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Dynamic Traffic Assignment for Electric Vehicles

Lukas Graf, Tobias Harks, Prashant Palkar
Augsburg University





ATMOS 2022 in Potsdam

I. Model

Base Model

  • directed graph $G=(V,E)$ with edge travel times $\tau_e \in \IR_{>0}$ and capacities $\nu_e \in \IR_{>0}$
  • commodities $i \in I$ with source and sink nodes $s_i, t_i \in V$ and bounded network inflow rate $u_i: [0,T] \to \IR_{\geq 0}$
  • route choices of all (infinitesimally small) agents: \[(h_{i,P})_{i,P} \in \left(L^2_+([0,T])\right)^{\sum_{i \in I}\Pc_i}\] where $\Pc_i$ is the set of all (simple) $s_i$,$t_i$-paths.
  • path inflows $h$ $\mapsto$ edge flow $(f_e)_e$ $\mapsto$ path arrival times $\mu_{i,P}$
          (here: Vickrey queuing model)
  • For almost all times $\theta \in [0,T]$: \[h_{i,P}(\theta)>0 \implies \mu_{i,P}(\theta) \leq \mu_{i,P'}(\theta) \text{ f.a. } P' \in \Pc_i\] Then $h$ is a Dynamic Equilibrium
Dynamic Equilibria are guaranteed to exist.

by [Han, Friesz, Yao 2013], [Cominetti, Correa, Larré, 2015].

+

Electric Vehicles

  • battery consumption $b_{i,e} \in \IR_{\geq 0}$
  • initial battery state $b_i \in \IR_{>0}$ and batt. capacity $b_{i,\max} \in \IR_{>0}$
  • recharging stations with different recharging modes
    (duration, amount, price, capacity)

 

  • ...and battery consumption $b_{i,e} \in \IR$ and prices $p_{i,e} \in \IR_{\geq 0}$
     
  • ...and initial batt. state $b_i \in \IR_{>0}$, batt. capacity $b_{i,\max} \in \IR_{>0}$ and function converting travel time + price to costs $c_{i,W}$
  • route choices of all (infinitesimal small) agents: \[(h_{i,W})_{i,W} \in \left(L^2_+([0,T])\right)^{\sum_{i \in I}\Wc_i}\] where $\Wc_i$ is the set of all (!) energy-feasible $s_i$,$t_i$-walks.
  • walk inflows $h$ $\mapsto$ edge flow $(f_e)_e$ $\mapsto$ walk arrival times $\mu_{i,W}$
    walk inflows $h$ $\mapsto$ edge flow $(f_e)_e$ $\mapsto$ walk costs $c_{i,W}$
  • For almost all times $\theta \in [0,T]$: \[h_{i,W}(\theta)>0 \implies c_{i,W}(\theta) \leq c_{i,W'}(\theta) \text{ f.a. } W' \in \Wc_i\] Then $h$ is an Energy-Feasible Dynamic Equilibrium



Do Energy-Feasible Dynamic Equilibria always exist?

II. Existence

Use a variational inequality (similiar to [Han, Friesz, Yao 2013] and [Cominetti, Correa, Larré, 2015]):
Given
  • $K \subseteq \left(L^2([a,b])\right)^d$ non-empty, closed, convex and bounded
  • $K \subseteq \left(L^2([a,b])\right)^{\color{red}d}$ non-empty, closed, convex and bounded
  • $\A: K \to \left(L^2([a,b])\right)^d$ sequentially weak-strong continuous
then there exists some
$h^* \in K$ such that $\scalar{\A(h^*)}{h-h^*} \geq 0 \text{ for all } h \in K$. [Lions, 1969]
For any* multi-commodity network there exists an energy-feasible dynamic equilibrium
            (i.e. $h_{i,W}(\theta)>0 \implies c_{i,W}(\theta) \leq c_{i,W'}(\theta) \text{ f.a. } W' \in \Wc_i$).
  1. Find finite set $\Wc' \subseteq \Wc$ of dominating walks (f.a. $h \in K, (i,W) \in \Wc, \theta \geq 0$ there exists $(i,W') \in \Wc'$ s.th. $c_{i,W'}(\theta) \leq c_{i,W}(\theta)$)
  2. Define $K \coloneqq \Set{h \in \left(L^2([a,b])\right)^\Wc | \sum_{W \in \Wc_i}h_{i,W} = u_i \text{ for all } i \in I}$.
  3. Define $K \coloneqq \Set{h \in \left(L^2([a,b])\right)^{\color{red}{\Wc}} | \sum_{W \in \Wc_i}h_{i,W} = u_i \text{ for all } i \in I}$.
  4. Define $K \coloneqq \Set{h \in \left(L^2([a,b])\right)^{\color{red}{\Wc'}} | \sum_{W \in \Wc_i}h_{i,W} = u_i \text{ for all } i \in I}$.
  5. Define $K \coloneqq \Set{h \in \left(L^2([a,b])\right)^{\Wc'} | \sum_{W \in \Wc_i}h_{i,W} = u_i \text{ for all } i \in I}$.
  6. Define $\A(h)_{i,W}(\theta) \coloneqq c_{i,W}(\theta) - \min\Set{c_{i,W'}(\theta) | W' \in \Wc_i} \geq 0$.
    Then: $\scalar{\A(h^*)}{h-h^*} \geq 0 \text{ for all } h \in K \implies h^*$ is energy-feasible DE.
$\exists$ a finite set $\Wc' \subseteq \Wc$ of dominating walks.

III. Computation

A numerical algorithm for computing energy-feasible dynamic equilibria:
      (cf. [Han, Eve, Friesz 2019])

Input: A network $\mathcal{N} = (G,(\tau_e),(\nu_e),(b_{i,e}),(p_{i,e}),(u_i),(s_i),(t_i))$, $\varepsilon > 0$
Output: An (approximate) energy-feasible DE $f$ in $\mathcal{N}$

(1) Compute a finite dominating walk set $\Wc'$

(2) Choose any initial flow $h^0 \in C$, $k \leftarrow 0$

(3) Repeat

(4) .. Calculate network loading for $h^k$ and costs $c$

(5) .. Determine $h^{k+1}$ by shifting flow from expensive walks
(0) .. .. to cheaper walks

(7) Until $\frac{\norm{h^k-h^{k-1}}}{h^k} \leq \varepsilon$

$h^k = h^{k-1} \iff$ $h^k$ is an energy-feasible dynamic equilibrium
The Nguyen Network
The Nguyen Network

Effects of different recharging prices

Effects of different recharging capacities

IV. Conclusion

  • Classical model can be augmented with energy-consumption and recharging (incl. different modes/prices)
  • Existence proofs for dynamic equilibria carry over with some adjustments
  • In fact: The same is true for an even more general model with $h \in S$ for some convex restriction set $S \subseteq \left(L^2([a,b])\right)^\Wc$
  • We can (numerically) compute energy-feasible equilibria
  • However, runtime explodes with increased network size
  • No structural insights in equilibria (yet)
Dynamic Traffic Assignment for Electric Vehicles Lukas Graf
Dynamic Traffic Assignment for Electric Vehicles - I. Model Lukas Graf
Dynamic Traffic Assignment for Electric Vehicles - II. Existence Lukas Graf
Dynamic Traffic Assignment for Electric Vehicles - III. Computation Lukas Graf
Dynamic Traffic Assignment for Electric Vehicles - IV. Conclusion Lukas Graf
Dynamic Traffic Assignment for Electric Vehicles - V. Generalization Lukas Graf