Papers for
traffic engineers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Hybrid reinforcement learning improves ramp metering with limited connected vehicles
Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments
Abstract: Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) offers a promising solution for ramp metering, existing approaches rely primarily on aggregated macroscopic data. Connected vehicles (CVs) provide vehicle-level observations that can complement aggregate traffic measurements, but their limited penetration produces incomplete microscopic information. This paper proposes a hybrid observation representation combining macroscopic traffic measurements with a two-channel grid encoding observed CV presence and speed. A Dueling Double Deep Q-Network processes these inputs to select ramp-metering green durations. The controller is trained under varying traffic demands and CV penetration rates and evaluated against ALINEA and macroscopic-only DRL variants in SUMO. Across 50 matched evaluation scenarios, the hybrid controller under partial CV visibility reduces the reported total travel time by 11.4 % and mean spillback duration by 84.9 % relative to ALINEA. Evaluating the same trained policy with full CV visibility yields a further travel-time reduction of approximately 1.6 %. Analysis across penetration rates suggests that the performance gap decreases as microscopic observations become more complete. These results support the use of complementary macroscopic and sparse microscopic observations for learning-based ramp metering. The source code implementation of the model is available at: https://github.com/youcefMehamlia/Multimodal-DRL-RMC
Robust bounds on iterative updates improve game and traffic algorithms
A Robust Perceptron Cycling Theorem and Applications
Abstract: The classical perceptron cycling theorem of Block and Levin \cite{BlockLevin1970} bounds correction sequences whose selected updates come from a finite set and have nonpositive inner product with the current state. We prove a robust variant for additive trajectories $z_{k+1}=z_k+u_k$, for all integers $k\geq0$, with increments in a finite set $U\subset E$, where $E$ is a finite-dimensional real inner-product space and $0\in\conv U$. Let $A\colon E\to E$ have positive-definite symmetric part, without requiring symmetry, and let $B\geq0$. If each $u_k$ is a $B$-approximate minimizer of $u\mapsto\ip{Az_k}{u}$ over $U$, then $\sup_{k\geq0}\norm{z_k}\leq C(1+\norm{z_0}+B)$, with $C=C(E,U,A)$ independent of the initial state, $B$, and all admissible update choices. Our central algorithmic consequence is an $O(k^{-1})$ last-iterate norm bound for harmonic vertex-returning Frank--Wolfe for affine strongly monotone variational inequalities on polytopes with relatively interior solutions. It extends the quadratic Frank--Wolfe/herding guarantee of Bach, Lacoste-Julien, and Obozinski \cite[Section~4.2]{BachEtAl2012} to nonsymmetric affine operators. We apply this bound to empirical best responses in strongly stable linear population games, quadratically regularized bilinear saddle problems, and traffic assignment with coercive affine asymmetric cost maps.