on the modeling of vehicular...
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ON THE MODELING OF VEHICULAR TRAFFIC
ON THE MODELING OF VEHICULAR
TRAFFIC
Ph.D. CourseComplex Systems in Engineering Sciences
Luisa FermoDipartimento di Matematica, Politecnico di Torino
ON THE MODELING OF VEHICULAR TRAFFIC
The system to be modeled: Vehicular Traffic along a one-way road. A first
description.
“The width of a traffic shock only encompasses a few vehicles ”
“ A fluid particle responds to stimuli from the front and frombehind, but a car is an anisotropic particle that mostly responds tofrontal stimuli ”
“ Unlike molecules, vehicles have personalities (e.g., aggressive andtimid) that remain unchanged by motion”
C.F. Daganzo
Requiem for second-order fluid approximation of traffic flow ,
Transp. Res., 29B (1995), 277/286
ON THE MODELING OF VEHICULAR TRAFFIC
The first step towards modeling: the choice of the scale of representation.
◮ Microscopic scale: Each vehicle is individually followed.The model writes as
xi = ai [t, {xk}Nk=1, {xk}
Nk=1], i = 1, ...,N
where xi is the scalar position of the i-th vehicle,t is the time,xi is the velocity,ai is a function describing the acceleration.
These models are not competitive from a computational pointof view.
ON THE MODELING OF VEHICULAR TRAFFIC
The first step towards modeling: the choice of the scale of representation.
◮ Macroscopic scale: Each vehicle is not individually followed.An example is given by
∂ρ
∂t+
∂q
∂x= 0
where ρ is the density,q is the flux.
Some of these models recover missing information fromexperimental observation in steady flow conditions.
ON THE MODELING OF VEHICULAR TRAFFIC
The first step towards modeling: the choice of the scale of representation.
◮ Kinetic Scale
1. Each vehicle is identified at time t∗ by◮ its position x
∗
∈ Dx∗ = [0, L] with L > 0 the length of theroad;
◮ its velocity v∗
∈ Dv∗ = [0, Vmax ] with Vmax > 0 the maximumspeed allowed along the road.
Remark: In the following we will consider the dimensionlessquantities:
x =x∗
L∈ Dx ≡ [0, 1], v =
v∗
Vmax
∈ Dv ≡ [0, 1],L
Vmax
t∗ = t.
The set {x , v} defines the microscopic state of the vehicle.
ON THE MODELING OF VEHICULAR TRAFFIC
The first step towards modeling: the choice of the scale representation.
2. In order to describe the overall system a distribution function
f = f (t, x , v) : [0, Tmax ] × Dx × Dv → R+
is introduced.
f (t, x , v)dxdv represents the infinitesimal number of vehiclesthat at time t are located in [x , x + dx ] and travel with aspeed belonging to [v , v + dv ].
ON THE MODELING OF VEHICULAR TRAFFIC
The first step towards modeling: the choice of the scale representation.
3. The model writes as:
∂f
∂t+ v
∂f
∂x= J[f ]
where J describes the interactions among vehicles.
4. One can compute macroscopic quantities like the density
ρ(t, x) =
∫
Dv
f (t, x , v)dv ,
and the flux
q(t, x) =
∫
Dv
v f (t, x , v)dv .
ON THE MODELING OF VEHICULAR TRAFFIC
Modeling the vehicular traffic at the Kinetic Scale
Remark on the Kinetic Approach
Problem:Like the macroscopic models, the kinetic approach is based on acontinuum hyphothesis.
⇓
This assumption is not phisically satisfied by cars along a road.
⇓
A Possible Solution: Discrete velocity kinetic models.
ON THE MODELING OF VEHICULAR TRAFFIC
Discrete velocity kinetic models: the basic idea
1. Introduce in Dv = [0, 1] a grid
Iv = {v1, v2, ..., vn−1, vn}
with v1 ≡ 0,vn ≡ 1,
vi < vi+1, ∀i = 1, ..., n − 1.
ON THE MODELING OF VEHICULAR TRAFFIC
Discrete velocity kinetic models: the basic idea
2. The overall system is now described by
f (t, x , v) =n
∑
i=1
fi (t, x)δvi(v)
where the n functions fi (t, x) = [0, Tmax ] × Dx → R+ are the
distribution functions of the speed classes.
fi (t, x)dx denotes the infinitesimal number of vehicles havingspeed vi that at time t are in [x , x + dx ].
ON THE MODELING OF VEHICULAR TRAFFIC
Discrete velocity kinetic models: the basic idea
3. The model writes as:
∂fi∂t
+ vi∂fi∂x
= Ji [f], ∀i = 1, ..., n
where f = (f1, ...fn) and Ji describes the interactions amongvehicles.
4. One can compute macroscopic quantities like the density
ρ(t, x) =n
∑
i=1
fi (t, x)
and the flux
q(t, x) =n
∑
i=1
vi fi (t, x).
