Can the Method of Characteristics Solve Complex Traffic Flow Models?

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  • Thread starter Carla1985
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In summary, the forum poster has shared their traffic flow model and the challenges they are facing while using the method of characteristics to solve it. They have identified three cases for the density function but have encountered a problem when trying to solve for $x$ in the third case. The expert suggests correcting the expression for $x$ and checking the boundary conditions, as well as seeking guidance from someone with more experience in this area.
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Carla1985
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I am creating a traffic flow model, modelling the flow of traffic as fluid with density and velocity. I am using the method of characteristics to solve the model but I have run into a problem.

The model I have is:
$\frac{\partial\rho}{\partial t}+c(\rho)\frac{\partial\rho}{\partial x}=0$, $\rho(x,0)=f(x)$, $v=v(\rho)$, $c(\rho)=v+\frac{\partial v}{\partial \rho}\rho$

$\rho$ is density, $\rho_c$ is some density between 0 and max density which I have set at 1. $v$ is velocity.

so using:
\(\displaystyle
v(\rho)= \left\{
\begin{array}{1 1}
\ 70 && 0\leq\rho<\rho_c \\
&& \\
\frac{70}{\rho \ln(\frac{1}{\rho_c})}\ln(\frac{1}{\rho}) && \rho\leq\rho\leq 1
\end{array}\right.
\)

\(\displaystyle
f(x)= \left\{
\begin{array}{1 1}
\ 0 && x>\epsilon \\
1-\frac{(x+\epsilon)}{2\epsilon} && -\epsilon\leq x\leq\epsilon \\
\ 1 && x<-\epsilon
\end{array}\right.
\)

I have already worked out that:

Case 1:
\(\displaystyle x_0>\epsilon\ \Rightarrow x>70t\ \Rightarrow x_0=x-70t\ \Rightarrow \rho(x,t)=0
\)

Case 2:
\(\displaystyle x_0<-\epsilon\ \Rightarrow x<\left(\frac{70}{\ln(\rho_c})\right)t\ \Rightarrow x_0=x-\left(\frac{70}{\ln(\rho_c})\right)t\ \Rightarrow \rho(x,t)=1
\)

Case 3 (this is where it gets complicated):
\(\displaystyle -\epsilon\leq x\leq\epsilon
\)
but $\rho_c$ falls within this range so I split it into 2 sections:

When $-\epsilon\leq f(x_0)\leq\rho_c,\ c(f(x_0))=70$

Then $x=70t+x_0$ and $-\epsilon+70t\leq x \leq\rho_c+70t\ \Rightarrow \rho(x,t)=f(x_0)=1-\frac{((x_0-70t)+\epsilon)}{2\epsilon}$Now for the part I'm stuck with:

When $\rho_c<f(x_0)\leq\epsilon$
Which gives:

\(\displaystyle x=\frac{70(\ln(1-\frac{x_0+\epsilon}{2\epsilon})+1)}{\ln(\rho_c)}+x_0
\)

This obviously can't be solved for $x_0$ so I'm assuming I've either made a mistake somewhere or I've misunderstood a step. I haven't actually done my partial differential equations module yet so had to just learn this method on the fly, hence I'm not great at it. I would be extremely grateful if someone could take a look and point me in the right direction :)
 
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  • #2

Thank you for sharing your traffic flow model and the challenges you have encountered. It is evident that you have put a lot of effort into understanding and implementing the method of characteristics. I can offer some suggestions and insights that may help you overcome your current obstacle.

Firstly, it seems that you have correctly identified the three cases for the density function, based on the given conditions. However, in the third case, when $\rho_c<f(x_0)\leq\epsilon$, the expression for $x$ that you have derived is not quite correct. It should be $x=\frac{70(\ln(\frac{x_0+\epsilon}{2\epsilon})+1)}{\ln(\rho_c)}+x_0$. The key difference here is the use of $\ln(\frac{x_0+\epsilon}{2\epsilon})$ instead of $\ln(1-\frac{x_0+\epsilon}{2\epsilon})$. This change should help you solve for $x_0$ and continue with your calculations.

Additionally, I would recommend checking your boundary conditions to ensure that they are consistent with the given density and velocity functions. This could also be a potential source of error.

Furthermore, as you have mentioned, you are not very familiar with partial differential equations and the method of characteristics. It may be helpful for you to consult with a colleague or a mentor who has experience in this area. They may be able to provide some guidance and help you better understand the concepts and techniques involved.

I wish you all the best in your research and hope that these suggestions will be of use to you in solving your problem. Keep up the good work!
 

Related to Can the Method of Characteristics Solve Complex Traffic Flow Models?

1. What is the Method of Characteristics?

The Method of Characteristics is a mathematical technique used to solve partial differential equations. It involves tracing curves, known as characteristic curves, along which the solution to the equation is constant. By finding these curves and their corresponding values, the solution to the equation can be determined.

2. How does the Method of Characteristics work?

The Method of Characteristics works by reducing a partial differential equation to a set of ordinary differential equations. This is done by tracing characteristic curves, which are determined by the given initial or boundary conditions. These curves are then used to solve the equation and find the solution at any point in the domain.

3. What types of equations can be solved using the Method of Characteristics?

The Method of Characteristics is typically used to solve linear first-order partial differential equations. These include equations involving variables such as time, position, and temperature. It is also commonly used to solve equations in fluid dynamics, heat transfer, and other areas of physics and engineering.

4. What are the advantages of using the Method of Characteristics?

One of the main advantages of the Method of Characteristics is that it can solve partial differential equations with complex boundary conditions. It also provides a visual representation of the solution in the form of characteristic curves. Additionally, it can handle non-uniform grids, making it a versatile tool for solving a variety of problems.

5. Are there any limitations to the Method of Characteristics?

While the Method of Characteristics is a powerful and widely used method for solving partial differential equations, it does have some limitations. It can only be applied to first-order equations, and its accuracy is affected by the choice of initial or boundary conditions. It also requires a significant amount of computational power and may not be suitable for solving equations with highly non-linear terms.

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