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Katsuhiro NishinariKatsuhiro NishinariFaculty of Engineering, University of Faculty of Engineering, University of
TokyoTokyo
JammologyJammology Physics of self-driven particlesPhysics of self-driven particles
Toward solution of all jamsToward solution of all jams
Outline
Introduction of “Jammology”
Self-driven particles, methodology
Simple traffic model for
ants and molecular motors
Conclusions
Jams Everywhere
School, Herd, Flock, etc.
Vehicles, ants, pedestrians, molecular motors…
Non-Newtonian particles,
which do not satisfy three laws of motion.
ex. 1) Action Reaction,
“force” is psychological
2) Sudden change of motion
What are self-driven particles (SDP)?
D. Helbing, Rev. Mod. Phys. vol.73 (2001) p.1067.D. Chowdhury, L. Santen and A. Schadschneider,Phys. Rep. vol.329 (2000) p.199.
Conventional mechanics,
or statistical physics cannot
be directly applicable. Rule-based approach
(e.g., CA model)
Numerical computations
Exactly solvable models
(ASEP,ZRP)
Jammology=Collective dynamics of SDP Text book of “Jammology”
Vehicles car, bus, bicycle, airplane,etc. Humans Swarm, animals, ant, bee, cockroach, fly, bird,fish,etc. Internet packet transportation Jams in human body Blood, Kinesin, ribosome, etc. Infectious disease, forest fire, money, etc.
Subjects of Jammology
Conventional theory of Jam = Queuing theory
In Out
Service
Breakdown of balance of in and out causes Jam.
What is NOT considered in Queuing theory
Exclusion effect of finite volume of SDP
ASEP model can deal the exclusion!
ASEP=A toy model for jamASEP( Asymmetric Simple Exclusion
Process )
t
1t
0 1 0 1 1 0 0 1 0 1 1 1 0 0 0
0 0 1 1 0 1 0 0 1 1 1 0 1 0 0
Rule : move forward if the front is empty
This is an exactly solvable model, i.e., we can calculatedensity distribution, flux, etc in the stationary state.
Who considered ASEP?Macdonald & Gibbs, Biopolymers, vol.6 (1968) p.1. Protein composition process of Ribosomes on mRNA
rp
This research has not been recognized until recently.
Fundamental diagram of ASEP
• Flow-density ・ ・ ・ ・ ・ Particle-hole symmetry
• Velocity-density ・ ・ ・ ・ ・ monotonic decrease
with periodic boundary condition
)1(q4112
1 vJJIn the stationary state of TASEP,flow - density relation is
M.Kanai, K.Nishinari and T.Tokihiro,J. Phys. A: Math. Gen., vol.39 (2006) pp.9071-9079..
Ultradiscrete method
Euler-Lagrange transformation
Macroscopic model
Burgers equation
ASEP(Rule 184)
OV model
CA model
Car-following model
Phys.Rev.Lett., vol.90 (2003) p.088701
J.Phys.A, vol.31 (1998) p.5439
xxxt uuuu 2
Ultradiscrete method reveals the relation between different traffic models!
Toward solution of all kind of jams! Vehicular traffic
cars, bus, trains,… Pedestrians Jams in our body
Ants drop a chemical (generically called pheromone) as they crawl forward. Other sniffing ants pick up the smell of the pheromone and follow the trail.
with periodic boundary conditions
Traffic in ant-trail
Ant trail traffic models and experiments Experiments and theory
Differential equations
)())(()()(
2
2
txUdt
tdx
dt
txd
)(),(),(),( 2 xgtxftxD
t
tx
Ant
pheromonal field
Langevin type equation
),( tx: ant density at x)(xf : evaporation rate
1) M. Burd, D. Archer, N. Aranwela and D.J. Stradling, American Natur. (2002)2) I.D.Couzin and N.R.Franks, Proc.R.Soc.Lond.B (2002)3) A. Dussutour, V. Fourcassie, D.Helbing and J.L. Deneubourg, Nature (2004)
E.M.Rauch, M.M.Millonas and D.R.Chialvo, Phys.Lett.A (1995)
Ant trail CA model
q q Q
1. Ants movement
2. Update Pheromone(creation & diffusion)
Dynamics:
f f fParameters: q < Q, f
One lane, uni-directional flow
D. Chowdhury, V. Guttal, K. Nishinari and A. Schadschneider,J.Phys.A:Math.Gen., Vol. 35 (2002) pp.L573-L577.
Bus Route Model Bus operation system = In fact the ant CA !
