Traffic Operations and Management

Continual Learning of Microscopic Traffic Models Using Neural Networks

Farid, Yashar Zeinali
Kreidieh, Abdul Rahman
Khalighi, Farnoush
Lobel, Hans
Bayen, Alexandre M.
2021

In a mixed-autonomy traffic scenario, where human drivers and autonomous vehicles share the streets, self-driving cars need to be able to predict in a robust manner the behaviour of human-driven vehicles, in order to guarantee a safe and smooth driving experience. Although traffic theory provides several models of human drivers, these models are often parameterized by few parameters which can limit their performance in modeling complex behaviors. The lack of sufficient model capacity and the behavioral shifts in human driving reduces the usefulness of these methods in real-life...

Solving N-Player Dynamic Routing Games with Congestion: A Mean Field Approach

Cabannes, Theophile
Lauriere, Mathieu
Perolat, Julien
Marinier, Raphael
Girgin, Sertan
2021

The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows - applied in macroscopic and mesoscopic traffic modeling - the article introduces a new N-player dynamic routing game with explicit congestion dynamics. The model is well-posed and can reproduce heterogeneous departure times and congestion spill back phenomena. However, as Nash equilibrium computations are PPAD-complete, solving the game becomes intractable for large but realistic...

Inter-Level Cooperation in Hierarchical Reinforcement Learning

Kreidieh, Abdul Rahman
Berseth, Glen
Trabucco, Brandon
Parajuli, Samyak
Levine, Sergey
Bayen, Alexandre M.
2021

Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach an end-to-end training paradigm is needed. However, training these multi-level policies has had limited success due to challenges arising from interactions between the goal-assigning and goal-achieving levels within a hierarchy. In this article, we consider the policy optimization process as a multi-agent process. This allows us to draw on connections between communication and cooperation in multi-agent RL, and...

Parallel Network Flow Allocation in Repeated Routing Games via LQR Optimal Control

Gibson, Marsalis
You, Yiling
Bayen, Alexandre
2021

In this article, we study the repeated routing game problem on a parallel network with affine latency functions on each edge. We cast the game setup in a LQR control theoretic framework, leveraging the Rosenthal potential formulation. We use control techniques to analyze the convergence of the game dynamics with specific cases that lend themselves to optimal control. We design proper dynamics parameters so that the conservation of flow is guaranteed. We provide an algorithmic solution for the general optimal control setup using a multiparametric quadratic programming approach (explicit MPC...

Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic

Kreidieh, Abdul Rahman
Zhao, Yibo
Parajuli, Samyak
Bayen, Alexandre
2021

Reinforcement learning techniques can provide substantial insights into the desired behaviors of future autonomous driving systems. By optimizing for societal metrics of traffic such as increased throughput and reduced energy consumption, such methods can derive maneuvers that, if adopted by even a small portion of vehicles, may significantly improve the state of traffic for all vehicles involved. These methods, however, are hindered in practice by the difficulty of designing efficient and accurate models of traffic, as well as the challenges associated with optimizing for the behaviors of...

Boundary Control of Conservation Laws Exhibiting Shocks

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

This chapter focuses on control of systems of conservation laws with boundary data. Problems with one or two boundaries are considered and, in particular, we focus on cases where shocks may be developed by the solution. However, for completeness we briefly discuss in Sect. 2.2 other existing results where singularities are prevented via suitable feedback controls such as in [32].

Boundary Control of Conservation Laws Exhibiting Shocks

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

This chapter focuses on control of systems of conservation laws with boundary data. Problems with one or two boundaries are considered and, in particular, we focus on cases where shocks may be developed by the solution. However, for completeness we briefly discuss in Sect. 2.2 other existing results where singularities are prevented via suitable feedback controls such as in [32].

Control Problems for Hamilton-Jacobi Equations Co-authored by Alexander Keimer

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

In this chapter, we introduce Hamilton-Jacobi PDEs. These PDEs are related to conservation laws and their solutions are the anti-derivative (in space) of the Entropy solutions of the corresponding conservation law, given that some assumptions are satisfied.

Control Problems for Hamilton-Jacobi Equations Co-authored by Alexander Keimer

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

In this chapter, we introduce Hamilton-Jacobi PDEs. These PDEs are related to conservation laws and their solutions are the anti-derivative (in space) of the Entropy solutions of the corresponding conservation law, given that some assumptions are satisfied.