ON THE MODELING OF VEHICULAR TRAFFIC
Discrete velocity kinetic model: the interaction term Ji
The kinetic models describe the interactions by appealing to thefollowing guidelines:
1. Cars are regarded as points, their dimensions are negligible;
2. Interactions are binary. More precisely we will call
A. Candidate vehicle: the vehicle that change its state;B. Field vehicle: the vehicle that causes such a change;C. Test vehicle: an ideal vehicle of the system whose microscopic
state is targeted by a hypothetical observer;
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity model: the interaction term Ji
3. Interactions modify by themselves only the velocity of thevehicles, not their positions;
4. Vehicles are anisotropic particles;
5. Interactions are conservative, in the sense that they preservethe total number of vehicles of the system. Thus the operatorJi [f] is required to satisfy
n∑
i=1
Ji [f] = 0.
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity model: the interaction term Ji
If the interactions are such that 1 − 5 are fulfilled then
Ji [f] = Gi [f, f] − fiLi [f],
where
◮ Gi [f, f] is the i-th gain operator giving the amount per unittime that get the velocity vi ;
◮ Li [f] is the i-th loss operator giving the amount of vehicles perunit time that lose the velocity vi .
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity model: the interaction term Ji
The interactions among vehicles are described in a stochastic way.Then,
◮ A table of games Aihk is introduced such that
Aihk ≥ 0,
n∑
i=1
Aihk = 1, ∀i = 1, ..., n.
◮ Moreover, an encounter rate ηhk is defined.
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity model: a look at the literature
V. Coscia, M. Delitala and P. FrascaOn the mathematical theory of vehicular traffic flow II. Discrete velocity kinetic
models ,Int. J. Non-Linear Mech., 42(3) (2007), 411-421.
M. Delitala and A. TosinMathematical modeling of vehicular traffic: a discrete kinetic theory approach ,Math. Models Methods Appl. Sci., 17 (2007), 901-932.
C. Bianca and E. CosciaOn the coupling of steady and adaptive velocity grids in vehicular traffic
modelling,
24(2) (2011), 149-155.
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity models with Local Interactions
The models write as:
∂fi∂t
+ vi∂fi∂x
= Ji [f], ∀i = 1, ..., n
where
Ji [f] =n
∑
h,k=1
ηhkAihk fhfk − fi
n∑
k=1
ηik fk
V. Coscia, M. Delitala and P. FrascaOn the mathematical theory of vehicular traffic flow II. Discrete velocity kinetic
models ,Int. J. Non-Linear Mech., 42(3) (2007), 411-421.
C. Bianca and E. CosciaOn the coupling of steady and adaptive velocity grids in vehicular traffic
modelling,
24(2) (2011), 149-155.
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity models with non local interactions
◮ One define Interaction length ξ the distance between theinteracting field and the candidate vehicle.
◮ If x is the position of the candidate vehicle and ξ is the lengthinteraction then one can define the interaction interval orvisibility zone as Iξ(x) = [x , x + ξ].
◮ One introduce a weight function w(x , y) weigthing theinteraction over the visibility zone.
M. Delitala and A. TosinMathematical modeling of vehicular traffic: a discrete kinetic theory approach ,Math. Models Methods Appl. Sci., 17 (2007), 901-932.
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear
ON THE MODELING OF VEHICULAR TRAFFIC
Kinetic discrete velocity models with non local interactions
The models write as
∂fi∂t
+ vi∂fi∂x
= Ji [f], ∀i = 1, ..., n (1)
where
Ji [f] =n
∑
h,k=1
∫
Iξ
ηhk(t, y)Aihk(t, y)fh(t, x)fk(t, y)w(x , y)dy
− fi (t, x)
n∑
k=1
∫
Iξ
ηik(t, y)fk(t, y)w(x , y)dy .
M. Delitala and A. Tosin
Mathematical modeling of vehicular traffic: a discrete kinetic theory approach ,Math. Models Methods Appl. Sci., 17 (2007), 901-932.
A. Bellouquid, E. De Angelis and L. Fermo
Towards the modeling of vehicular traffic as a complex system: a kinetic approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
All the terms appearing in the equations are modelled taking intoaccount the Traffic Phase, namely, traffic states having specificempirical spatiotemporal features. These features are specific onlyto a single traffic phase. It is characterized by a certain set ofstatistical properties of traffic variables (density, mean velocity,flux).
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
Classically there are two traffic phase:
◮ Free flow where vehicles are able to change a lane and topass. The maximum density achievable under free flow iscalled critical density ρc .
◮ Congested flow occurs when the vehicle density is highenough. Here the speed is lower than the lowest speed in freeflow.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
Kerner identify three traffic phase:
◮ Free flow (F) where vehicles are able to change a lane and topass. The maximum density achievable under free flow iscalled critical density ρc .
◮ Congested flow occurs when the vehicle density is highenough. Here the speed is lower than the lowest speed in freeflow.
◮ Syncronized flow (S) Kerner describes synchronized flow asthe phase at which vehicles are accelerating to meet free flowtraffic.
