In this paper we propose a multiscale traffic model, based on the family of Generic Second Order Models, which integrates multiple trajectory data into the velocity function. This combination of a second order macroscopic model with microscopic information allows us to reproduce significant variations in speed and acceleration that strongly influence traffic emissions. We obtain accurate approximations even with a few trajectory data. The proposed approach is therefore a computationally efficient and highly accurate tool for calculating macroscopic traffic quantities and estimating emissions.
Citation: Caterina Balzotti, Maya Briani. Estimate of traffic emissions through multiscale second order models with heterogeneous data[J]. Networks and Heterogeneous Media, 2022, 17(6): 863-892. doi: 10.3934/nhm.2022030
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In this paper we propose a multiscale traffic model, based on the family of Generic Second Order Models, which integrates multiple trajectory data into the velocity function. This combination of a second order macroscopic model with microscopic information allows us to reproduce significant variations in speed and acceleration that strongly influence traffic emissions. We obtain accurate approximations even with a few trajectory data. The proposed approach is therefore a computationally efficient and highly accurate tool for calculating macroscopic traffic quantities and estimating emissions.
Fractional differential equations (FDEs) appeared as an excellent mathematical tool for, modeling of many physical phenomena appearing in various branches of science and engineering, such as viscoelasticity, statistical mechanics, dynamics of particles, etc. Fractional calculus is a recently developing work in mathematics which studies derivatives and integrals of functions of fractional order [26].
The most used fractional derivatives are the Riemann-Liouville (RL) and Caputo derivatives. These derivatives contain a non-singular derivatives but still conserves the most important peculiarity of the fractional operators [1,2,10,11,23,24]. Atangana and Baleanu described a derivative with a generalized Mittag-leffler (ML) function. This derivative is often called the Atangana-Baleanu (AB) fractional derivative. The AB-derivative in the senses of Riemman-Liouville and Caputo are denoted by ABR-derivative and ABC-derivative, respectively.
The AB fractional derivative is a nonlocal fractional derivative with nonsingular kernel which is connected with various applications [3,5,6,8,9,13,14,15,16]. Using the advantage of the non-singular ML kernal present in the AB fractional derivatives, operators, many authors from various branches of applied mathematics have developed and studied mathematical models involving AB fractional derivatives [18,22,29,30,31,32,35,36,37].
Mohamed et al. [25] considered a system of multi-derivatives for Caputo FDEs with an initial value problem, examined the existence and uniqueness results and obtained numerical results. Sutar et al. [32,33] considered multi-derivative FDEs involving the ABR derivative and examined existence, uniqueness and dependence results. Kucche et al. [12,19,20,21,34] enlarged the work of multi-derivative fractional differential equations involving the Caputo fractional derivative and studied the existence, uniqueness and continuous dependence of the solution.
Inspired by the preceding work, we perceive the multi-derivative nonlinear neutral fractional integro-differential equation with AB fractional derivative of the Riemann-Liouville sense of the problem:
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),∫ȷ0K(ȷ,θ,V(θ))dθ,∫T0χ(ȷ,θ,V(θ))dθ),ȷ∈I | (1.1) |
V(0)=V0∈R, | (1.2) |
where ⋆0Dδȷ denotes the ABR fractional derivative of order δ∈(0,1), and φ∈C(I×R×R×R,R) is a non-linear function. Let P1V(ȷ)=∫ȷ0K(ȷ,θ,V(θ))dθ and P2V(ȷ)=∫T0χ(ȷ,θ,V(θ))dθ. Now, (1.1) becomes,
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I, | (1.3) |
V(0)=V0∈R. | (1.4) |
In this work, we derive a few supplemental results using the characteristics of the fractional integral operator εαδ,η,V;c+. The existence results are obtained by Krasnoselskii's fixed point theorem and the uniqueness and data dependence results are obtained by the Gronwall-Bellman inequality.
Definition 2.1. [14] The Sobolev space Hq(X) is defined as Hq(X)={φ∈L2(X):Dβφ∈L2(X),∀|β|≤q}. Let q∈[1,∞) and X be open, X⊂R.
Definition 2.2. [11,17] The generalized ML function Eαδ,β(u) for complex δ,β,α with Re(δ)>0 is defined by
Eαδ,β(u)=∞∑t=0(α)tα(δt+β)utt!, |
and the Pochhammer symbol is (α)t, where (α)0=1,(α)t=α(α+1)...(α+t−1), t=1,2...., and E1δ,β(u)=Eδ,β(u),E1δ,1(u)=Eδ(u).
