analysis motivated by vehicular traffic and crowd dynamics · rmcolombo conservation laws...
TRANSCRIPT
![Page 1: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/1.jpg)
RMColombo
Analysis Motivated byVehicular Traffic and Crowd Dynamics
Rinaldo M. ColomboUniversity of Brescia
http://rinaldo.unibs.it
Benasque – August 22nd, 2017
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RMColombo
Conservation Laws
Introduction
Vehicular TrafficMacroscopic ModelsBraess Paradox
Crowd DynamicsModeling CrowdControlling Crowd
Predators – Prey
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RMColombo
Introduction – Analytic Theory
∂tu + divx f (t, x , u) = g(t, x , u)t ∈R+ timex ∈RN spaceu ∈Rn unknown
f smooth fluxg smooth source
Euler Statement 1755Riemann Regular Solutions 1860
n ≥ 1, N ≥ 1?
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RMColombo
Introduction – Analytic Theories
∂tu + divx f (t, x , u) = g(t, x , u)t ∈R+ timex ∈RN spaceu ∈Rn unknown
f smooth fluxg smooth source
Scalar MultiD n = 1, N ≥ 1
∂tu + divx f (t, x , u) = g(t, x , u)
Existence(Kruzkov: Mat.Sb., 1970)
Uniqueness(Kruzkov: Mat.Sb., 1970)
Dependence on data(Kruzkov: Mat.Sb., 1970)
Dependence on f , g(Colombo, Mercier, Rosini: CMS, 2009)
Systems in 1D n ≥ 1, N = 1
∂tu + ∂x f (u) = 0
Existence(Glimm: CPAM, 1965)
Uniqueness(Bressan & c.: 1999, 2000)
Dependence on data(Bressan & c.: 1995, 2000)
Dependence on f(Bianchini, Colombo: PAMS, 2002)
n ≥ 1, N ≥ 1?
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RMColombo
Introduction – Analytic Theories
∂tu + divx f (t, x , u) = g(t, x , u)t ∈R+ timex ∈RN spaceu ∈Rn unknown
f smooth fluxg smooth source
Scalar MultiD n = 1, N ≥ 1
∂tu + divx f (t, x , u) = g(t, x , u)
Existence(Kruzkov: Mat.Sb., 1970)
Uniqueness(Kruzkov: Mat.Sb., 1970)
Dependence on data(Kruzkov: Mat.Sb., 1970)
Dependence on f , g(Colombo, Mercier, Rosini: CMS, 2009)
Systems in 1D n ≥ 1, N = 1
∂tu + ∂x f (u) = 0
Existence(Glimm: CPAM, 1965)
Uniqueness(Bressan & c.: 1999, 2000)
Dependence on data(Bressan & c.: 1995, 2000)
Dependence on f(Bianchini, Colombo: PAMS, 2002)
n ≥ 1, N ≥ 1?
![Page 6: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/6.jpg)
RMColombo
Introduction – Analytic Theories
∂tu + divx f (t, x , u) = g(t, x , u)t ∈R+ timex ∈RN spaceu ∈Rn unknown
f smooth fluxg smooth source
Scalar MultiD n = 1, N ≥ 1
∂tu + divx f (t, x , u) = g(t, x , u)
Existence(Kruzkov: Mat.Sb., 1970)
Uniqueness(Kruzkov: Mat.Sb., 1970)
Dependence on data(Kruzkov: Mat.Sb., 1970)
Dependence on f , g(Colombo, Mercier, Rosini: CMS, 2009)
Systems in 1D n ≥ 1, N = 1
∂tu + ∂x f (u) = 0
Existence(Glimm: CPAM, 1965)
Uniqueness(Bressan & c.: 1999, 2000)
Dependence on data(Bressan & c.: 1995, 2000)
Dependence on f(Bianchini, Colombo: PAMS, 2002)
n ≥ 1, N ≥ 1?
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f (u) = λ u ∂tu + λ∂xu = 0 u(t, x) = uo(x − λ t)
u
x
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f (u) = λ u ∂tu + λ∂xu = 0 u(t, x) = uo(x − λ t)
u
x
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f (u) = λ u ∂tu + λ∂xu = 0 u(t, x) = uo(x − λ t)
u
x
u
x
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
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RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
![Page 14: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/14.jpg)
RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
u
x
![Page 15: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/15.jpg)
RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
![Page 16: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/16.jpg)
RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
![Page 17: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/17.jpg)
RMColombo
Introduction – Key Features
1. Evolution
2. Irreversible
3. Finite Speed
4. Conservation
5. Singularities
Simplest Casen = 1,N = 1,
f = f (u), g ≡ 0{∂tu + ∂x f (u) = 0u(0, x) = uo(x)
f non linear ∂tu + f ′(u) ∂xu = 0 u(t, x) =?
u
x
u
x
![Page 18: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/18.jpg)
RMColombo
Introduction
Discontinuities!
