Exact and grid-free solutions to the Lighthill–Whitham– Richards traffic flow model with bounded...

Exact and grid-free solutions to the Lighthill–Whitham–

Richards traffic flow model with bounded acceleration

Christian ClaudelAssistant professor, Civil, Architectural and Environmental EngineeringUniversity of Texas, Austin

OutlinePrimitive flow models based on LWR

- The LWR PDE- Integral formulation and physical interpretation

Fast solution methods for LWR- Lax-Hopf formula- Inf-morphism property- Applications

Hybrid LWR-bounded acceleration models- Definition- Grid-free computational methods

Conclusion

- First derived by Lighthill-Whitham (1955), and extended by Richards (1956)- First order scalar hyperbolic conservation law

- Based on the conservation of vehicles, and on the existence of a relationship between flow and density: q=ψ(ρ).

ψ(.) is assumed to be concave

[Newell 93], [Daganzo 03,06]

The LWR PDE

Flow

ψ(ρ

) (ve

h/h)

Density ρ (veh/mile)0 10050

1000

2000

Source: PeMS

Equivalently, we can define M(t,x) such that:

The function M(t,x) is called Moskowitz function. Its spatial derivative is the opposite of the density function; its temporal derivative is the flow function.

M(t,x) satisfies the following Hamilton-Jacobi PDE:


Integral formulation

M(t,x) can be interpreted as a vehicle label at location x and time t (assuming that no vehicles pass each other).

M(t,x) is also known as the cumulative vehicle number in the traffic flow community.


2 13456

vehicle label

position

65 4 3 2 1

Physical interpretation of the Moskowitz function





Conclusion

The Moskowitz formulation is particularly suited to Lagrangian problems: when no passing is allowed, vehicles keep their labels, which enables the definition of internal conditions (trajectory constraints that apply to the solution)

Defining internal conditions for the LWR PDE is possible, but not as easy.


Value conditions

Lax-Hopf formula

For a boundary data function c(.,.), the solution Mc(.,.) is given by:

where is the convex transform of ψ

[Lax 1973] [Aubin Bayen Saint Pierre SIAM SICON 2009] [Claudel Bayen IEEE TAC part II 2010]

Time

Position

c(.,.)

• Inf-morphism property Let us assume that the boundary data c is the minimum of a finite number of lower semicontinuous functions:

The solution associated with the above boundary data function can be decomposed as:

Inf-morphism

[Claudel Bayen IEEE TAC part II 2010] [Mazare Dehwah Claudel Bayen, TR-B 2012]

Time

Position

c12(.,.)

c1(.,.)

c2(.,.)

c3(.,.) c8(.,.)

c5(.,.) c6(.,.) c4(.,.)

c7(.,.)

c9(.,.)

c10(.,.)

c11(.,.)

Physical interpretation


• Inf-morphism property:

The solution associated with a set of boundary conditions is the minimum of the solutions associated with each boundary condition.

[Newell 93] [Daganzo 06] [Aubin Bayen Saint-Pierre 07]

• Inf-morphism property Let us assume that the boundary data c is the minimum of a finite number of lower semicontinuous functions:


Semi-analytic computational methods


Time

Position

c2(.,.)

c3(.,.) c8(.,.)

c4(.,.)

c10(.,.)

• Inf-morphism property The solution associated with the above boundary data function can be decomposed as:

If is linear, then can be explicitly computed (solution to a 1D convex optimization problem.



.,.jc .,.jc

M

Position

c12(.,.)

c1(.,.)

c2(.,.)

c3(.,.) c8(.,.)

c5(.,.) c6(.,.) c4(.,.)

c7(.,.)

c9(.,.)

c10(.,.)

c11(.,.)

Semi-analytic formulation

Most of the time, we consider piecewise constant initial and boundary conditions for the LWR PDE. This translates into piecewise affine initial and boundary conditions for the HJ PDE.

[Daganzo 2005 TR-B] [Daganzo 2006] [Mazare Dehwah Claudel Bayen 2012]




posi

tion

positiontime

time

Solutions to affine value conditions

The question becomes: can we quickly compute the solution associated with an affine (linear) initial or boundary condition?

To be able to use the inf morphism property in general, we need each affine initial or boundary condition to be defined on a subset of

Non physical/mathematical problem

[Daganzo 2005 TR-B] [Daganzo 2006] [Claudel Bayen TAC 2010]

Solutions to affine value conditions

Example of solution: affine initial condition

Lax-Hopf formula:

Objective function: convex (in u):

constraints also convex (intersection of intervals) : 1-D convex optimization.

[Claudel Bayen TAC 2010]

Solution structureSolution structure:

[Claudel Bayen TAC 2010]

Extension for solutions to the LWR PDE

The solutions to the LWR PDE (density) can also be computed semi analytically:

Godunov

Lax-Hopf

• Pros– Very fast (speed depends upon the problem, and number of points

used)– Exact derivation of both the density and Moskowitz function (for the

same cost), for any concave fundamental diagram, on any grid– Can integrate probe data

• Cons– Slower than the CTM if the density has to be computed everywhere


Faster algorithm for triangular fundamental diagram


• Many existing computational methods:• For LWR:

- Godunov scheme (or equivalently CTM)- Wave-front tracking- Other finite difference schemes (ENO, WENO)

• For HJ:- Lax Friedrichs schemes (or other numerical schemes)- Variational method (dynamic programming)- Semi-analytic method (for homogeneous problems),

which can be used for both HJ and LWR

[Daganzo06] [Claudel Bayen IEEE TAC part I & 2, 2010] [Mazare Dehwah Claudel Bayen, TR-B 2012]





Conclusion

Hybrid models

• We consider a hybrid model, in which vehicles either follow the LWR model or have a constant acceleration

• The velocity of the flow of vehicles is defined by

• Since N is not differentiable, v is not always defined

[Lebacque, 2002] [Leclercq, 2007] [Qiu Abdellatif Abdelaziz Claudel 2013]

Modified solution components

• Let us define “modified” solution components:

where


Modified solution components

• The modified solution components either satisfy the LWR model, or describe vehicles with constant acceleration

• They also satisfy an inf-property:


Analytical derivation of modified solution components• Since the solutions components Mci(.,.) are known

analytically, we can similarly compute the modified components analytically for initial, upstream, downstream and internal conditions (fixed or moving bottlenecks)

• Solution structure (initial condition)


Analytical derivation of modified solution components• Solution structure (upstream and downstream boundary

condition)


Analytical derivation of modified solution components

• Solution structure (internal condition)


• Example (I): solution to LWR-bounded acceleration model with a moving bottleneck

Applications

• Example (II): solution to LWR-bounded acceleration model with a fixed bottleneck (~1 s computational time on a desktop computer)

Applications

Conclusion

Fast and exact algorithm for computing solutions to the LWR

traffic flow model with bounded acceleration

Theory works only for specific fundamental diagrams (linear in congestion)

Computational cost is identical to the Lax-Hopf algorithm

Exact and grid-free solutions to the Lighthill–Whitham– Richards traffic flow model with bounded...

Documents

Transcript of Exact and grid-free solutions to the Lighthill–Whitham– Richards traffic flow model with bounded...