◮ Wide moving jam (J) When vehicles move up the highwaythrough bottlenecks. The minimum density achieved undercongestion is called jam density ρj .
B. S. KernerThe Physics of Traffic,Springer 2004.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
Now, we come back to equation (1) and model terms ηhk and Aihk .
◮ At first, we introduce a parameter α identifying theenvironmental conditions:
α = α0 +ρc
ρcmax(1 − α0),
where α0 is the minimum value of α identified by experiments,ρc is the critical density,ρcmax is the maximum critical density.
◮ Then we define the encounter rate η = ηhk = 1 + αρ2, ∀h, k .
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
◮ We give a table of games according to the three traffic phase.In FREE FLOW (0 ≤ ρ ≤ ρc)
Aihk =
1, i = n;
0, otherwise.(2)
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
◮ In WIDE MOVING JAM (ρj ≤ ρ ≤ 1)
Aihk =
1, i = 1;
0, otherwise.(3)
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
◮ In SYNCRONIZED FLOW (ρc < ρ < ρj)
A. Interaction with faster vehicles.
Aihk =
1 − α (ρj + ρc − ρ), i = h;
α(i − h)
1k
∑
i=h+1
1
i − h
(ρj + ρc − ρ), i = h + 1, ..., k ;
0, otherwise.
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
B. Interaction with slower vehicles.
Aihk =
α (ρj + ρc − ρ), i = h;
[1 − α (ρj + ρc − ρ)](h − i)
h−1∑
i=k
(h − i)
, i = k , ..., h − 1;
0, otherwise.
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
C. Interaction with equally fast vehicles.
Aihk =
(1 − α)(h − i)
h−1∑
i=1
(h − i)
(1 − ρj − ρc + ρ), i = 1, ...h − 1;
α + (1 − 2α)(ρs + ρc − ρ), i = h;
α 1(i − h)
1n
∑
i=h+1
1
i − h
(ρj + ρc − ρ), i = h + 1, ..., n.
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
C. Interaction with equally fast vehicles.
Ai11 =
1 − α(ρj + ρc − ρ), i = 1;
α(ρj + ρc − ρ), i = 2;
0, otherwise.
Ainn =
α (1 − ρj − ρc + ρ), i = n − 1;
1 − α (1 − ρj − ρc + ρ), i = n;
0, otherwise.
A. Bellouquid, E. De Angelis and L. Fermo
Towards the modeling of vehicular traffic as a complex system: a kinetic approach,Math. Models Methods Appl. Sci., (2012), to appear.
ON THE MODELING OF VEHICULAR TRAFFIC
A Kinetic discrete velocity model
Only for the sake of simplicity here we have consideredthe microscopic state {x , v}.
In the following paper
A. Bellouquid, E. De Angelis and L. FermoTowards the modeling of vehicular traffic as a complex system: a kinetic
approach,Math. Models Methods Appl. Sci., (2012), to appear.
the microscopic state {x , v , u} is considered where u is anadditional variable denoting the quality of the micro-systemdriver-vehicle.
ON THE MODELING OF VEHICULAR TRAFFIC
The non stationary problem
◮ We study the evolution of the system.The solution will depend on the time and mathematically themodel will be described by
1. Integro-differential equations;2. Initial conditions;3. Boundary conditions.
ON THE MODELING OF VEHICULAR TRAFFIC
Example: Formation of Clustering
The model:
∂fi∂t
+ vi∂fi∂x
= Ji [f]
fn(0, x) = 100 sin2 (10π(x − 0.2)(x − 0.3)), x ∈ [0.2, 0.3],
fn−1(0, x) = 50 sin2 (10π(x − 0.5)(x − 0.6)), x ∈ [0, 5, 0.6]
fi (0, x) = 0, ∀i = 1, ..., n − 2, ∀x ∈ Dx
fi (t, 0) = fi (t, 1) ∀i = 1, ..., n
ON THE MODELING OF VEHICULAR TRAFFIC
Example: Formation of Clustering in bad road conditions
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=1.1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=1.14
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρt=1.40
ON THE MODELING OF VEHICULAR TRAFFIC
Example: Formation of Clustering in optimal road conditions
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=1.9
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
ρ
t=2.19
ON THE MODELING OF VEHICULAR TRAFFIC
The spatially homogeneous problem
The model writes as
dfijdt
= η
n∑
h,k=1
Aihk fh(t) fk(t) − fi (t)
n∑
k=1
fk(t)
,
fi (0) = f 0i ,
ON THE MODELING OF VEHICULAR TRAFFIC
Velocity Diagram
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ρ
ξ
α=0.95
α=0.7
α=0.5
α=0.3
Figure: Velocity diagram: mean velocity ξ versus density ρ
ON THE MODELING OF VEHICULAR TRAFFIC
Fundamental Diagram
0 0.2 0.4 0.6 0.8 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
ρ
q
α=0.95
α=0.7
α=0.5
α=0.3
Figure: Fundamental diagram: flux q versus density ρ