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RMColombo
Discontinuities!
Viscosity Entropy Stability
They all agree!
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RMColombo
Discontinuities!
Viscosity Entropy Stability
They all agree!
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RMColombo
Vehicular Traffic
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
cars are conserved
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I Multi–Population Models
I Networks
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I Multi–Population Models
I Networks
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I Multi–Population Models
I Networks
![Page 25: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/25.jpg)
RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I Requiem (Daganzo: Transp. Research B, 1995)I Resurrection (Aw, Rascle: SIAM Appl. Math., 2000)I (Zhang: Transp. Research B, 2002)
I 2–Phase Models
I Multi–Population Models
I Networks
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I (Colombo: SIAM Appl. Math., 2002)I (Colombo, Marcellni, Rascle: SIAM Appl. Math., 2010)I (Blandin, Work, Goatin, Piccoli, Bayen: SIAM Appl. Math., 2010)
I Multi–Population Models
I Networks
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I Multi–Population Models
I (Zhang, Jin: Transp. Research Rec., 2002)I (Benzoni–Gavage, Colombo: EJAM, 2003)
I Networks
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RMColombo
Vehicular Traffic – Macroscopic Models
t = timex = space
ρ =
{(density)occupancy
∂tρ+ ∂x(ρ v) = 0
v = ?
LWR(Lighthill, Whitham: Proc. London. A., 1955)
(Richards: Operations Res., 1956)
v decreasingv(0) = vmax
v(R) = 0
I Second Order Models
I 2–Phase Models
I Multi–Population Models
I Networks
I (Garavello, Kahn, Piccoli: Book, 2016)
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RMColombo
Vehicular Traffic
– Braess Paradox
D
A
B
C
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
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RMColombo
Vehicular Traffic
– Braess Paradox
D
A
B
C
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Route ABC : 4000 cars need4000
100+ 45 = 85
![Page 31: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/31.jpg)
RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
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RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Route ABC :] cars
100+ 45
⇒ 2000
100+ 45 = 65
Route ADC : 45 +] cars
100
⇒ 2000
100+ 45 = 65
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RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Route ABC :] cars
100+ 45 ⇒ 2000
100+ 45 = 65
Route ADC : 45 +] cars
100⇒ 2000
100+ 45 = 65
![Page 34: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/34.jpg)
RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
![Page 35: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/35.jpg)
RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Route ABC :] cars
100+ 45
Route ADC : 45 +] cars
100
Route ABDC :] cars
100+ 0 +
] cars
100
⇒ 4000
100+
4000
100= 80
![Page 36: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/36.jpg)
RMColombo
Vehicular Traffic
– Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Route ABC :] cars
100+ 45
Route ADC : 45 +] cars
100
Route ABDC :] cars
100+ 0 +
] cars
100⇒ 4000
100+
4000
100= 80
![Page 37: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/37.jpg)
RMColombo
Vehicular Traffic – Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Only ABC 80ABC + ADC 65ABC + ADC + ABDC 80
![Page 38: Analysis Motivated by Vehicular Traffic and Crowd Dynamics · RMColombo Conservation Laws Introduction Vehicular Tra c Macroscopic Models Braess Paradox Crowd Dynamics Modeling Crowd](https://reader034.vdocument.in/reader034/viewer/2022042223/5ec9effbcecb1a389121035c/html5/thumbnails/38.jpg)
RMColombo
Vehicular Traffic – Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Only ABC 80ABC + ADC 65ABC + ADC + ABDC 80
Nash equilibrium vs. optimality
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RMColombo
Vehicular Traffic – Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
Real!
Stuttgart Highway segment closed 1968
New York 42nd street closed 22.04.1990Seoul 6 lanes highway substituted by a park 2008
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RMColombo
Vehicular Traffic – Braess Paradox
A
B
C
D
From To Time
A B] cars
100
B C 45
A D 45
D B] cars
100
B D 0
(Colombo, Holden: JOTA, 2016)
Characterization?Dynamics?