The dynamics is the same as the ant model Q Q q
f f f f
Loose cluster formation = buses bunching up together
Modeling of pedestrians Basic features of collective behaviours of pedestrians 1) Arch formation at exit 2) Oscillation of flow at bottleneck 3) Lane formation of counterflow at corridor
Models for evacuation
D.Helbing, I.Farkas and T.Vicsek, Nature (2000).
Floor field model (CA model)
Social force model (Continuous model)
C.Burstedde, K.Klauck, A.Schadschneider,J.Zittartz, Physica A (2001)
Floor field CA Model
Idea : Footprints = Feromone
Long range interaction is
imitated by local interaction
through „memory on a floor“.
C.Burstedde, K.Klauck, A.Schadschneider,J.Zittartz, Physica A, vol.295 (2001) p.507.
Pedestrians in evacuation = herding behavior = long range interaction
For computational efficiency, can we describe the behavior of pedestrians by using local interactions only?
Details of FF model
Floor is devided into cells (a cell=40*40 cm2) Exclusion principle in each cell Parallel update A person moves to one of nearest cells with
the probability defined by „floor field(FF)“. Two kinds of FF is introduced in each cell:
1) Dynamic FF ・・・ footprints of persons
2) Static FF ・・・ Distance to an exit
ijp
Dymanic FF (DFF)Number of footprints on each cell Leave a footprint at each cell whenever a
person leave the cell Dynamics of DFF dissipation + diffusion dissipation ・・・ diffusion ・・・
Herding behaviour = choose the cell that has more footprints Store global information to local cells
4
2
1
1
Static FF (SFF) = Dijkstra metric Distance to the destination is recorded at each cell
This is done by Visibility Graph and Dijkstra method.Two exits with four obstacles
0 20 40 60 80 1000
20
40
60
80
100
One exit with a obstacle
K. Nishinari, A. Kirchner, A. Namazi and A. Schadschneider, IEICE Trans. Inf. Syst., Vol.E87-D (2004) p.726.
Problem of “Zone partition”
By using SFF
Application of SFF
Which door is the nearest?
The ratio of an area to the totalarea determines the number ofpeople who use the door in escaping from this room.
Probability of movement
)exp()exp()exp( ijIijsijDij IkSkDkp
ijI
ijS
ijDDistance between the cell (i,j) and a door.
Number of footprints at the cell (i,j).
Set I=1 if (i,j) is the previous direction of motion.
Update procedure
1. Update DFF (dissipation & diffusion)
2. Calculate and determine the target cell
3. Resolution of conflict
4. Movement
5. Add DFF +1
ijp
Parameter ]1,0[
1
All of them cannot move.
One of them can move.
Resolution of conflict
Initial: Calculate SFF
Meanings of parameters in the model kS kD
kS large: Normal (kS small: Random walk)
kD large: Panic
kD / kS ・・・ Panic degree (panic parameter)
large: competition
small: coorporation
)exp()exp( ijsijDij SkDkp
Simulations using inertia effect There is a minimum in the evacuation time when
the effect of inertia is introduced.
People becomeless flexible toform arches.(Do not care others!) People become flexible
to avoid congestion.
SFF is strongly disturbed.
Simulation Example: Evacuation at Osaka-Sankei Hall
Jams near exits.
Hamburg airport in Germany
Influence of Obstacle3.0,0,10 DS kkPlaced an obstacle near exit.
1offset
Center
None
2offset
Intensive competition.
A.Kirchner, K.Nishinari, and A.Schadschneider,Phys. Rev. E, vol.67 (2003) p.056122.D.Helbing, I.Farkas and T.Vicsek, Nature, vol.407 (2000) p.487.
If an obstacle is placed asymmetrically, total evacuation time is reduced !
Conclusions• Traffic Jams everywhere
= Jammology is interdisciplinary research
among Math. , Physics and Engineering!
Examples
• Ant trail CA model is proposed by extending ASEP. The model is well analyzed by ZRP.
• Non-monotonic variation of the average speed of the ants is confirmed by robots experiment.
• Traffic jam in our body is related to diseases.
• Modelling molecular motors
• Jammology = Math. , Physics and Engineering
Conclusions
Ant trail CA model is proposed by extending ASEP. The model is well analyzed by ZRP.
Non-monotonic variation of the average speed of the ants is confirmed by robots experiment.
FF model is a local CA model with memory, which can emulating grobal behavior.
FF model is quite efficient tool for simulating
pedestrian behavior. Jammology = Math. , Physics and Engineering
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