Definition 2.3. [4] The ABR fractional derivative of V of order δ is
⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=B(δ)1−δddȷ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ]V(θ)dθ, |
where V∈H1(0,1), δ∈(0,1), B(δ)>0. Here, Eδ is a one parameter ML function, which shows B(0)=B(1)=1.
Definition 2.4. [4] The ABC fractional derivative of V of order δ is
⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ]V′(θ)dθ, |
where V∈H1(0,1), δ∈(0,1), and B(δ)>0. Here, Eδ is a one parameter ML function, which shows B(0)=B(1)=1.
Lemma 2.5. [4] If L{g(ȷ);b}=ˉG(b), then L{⋆0Dδȷg(ȷ);b}=B(δ)1−δbδˉG(b)bδ+δ1−δ.
Lemma 2.6. [26] L[ȷmδ+β−1E(m)δ,β(±aȷδ);b]=m!bδ−β(bδ±a)m+1,Em(ȷ)=dmdȷmE(ȷ).
Definition 2.7. [17,27] The operator εαδ,η,V;c+ on class L(m,n) is
(εαδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))]=∫t0(ȷ−θ)α−1Eαδ,η[V(ȷ−θ)δ]Θ(θ)dθ,ȷ∈[c,d], |
where δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.8. [17,27] The operator εαδ,η,V;c+ is bounded on C[m,n], such that ‖(εαδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))]‖≤P‖Θ‖, where
P=(n−m)Re(η)∞∑t=0|(α)t||α(δt+η)|[Re(δ)t+Re(η)]|V(n−m)Re(δ)|tt!. |
Here, δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.9. [17,27] The operator εαδ,η,V;c+ is invertible in the space L(m,n) and φ∈L(m,n) its left inversion is given by
([εαδ,η,V;c+]−1)[V(ȷ)−x(ȷ,y(ȷ))]=(Dη+ςc+ε−αδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n], |
where δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.10. [17,27] Let δ,η,V,α∈C(Re(δ),Re(η)>0),n>m and suppose that the integral equation is
∫ȷ0(ȷ−θ)α−1Eαδ,η[V(ȷ−θ)δ]Θ(θ)dθ=φ(ȷ),ȷ∈(m,n], |
is solvable in the space L(m,n).Then, its unique solution Θ(ȷ) is given by
Θ(ȷ)=(Dη+ςc+ε−αδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n]. |
Lemma 2.11. [7] (Krasnoselskii's fixed point theorem) Let A be a Banach space and X be bounded, closed, convex subset of A. Let F1,F2 be maps of S into A such that F1V+F2φ∈X ∀ V,φ∈U. The equation F1V+F2V=V has a solution on S, and F1, F2 is a contraction and completely continuous.
Lemma 2.12. [28] (Gronwall-Bellman inequality) Let V and φ be continuous and non-negative functions defined on I. Let V(ȷ)≤A+∫ȷaφ(θ)V(θ)dθ,ȷ∈I; here, A is a non-negative constant.
V(ȷ)≤Aexp(∫ȷaφ(θ)dθ),ȷ∈I. |
In this part, we need some fixed-point-techniques-based hypotheses for the results:
(H1) Let V∈C[0,T], function φ∈(C[0,T]×R×R×R,R) is a continuous function, and there exist +ve constants ζ1,ζ2 and ζ. ‖φ(ȷ,V1,V2,V3)−φ(ȷ,φ1,φ2,φ3)‖≤ζ1(‖V1−φ1‖+‖V2−φ2‖+‖V3−φ3‖) for all V1,V2,V3,φ1,φ2,φ3 in Y, ζ2=maxV∈R‖f(ȷ,0,0,0)‖, and ζ=max{ζ1,ζ2}.
(H2) P1 is a continuous function, and there exist +ve constants C1,C2 and C. ‖P1(ȷ,θ,V1)−P1(ȷ,θ,φ1)‖≤C1(‖V1−φ1‖)∀V1,φ1 in Y, C2=max(ȷ,θ)∈D‖P1(ȷ,θ,0)‖, and C=max{C1,C2}.