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RMColombo
Crowd Dynamics
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RMColombo
Crowd Dynamics
∂tρ+ divx(ρ v(ρ)~v(x)
)= 0
{v = speed modulus~v = velocity direction
(Colombo, Facchi, Maternini: HYP2008 Proceedings, 2009)
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RMColombo
Crowd Dynamics∂tρ+ divx
(ρ ~V (ρ, x)
)= 0
∂tρ+ divx
(ρ v(ρ)
(~v(x)
))= 0
Theorem: If: v is smooth, decreasing, v(0) = V , v(ρ) = 0;~v is smooth;η is smooth with compact support;
Then: Existence & Uniqueness in L1
Lipschitz Continuity from Data and EquationViability (discomfort)
(Colombo, Garavello, Lecureux–Mercier: M3AS, 2012)
(Colombo, Lecureux–Mercier: Acta Math. Sc., 2012)
(Gottlich, Hoher, Schindler, Schleper: Appl.Mat.Mod., 2014)
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RMColombo
Crowd Dynamics∂tρ+ divx
(ρ ~V (ρ, x)
)= 0
∂tρ+ divx
ρ v(ρ)
~v(x) +avoidhigh
density
= 0
Theorem: If: v is smooth, decreasing, v(0) = V , v(ρ) = 0;~v is smooth;η is smooth with compact support;
Then: Existence & Uniqueness in L1
Lipschitz Continuity from Data and EquationViability (discomfort)
(Colombo, Garavello, Lecureux–Mercier: M3AS, 2012)
(Colombo, Lecureux–Mercier: Acta Math. Sc., 2012)
(Gottlich, Hoher, Schindler, Schleper: Appl.Mat.Mod., 2014)
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RMColombo
Crowd Dynamics∂tρ+ divx
(ρ ~V (ρ, x)
)= 0
∂tρ+ divx
ρ v(ρ)
~v(x)− κ gradx (ρ ∗ η)√1 +
∥∥gradx (ρ ∗ η)∥∥2
= 0
Theorem: If: v is smooth, decreasing, v(0) = V , v(ρ) = 0;~v is smooth;η is smooth with compact support;
Then: Existence & Uniqueness in L1
Lipschitz Continuity from Data and EquationViability (discomfort)
(Colombo, Garavello, Lecureux–Mercier: M3AS, 2012)
(Colombo, Lecureux–Mercier: Acta Math. Sc., 2012)
(Gottlich, Hoher, Schindler, Schleper: Appl.Mat.Mod., 2014)
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RMColombo
Crowd Dynamics∂tρ+ divx
(ρ ~V (ρ, x)
)= 0
∂tρ+ divx
ρ v(ρ)
~v(x)− κ gradx (ρ ∗ η)√1 +
∥∥gradx (ρ ∗ η)∥∥2
= 0
Theorem: If: v is smooth, decreasing, v(0) = V , v(ρ) = 0;~v is smooth;η is smooth with compact support;
Then: Existence & Uniqueness in L1
Lipschitz Continuity from Data and EquationViability (discomfort)
(Colombo, Garavello, Lecureux–Mercier: M3AS, 2012)
(Colombo, Lecureux–Mercier: Acta Math. Sc., 2012)
(Gottlich, Hoher, Schindler, Schleper: Appl.Mat.Mod., 2014)
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RMColombo
Crowd Dynamics∂tρ+ divx
(ρ ~V (ρ, x)
)= 0
∂tρ+ divx
ρ v(ρ)
~v(x)− κ gradx (ρ ∗ η)√1 +
∥∥gradx (ρ ∗ η)∥∥2
= 0
Theorem: If: v is smooth, decreasing, v(0) = V , v(ρ) = 0;~v is smooth;η is smooth with compact support;
Then: Existence & Uniqueness in L1
Lipschitz Continuity from Data and EquationViability (discomfort)
(Colombo, Garavello, Lecureux–Mercier: M3AS, 2012)
(Colombo, Lecureux–Mercier: Acta Math. Sc., 2012)
(Gottlich, Hoher, Schindler, Schleper: Appl.Mat.Mod., 2014)
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RMColombo
Crowd Dynamics – Time to Exit
t = 0.000
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RMColombo
