37
Regional traffic
management & control
Flexible public
transport services
Urban active mode
mobility
Cooperative systems
and driver automation
Urban Traffic and
Transport data

35. Thank you for your attention

36. Traffic management in transition
• Traffic Management has been successful in better utilising existing
infrastructure, recent major advances in coordination of measures
• New in-car technology has potential to make traffic management more effective
by improving monitoring and actuation, even at low penetration levels, if (and
only if!) we use smart approaches to make most out of the new options!
• Important aspects in this are the speed at which cooperative driving will be
introduced and how we deal with mixed traffic
• But there are other aspects that will determine the speed at which we move
towards fully automated urban transportation systems…

37. Integrated & hyper-connected urban mobility
• Uni-modal urban transport system not likely!
• Using key technological trends (big data,
connectivity), social trends in attitude towards
(car-) ownership, increased flexibility in work and
leisure time, and objectives / requirements
regarding urban mobility (impacts of liveability,
health, and resilience)…
• Innovations should foster transition to a integrated
connected urban mobility system, with pillars:
1. Seamless integration of services - prioritising
sustainable modes - via hyper-connectivity
2. Flexible / efficient use infrastructure & space
3. Common Data Platform
40