(H3) P2 is a continuous function and there are +ve constants D1,D2 and D. ‖P2(ȷ,θ,V1)−P2(ȷ,θ,φ1)‖≤D1(‖V1−φ1‖) for all V1,φ1 in Y, D2=max(ȷ,θ)∈D‖P2(ȷ,θ,0)‖ and D=max{D1,D2}.
(H4) Let x∈c[0,I], function u∈(c[0,I]×R,R) is a continuous function, and there is a +ve constant k>0, such that ‖u(ȷ,x)−u(ȷ,y)‖≤k‖x−y‖. Let Y=C[R,X] be the set of continuous functions on R with values in the Banach space X.
Lemma 2.13. If (H2) and (H3) are satisfied the following estimates, ‖P1V(ȷ)‖≤ȷ(C1‖V‖+C2),‖P1V(ȷ)−P1φ(ȷ)‖≤Cȷ‖V−φ‖, and ‖P2V(ȷ)‖≤ȷ(D1‖V‖+D2),‖P2V(ȷ)−P2φ(ȷ)‖≤Dȷ‖V−φ‖.
Theorem 3.1. The function φ∈C(I×R×R×R,R) and V∈C(I) is a solution for the problem of Eqs (1.3) and (1.4), iff V is a solution of the fractional equation
V(ȷ)=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I. | (3.1) |
Proof. (1) By using Definition 2.3 and Eq (1.3), we get
ddȷ(V(ȷ)+B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ)=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)). |
Integrating both sides of the above equation with limits 0 to ȷ, we get
V(ȷ)+B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ−V(0)=∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I. |
Conversely, with differentiation on both sides of Eq (3.1) with respect to ȷ, we get
dVdȷ+B(δ)1−δddȷ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I. |
Using Definition 2.3, we get Eq (1.3) and substitute ȷ=0 in Eq (3.1), we get Eq (1.4).
Proof. (2) In Equation (1.3), taking the Laplace Transform on both sides, we get
L[V′(ȷ);b]+L[⋆0Dδȷ;b][V(ȷ)−x(ȷ,y(ȷ))]=L[φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ));b]. |
Now, using the Laplace Transform formula for the AB fractional derivative of the RL sense, as given in Lemma 2.5, we get
bˉX(b)−[V(ȷ)−x(ȷ,y(ȷ))]−V(0)+B(δ)1−δbδˉX(b)bδ+δ1−δ=ˉG(b), |
ˉX(b)=[V(ȷ);b] and ˉG(b)=L[φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ));b]. Using Eq (1.4), we get
ˉX(b)=V01b−B(δ)1−δbδ−1ˉX(b)bδ+δ1−δ[V(ȷ)−x(ȷ,y(ȷ))]+1bˉG(b). | (3.2) |
In Eq (3.2) applying the inverse Laplace Transform on both sides using Lemma 2.6 and the convolution theorem, we get
L−1[ˉX(b);ȷ]=V0L−1[1b;ȷ]−B(δ)1−δ(L−1[bδ−1bδ+δ1−δ][V(ȷ)−x(ȷ,y(ȷ))]∗L−1[ˉX(b);ȷ])+L−1[ˉG(b);ȷ]∗L−1[1b;ȷ]=V0−B(δ)1−δ(Eδ[−δ1−δȷδ][V(ȷ)−x(ȷ,y(ȷ))])+φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ))=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ.V(ȷ)=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ. | (3.3) |
Theorem 3.2. Let δ∈(0,1). Define the operator F on C(I):
(FV)(ȷ)=V0−B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],V∈C(I). | (3.4) |
(A) F is a bounded linear operator on C(I).
(B) F satisfying the hypotheses.
(C) F(X) is equicontinuous, and X is a bounded subset of C(I).
(D) F is invertible, function φ∈C(I), and the operator equation FV=φ has a unique solution in C(I).
Proof. (A) From Definition 2.7 and Lemma 2.8, the fractional integral operator ε1δ,1,−δ1−δ;0+ is a bounded linear operator on C(I), such that
‖ε1δ,1,−δ1−δ;0+‖‖[V(ȷ)−x(ȷ,y(ȷ))]‖≤P‖V‖,ȷ∈I,where |
P=T∞∑n=0(1)nα(δn+1)(δn+1)|−δ1−δTδ|nn!=T∞∑n=0(δ1−δ)nTδnα(δn+2)=TEδ,2(δ1−δTδ), |
and we have
‖FV‖=|B(δ)1−δ|‖ε1δ,1,−δ1−δ;0+‖‖[V(ȷ)−x(ȷ,y(ȷ))]‖≤PB(δ)1−δ‖V‖,∀V∈C(I). | (3.5) |
Thus, FV=φ is a bounded linear operator on C(I).