Crowd Dynamics – Time to Exit
t = 1.010
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RMColombo
Crowd Dynamics – Time to Exit
t = 2.118
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RMColombo
Crowd Dynamics – Time to Exit
t = 4.438
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RMColombo
Crowd Dynamics – Time to Exit
t = 6.253
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RMColombo
Crowd Dynamics – Time to Exit
t = 8.774
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RMColombo
Crowd Dynamics – Time to Exit
t = 11.396
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RMColombo
Crowd Dynamics – Lane Formation
Two populations moving in opposite directions
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RMColombo
Crowd Dynamics – Lane Formation
∂tρ1 + divx
ρ1 v(ρ1)
(~v1(x)− ε11∇(ρ1∗η)√
1+‖∇(ρ1∗η)‖2− ε12∇(ρ2∗η)√
1+‖∇(ρ2∗η)‖2
) = 0
∂tρ2 + divx
ρ2 v(ρ2)
(~v2(x)− ε21∇(ρ1∗η)√
1+‖∇(ρ1∗η)‖2− ε22∇(ρ2∗η)√
1+‖∇(ρ2∗η)‖2
) = 0
~v1 =
[10
]+ δ η(x , y) =
[1− (2x)2
]3 [1− (2y)2
]3χ
[−0.5,0.5]2(x , y)
~v2 =
[−10
]+ δ v(ρ) = 4 (1− ρ)
ε11 = 0.3 ε12 = 0.7ε21 = 0.7 ε22 = 0.3
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RMColombo
Crowd Dynamics – Shepherd Dog (Consensus)
Given:
∂tρ+ divx(ρ v(x , ρ, p)
)= 0 HCL
p = ϕ(t, p,
(ρ(t) ∗ η
)(p))
ODE
IF: v ∈ C2([0,R]× RN × RN ;RN) is such that . . .η ∈ C1
c(RN ;R)ϕ Caratheodory, Locally Lipschitz, Sublinear
Then: There exists a solution (u,w), withX ρ = ρ(t, x) weak entropy solution to HCLX p = p(t) Caratheodory solution to ODEX stability estimates∥∥(ρ1 − ρ2)(t)
∥∥L1
+∥∥(p1 − p2)(t)
∥∥≤ C (t) ·
(∥∥∂ρ(v1 − v2)∥∥L∞
+∥∥divx (v1 − v2)
∥∥L1
+‖ϕ1 − ϕ2‖L∞ + ‖η1 − η2‖L1
+‖ρ1 − ρ2‖L1 + ‖p1 − p2‖)
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RMColombo
Crowd Dynamics – Shepherd Dog (Consensus)
Given:
∂tρ+ divx(ρ v(x , ρ, p)
)= 0 HCL
p = ϕ(t, p,
(ρ(t) ∗ η
)(p))
ODE
IF: v ∈ C2([0,R]× RN × RN ;RN) is such that . . .η ∈ C1
c(RN ;R)ϕ Caratheodory, Locally Lipschitz, Sublinear
Then: There exists a solution (u,w), withX ρ = ρ(t, x) weak entropy solution to HCLX p = p(t) Caratheodory solution to ODEX stability estimates∥∥(ρ1 − ρ2)(t)
∥∥L1
+∥∥(p1 − p2)(t)
∥∥≤ C (t) ·
(∥∥∂ρ(v1 − v2)∥∥L∞
+∥∥divx (v1 − v2)
∥∥L1
+‖ϕ1 − ϕ2‖L∞ + ‖η1 − η2‖L1
+‖ρ1 − ρ2‖L1 + ‖p1 − p2‖)
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RMColombo
Crowd Dynamics – Shepherd Dog (Consensus)
Given:
∂tρ+ divx(ρ v(x , ρ, p)
)= 0 HCL
p = ϕ(t, p,
(ρ(t) ∗ η
)(p))
ODE
IF: v ∈ C2([0,R]× RN × RN ;RN) is such that . . .η ∈ C1
c(RN ;R)ϕ Caratheodory, Locally Lipschitz, Sublinear
Then: There exists a solution (u,w), withX ρ = ρ(t, x) weak entropy solution to HCLX p = p(t) Caratheodory solution to ODEX stability estimates∥∥(ρ1 − ρ2)(t)
∥∥L1
+∥∥(p1 − p2)(t)
∥∥≤ C (t) ·
(∥∥∂ρ(v1 − v2)∥∥L∞
+∥∥divx (v1 − v2)
∥∥L1
+‖ϕ1 − ϕ2‖L∞ + ‖η1 − η2‖L1
+‖ρ1 − ρ2‖L1 + ‖p1 − p2‖)
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RMColombo
Crowd Dynamics – Shepherd Dog (Consensus)
(Colombo, Mercier: JNLS, 2012)
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RMColombo
