Can we use methods from cooperative traffic and crowd modelling and management to manage drone traffic flows? I think we can! In this ppt, I explain how we can instill distributed traffic management in 3D...

2. Taking surface traffic management
to the next dimension…
ITS for
Drone Traffic

3. ▪
Serge Hoogendoorn
Traffic & Crowd Management
Victor Knoop
Traffic Flow Theory
Sascha Hoogendoorn-Lanser
Mobility Innovation Centre Delft

4. Contributions of talk
We aim to inspire by:
▪ Present our three level approach to distributed
connected traffic management
▪ Explore with you – the drone experts – the
opportunities this provides for drone management
▪ Present a research agenda and future directions
In our vision, we look at a future where the
airspace is becoming scarce due to the number of
drones that want to use it…
We look at algorithms, not so much at technology…
We build on over 25 years of (research and practical)
experience in traffic modelling, simulation, and
management in various domains…
09-10-2020

5. Application examples by our group
▪ Use of simple control strategy
Pedestrian flow modelling and crowd management
Modelling cyclist and pedestrians in shared space Control schemes for connected and autonomous vessels
Control schemes for CAVs
Generic machinery:
Differential game theory
and dedicated numerical
solution algorithms and
distributed control

6. ▪
Our distributed control approaches are
inspired on phenomena that we see in
vehicular, pedestrian and vessel traffic…

7. Self-organisation in traffic
▪ Consider situation where two pedestrian groups
walk in the opposite direction of each other
▪ So-called bi-directional lanes will form
automatically without any divine intervention or
central coordination, but also with limited
communication between the pedestrians
▪ This process is super efficient:
▪ It occurs almost instantaneously
▪ Walking speeds remain high
▪ Very limited loss of walkway capacity (max.
pedestrian / hour that can be served)
▪ This efficient self-organisation is not limited to
bi-directional or crossing pedestrian flows…

9. Wow, modelling these interactions must be super complex!
▪ Well… No, they are not!
▪ Assuming that pedestrians…
▪ Want to get from A to B
▪ Do not want to get too close to each other
and try stick to constant speed & direction
▪ Predict behaviour of others (e.g., they stay
on course, do no instantaneously stop;
they cooperate or not)
and using differential game theory
results in mathematical models
describing the behaviour of individual
pedestrians in relation to others
▪ Resulting simulation models reproduce
efficient self-organisation accurately!
09-10-2020

10. ▪
Our proposition:
Our game theoretical approach can inspire
control approaches for efficient
interactions between drones
capatilising on
self-organised phenomena

11. Drone swarm control simulation example
▪ Simple control strategy based on
constant speed and direction strategy
inspired by our NOMAD pedestrian model
▪ No central control: drones behave based
on position information of other drones
(via sensing, communication)
▪ Example: two clouds of drones crossing
▪ Model shows safe and efficient operations:
▪ Minimum distance between drones stays
above set distance value 𝑑!
▪ Efficient passing of drones (lowest mean
speed 83% of undisturbed speeds)
▪ Test scenarios show self-organisation (like
pedestrian flows); requires more research
following of this section, we will derive a relation for the equilibrium velocit
the density gradient r⇢, that will be given by expressions (15) and (16) respe
3.2. Model derivation
We will start with the anisotropic social force model of Helbing [1] that d
influenced by the pedestrians j near pedestrian i:
E
ai =
E
v0
i E
vi
⌧i
Ai
X
j
e
Rij
Bi · E
nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j; E
nij is the unit vect
the angle between the direction of i and the position of j; E
vi denotes the velo
The vector E
v0
i denotes the desired velocity of pedestrian i. It describes both
walking direction. We will assume that the desired velocity is a function of t
a priori. That is, we assume that there is no direct influence of the prevailing
it is, for example, only based on the shortest route to the destination. For app
refer to Refs. [21,30].
The parameter ⌧i describes the relaxation time, reflecting the time needed t
the interaction strength; Bi denotes a scaling parameter, describing the rate
function of distance; 0  i  1 denotes the anisotropy parameter, reflecti
front of i compared to those at the back. Note that i = 1 implies isotropy, i
from the back as to stimuli from the front.
As mentioned, we will assume that the system is in equilibrium, that is, w
velocity satisfies:
E
vi = E
v0
i ⌧iAi
X
j
e
Rij
Bi · E
nij ·
✓
i + (1 i)
1 + cos ij
2
◆
.