(B) We consider V,φ∈C(I). By using linear operator F and bounded operator ε1δ,1,−δ1−δ;0+, for any ȷ∈I,
|(FV)(ȷ)−(Fφ)(ȷ)|=|F(V−φ)[V(ȷ)−x(ȷ,y(ȷ))]|≤B(δ)1−δ‖(ε1δ,1,−δ1−δ;0+V−φ)[V(ȷ)−x(ȷ,y(ȷ))]‖≤PB(δ)1−δ‖V−φ‖. |
Where, P=TEδ,2(δ1−δTδ), then the operator F is satisfied the hypotheses with constant PB(δ)1−δ.
(C) Let U={V∈C(I):‖V‖≤R} be a bounded and closed subset of C(I), V∈U, and ȷ1,ȷ2∈I with ȷ1≤ȷ2.
|(FV)(ȷ1)−(FV)(ȷ2)|=|B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ1)−u(l1,x(l))]−B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ2)−u(l2,x(l))]|≤B(δ)1−δ|∫ȷ10{Eδ[−δ1−δ(ȷ1−θ)δ]−Eδ[−δ1−δ(ȷ2−θ)δ]}[V(ȷ)−x(ȷ,y(ȷ))]dθ|+B(δ)1−δ|∫ȷ2ȷ1Eδ[−δ1−δ(ȷ2−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ|≤B(δ)1−δ∞∑n=0|(−δ1−δ)n|1α(nδ+1)∫ȷ10|(ȷ1−θ)nδ−(ȷ2−θ)nδ||[V(ȷ)−x(ȷ,y(ȷ))]|dθ+B(δ)1−δ∞∑n=0|(−δ1−δ)n|1α(nδ+1)∫ȷ2ȷ1|(ȷ2−θ)nδ||[V(ȷ)−x(ȷ,y(ȷ))]|dθ≤LB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1)∫ȷ10(ȷ2−θ)nδ−(ȷ1−θ)nδdθ+LB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1)∫ȷ2ȷ1(ȷ2−θ)nδdθ≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1){−(ȷ2−ȷ1)nδ+1+ȷnδ+12−ȷnδ+11+(ȷ2−ȷ1)nδ+1}≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+2){ȷnδ+12−ȷnδ+11}|(FV)(ȷ1)−(FV)(ȷ2)|≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+2){ȷnδ+12−ȷnδ+11}. | (3.6) |
Hence, if |ȷ1−ȷ2|→0 then |(FV)(ȷ1)−(FV)(ȷ2)|→0.
∴ is equicontinuous on
(D) By Lemmas 2.9 and 2.10, , and we get
(3.7) |
By Eqs (3.4) and (3.5), we have
where with . This shows is invertible on and
has the unique solution,
(3.8) |
Theorem 4.1. Let . Then, the ABR derivative , is solvable in ), and the solution in is
(4.1) |
where , and .
Proof. The corresponding fractional equation of the ABR derivative
is given by
Using operator of Eq (3.4), we get
(4.2) |
Equations (3.7) and (4.2) are solvable, and we get
(4.3) |
Theorem 4.2. Let satisfy – with where , if . Then problem of (1.3) and (1.4) has a solution in provided
(4.4) |
Proof. Define
where Let . Consider and given as
Let is the fractional Eq (3.1) to the problems (1.3) and (1.4).
Hence, the operators and satisfy the Krasnoselskii's fixed point theorem.
Step (ⅰ) is a contraction.
By – on , and ,
(4.5) |
This gives,
Step (ⅱ) is completely continuous. By using Theorem 3.3 and Ascoli-Arzela theorem, is completely continuous.
Step (ⅲ) , for any , using Theorem 3.3, we obtain
(4.6) |
By definition of , we get
(4.7) |
Using the Eq (4.5) in (4.7), we get condition of Eq (4.4).
(4.8) |
This gives, ,
From Steps (ⅰ)–(ⅲ), all the conditions of Lemma 2.11 follow.