Crowd Dynamics – Shepherd Dog (Consensus)
(Colombo, Mercier: JNLS, 2012)
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RMColombo
Crowd Dynamics – Policemen vs. Hooligans
∂tρi + divx
[ρi (1− ρi )
(−w i (x , p) +Ai (ρ)
)]= 0 i = 1, 2
pk = Ik(p) + Bk(ρ) k = 1, . . . , 4
A1(ρ) = ε11 η∗(ρ1−ρ) ∇x (ρ1∗η)√1+‖η∗(ρ1−ρ)∇x (ρ1∗η)‖2
+ ε12 η∗(ρ2−ρ1)∇x (ρ2∗η)√1+‖η∗(ρ2−ρ1)∇x (ρ2∗η)‖2
,
A2(ρ) = ε22 η∗(ρ2−ρ) ∇x (ρ2∗η)√1+‖η∗(ρ1−ρ)∇x (ρ2∗η)‖2
+ ε21 η∗(ρ1−ρ2)∇x (ρ1∗η)√1+‖η∗(ρ1−ρ2)∇x (ρ1∗η)‖2
,
Bk(ρ)(p) = ε1∇x ((η∗ρ1)(η∗ρ2))(pk )√
1+‖∇x ((η∗ρ1)(η∗ρ2))(pk )‖2
(Borsche, Colombo, Garavello, Meurer: JNLS, 2015)
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RMColombo
Crowd Dynamics – Policemen vs. Hooligans
(Borsche, Colombo, Garavello, Meurer: JNLS, 2015)
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RMColombo
Crowd Dynamics – 3D!
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RMColombo
Crowd Dynamics – 3D!
Film
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RMColombo
Hyperbolic Parabolicvs.
Predators – Prey
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RMColombo
Hyperbolic Parabolicvs.
Predators
–
Prey
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RMColombo
Hyperbolic Predators vs. Parabolic Prey
Predators: u = u(t)Prey: w = w(t)
{∂tu
+ divx(u v(w)
)
= (αw − β) u∂tw
− µ∆w
= (γ − δ u)w
α = predators birth rate due to prey
β = predators mortality rate
γ = prey birth rate
δ = prey mortality rate due to predators
Predators Prey
v(w) = κgrad (w ∗ η)√
1 +∥∥grad (w ∗ η)
∥∥2diffuse
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RMColombo
Hyperbolic Predators vs. Parabolic Prey
Predators: u = u(t, x)Prey: w = w(t, x)
{∂tu
+ divx(u v(w)
)
= (αw − β) u∂tw − µ∆w = (γ − δ u)w
α = predators birth rate due to prey
β = predators mortality rate
γ = prey birth rate
δ = prey mortality rate due to predators
Predators
Prey
v(w) = κgrad (w ∗ η)√
1 +∥∥grad (w ∗ η)
∥∥2
diffuse
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RMColombo
Hyperbolic Predators vs. Parabolic Prey
Predators: u = u(t, x)Prey: w = w(t, x)
{∂tu + divx
(u v(w)
)= (αw − β) u
∂tw − µ∆w = (γ − δ u)w
α = predators birth rate due to prey
β = predators mortality rate
γ = prey birth rate
δ = prey mortality rate due to predators
Predators Prey
v(w) = κgrad (w ∗ η)√
1 +∥∥grad (w ∗ η)
∥∥2diffuse
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RMColombo
Hyperbolic Predator vs. Parabolic Prey
There exists R : R+ ×X+ → X+ with the properties:
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RMColombo
Hyperbolic Predator vs. Parabolic Prey
There exists R : R+ ×X+ → X+ with the properties:
1. X+ = (L1 ∩ L∞ ∩ BV)(RN ;R)× (L1 ∩ L∞)(RN ;R)
2. R is a semigroup
3. t → Rt(uo ,wo) solves the system
4. t → Rt(uo ,wo) is continuous in time
5. (uo ,wo)→ Rt(uo ,wo) is locally Lipschitz continuous
6. Growth estimates
7. Propagation speed
(Colombo, Rossi: Comm.Math.Sc., 2015)
(Colombo, Marcellini, Rossi: NHM, 2016)
(Rossi, Schleper: M2AN, 2016)
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RMColombo
Hyperbolic Predator vs. Parabolic Prey