12. Drone interaction strategies in relation to
Sensing and Communication quality
Cooperative schemes
Drones jointly optimise
interactions and objectives
Non-cooperative schemes
Each drone optimises
objectives without
cooperation
Risk-averse schemes
Drone assumes other drones
try to come as close as
possilbe
Nash game Princess monster game
Coalition games
Increased levels of cooperation lead to increased efficiency
at the expense of higher cost of data collection and communication between drones
Decreasing sensing and communication requirements

13. ▪
If fully decentralised self-organisation
is so efficient….
(why) do we need to intervene?

14. Limits to self-organisation: some well know examples
Freezing by heating
Heterogeneity messes up
efficiency
Undesireable capacity
distribution
Priority for big flows
Breakdown & capacity drop
Self-organisation fails in
oversaturated conditions

15. Limits to self-organisation make
sensible interventions necessary
09-10-2020

16. Example interventions in connected traffic management
Freezing by heating
Heterogeneity messes up
efficiency
Undesireable capacity
distribution
Priority for major flow
Breakdown & capacity drop
Self-organisation fails in
oversaturated conditions
Homogenisation, e.g. by
speed limits
Redistribution of capacity,
e.g. using traffic lights
Area inflow regulation, e.g.
via gating, re-routing, or
demand management

17. Connected traffic control
▪ Example shows how in a setting with connected
vehicles, an intersection controller deals with
the interactions at a potential pinch point
including priorisation
▪ Approach deals with joint optimisation of vehicle
trajectories (for comfort, fuel consumption, and
/ or emission optimisation) and capacity
distribution (for fair and optimal throughput)
▪ Approach substantially reduces delay (and
pollution) compared to simple controller (-20%)
▪ Concept is generic and can be used for any
type of traffic flow, including drones
Liu M., Zhao J., Hoogendoorn S., Wang M. A single-layer approach for joint
optimization of traffic signals and cooperative vehicle trajectories at isolated
intersections (2022) Transp. Research Part C: Emerging Technologies, 134

18. Three level approach to (drone) traffic management
Safe and efficient drone
interaction strategies via
game theoretical concepts
Methods for control and
prioritise interactions of
flows at bottlenecks
Network control methods
for fully utilising network
capacity becomes necessary
Increasing traffic demand

19. Need for network management
▪ Local control cannot ensure good use of network capacity
automatically
▪ From surface traffic we know that (crowds, car networks) that
local congestion can lead to severe underperformance of
the network
▪ Integrated Network Management (INM) aims to better
distribute traffic over network (e.g., by signal coordination,
smart routing) and ensure that network is not
oversaturated (gating)
▪ Various approaches (decentralised, distributed, or
centralised) succesfully developed and deployed in a number
of scientific and applied projects (e.g., Praktijkproef
Amsterdam, POC DVM Utrecht, AFM Rotterdam)
To keep traffic flows at ‘t Goylaan smooth
given priorities for bike and bus, a network
approach was piloted in Utrecht
Catastrophy at the Loveparade (2011) started
with a local bottleneck which was not
managed correctly

20. Flow and self-organisation
characteristics, requirements for
interventions
Identify technical possibilities &
requirements (lsensing,
communication), regulations,
algorithm development, simulation
Technical possibilities & requirements
(coop. sensing, communication),
algorithm development (multi obj.
optimisation), simulation
Identification of technical requirements
(communication quality, cloud
solutions), security, development of
coordinated control schemes
Network-wide 3D
traffic flow characteristics, need for
network control
Approach seems to have strong linkages to 5G/6G architecture!
Micro interaction control Local cooperative control Network control
On-device Edge Cloud

21. Summary
Based on our experience in dynamic traffic,
crowd and maritime management, we:
▪ Presented three level approach to
connected drone traffic management
(drone level, bottleneck level, network level)
▪ Discussed approaches and results at all
levels
▪ Presented a research agenda and future
directions
▪ Provided first results of taking our work ’to
the next dimension’
Major opportunities expected by cross-
fertilisation between our fields!
09-10-2020
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