Theorem 4.3. By Theorem 4.2, the Eqs (1.3) and (1.4) have a unique solution in
Proof. (1) The problems (1.3) and (1.4) have an operator equation form as:
(4.9) |
where,
By Theorem 4.2, Eq (4.7) is solvable in , by Lemma 2.10 we get a unique solution of Eqs (1.3) and (1.4),
Proof. (2) Let be solutions of Eqs (1.3) and (1.4). By fractional integral operators and – we find, for any ,
(4.10) |
Theorem 5.1. By Theorem 4.2, if is a solution of Eqs (1.3) and (1.4), then
(5.1) |
where,
Proof. If is a solution of Eqs (1.3) and (1.4), for all
By Lemma 2.12, we get
(5.2) |
We discuss data dependence results for the problem
(6.1) |
(6.2) |
Theorem 6.1. Equation (4.2) holds, and where are real numbers such that,
(6.3) |
is a solution of ABR fractional derivative Eqs (6.1) and (6.2), and is a solution of Eqs (1.3) and (1.4).
Proof. Let are the solution of Eqs (1.3) and (1.4), (6.1) and (6.2) respectively. We find for any
By Lemma 2.12, we get
(6.4) |
Let any and
(7.1) |
(7.2) |
(7.3) |
(7.4) |
Theorem 7.1. Let the function satisfy Theorem 4.2. Suppose there exists such that,
If are the solutions of Eqs (7.1) and (7.3), then
(7.5) |
where
Proof. Let, for any ,
By Lemma 2.12,
(7.6) |
Consider a nonlinear ABR fractional derivative with neutral integro-differential equations of the form:
(8.1) |
(8.2) |
is a continuous nonlinear function such that,
and
We observe that for all and
(8.3) |
The function satisfies – with constant . From Theorem 4.2, we have and T = 2 which is substitute in Eq (4.2), and we get
(8.4) |
If the function satisfies Eq (8.4), then Eqs (8.1) and (8.2) have a unique solution.
(8.5) |
In this research article, we explored multi-derivative nonlinear neutral fractional integro-differential equations involving the ABR fractional derivative. The elementary results of the existence, uniqueness and dependence solution on various data are based on the Prabhakar fractional integral operator involving a generalized ML function. The existence results are obtained by Krasnoselskii's fixed point theorem, and the uniqueness and data dependence results are obtained by the Gronwall-Bellman inequality with continuous functions.
The research on Existence and data dependence results for neutral fractional order integro-differential equations by Khon Kaen University has received funding support from the National Science, Research and Innovation Fund.
The authors declare no conflict of interest.
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1. | Andreas Frommer, Daniel B. Szyld, On the convergence of randomized and greedy relaxation schemes for solving nonsingular linear systems of equations, 2023, 92, 1017-1398, 639, 10.1007/s11075-022-01431-7 | |
2. | Yansheng Su, Deren Han, Yun Zeng, Jiaxin Xie, On greedy multi-step inertial randomized Kaczmarz method for solving linear systems, 2024, 61, 0008-0624, 10.1007/s10092-024-00621-0 |
A sample of a GPS trajectory dataset (left) and of flux data coming from a fixed sensor (right). The data was provided by Autovie Venete S.p.A and are not publicly available
Left: flux function
Comparison between model (1) with velocity function
Left: Trajectories generated by the second order microscopic model used in [12]. Right: Evolution in space and time of the macroscopic density
Evolution of the density
Left: A sample of the trajectories drawn in Figure 4-left. Right: Evolution in space and time of the density
Comparison between the microscopic E-max-formula and the E-exp-formula (see Section 5.2)
Effects of monitored slow vehicles on the second order model (15), see Section 5.1
Vehicle trajectories (25) on a stretch of the road
Section 5.2 tests. Density profile at
Section 5.3 test. Sketch of the highway network, where the roads are numbered from 1 to 6, the triangles represent the fixed sensors, the diverge junctions are represented by points D1, D2, D3 and the merge ones by points M1, M2, M3
Section 5.3 test. Example of real trajectory data recorded on 27/08/2021. The size of the space-time circles is proportional to vehicles velocity. The data were provided by Autovie Venete S.p.A and are not publicly available
Section 5.3 test. Variation in time of the flux per minute of heavy vehicles recorded by the three sensors on 27/08/2021. The data were provided by Autovie Venete S.p.A and are not publicly available
Section 5.3 test. Density of vehicles at different times of the simulation
Section 5.3 test. Total emissions on road 2 (left) and road 3 (right)
Section 5.3 test. Density, speed, acceleration and
Section 5.3 test. Domain
Section 5.3 test. Source term of