Traffic control and management

Últimos artículos

• VLALight: A Vision-Language-Action Model for Traffic Signal Control

  Pan Zhang, Siqi Lai, Kemu Dong, Hao Liu · 30 de septiembre de 2026

  Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signalized intersections and provide rich visual observations of evolving traffic, existing TSC methods typically rely on manually engineered traffic state…

• VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control

  Kemou Jiang, Maonan Wang, Xingchen Zou, Jiayue Zhu, Yuhang Fu, Sicheng Wang, Xi Chen, Yirong Chen, Zhiyong Cui · 28 de septiembre de 2026

  Traffic signal control (TSC) is essential for mitigating urban congestion. Recent advances in vision-language models (VLMs) enable richer interpretation of intersection scenes, opening new opportunities for visual-context-aware TSC. However, the loose coupling and repeated information conversion bet…

• Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

  Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin · 21 de septiembre de 2026

  Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control. This paper presents a shared latent-space framework that connects simulator calibration and reinforcement learning control through a common learne…

• Driver Behavior Estimation at Signalized Intersections Using a Physics-Constrained Decision-Conditioned Autoregressive Transformer

  Mohammad Khoshkdahan, Pavel Laskov, Alexey Vinel · 16 de septiembre de 2026

  Red-light violations and harsh braking at signalized intersections are major contributors to traffic accidents. This paper analyzes and predicts human driver decision-making and longitudinal trajectory behavior during traffic light signal transitions. We collected a diverse real-world dataset compri…

• Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling

  Rx Fan, Zhan H · 9 de septiembre de 2026

  Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop generation must reconcile multiple decision time scales while its context evolves with generated states. Existing methods often unfold long futures fro…

• Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic

  Biao Yin, Abderrahmane Kasmi, Nadir Farhi · 28 de agosto de 2026

  Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control in real-world traffic is challenging due to uncertainty and heterogeneous driving behaviors. Reinforcement learning (RL) has strong potential for addre…

• Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning

  Madelaine Martinez-Ferguson, Chun Wang, Mustafa Can Camur, Xueping Li · 27 de agosto de 2026

  Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experimentation is expensive and existing approaches rely on predefined control policies that cannot capture adaptive behaviors. This paper presents an agent…

• Quantum-Inspired Modeling of Driving Behavior

  Mohammad Elayan, Omid Armantalab, Wissam Kontar · 27 de agosto de 2026

  Driver behavior is heterogeneous, context-dependent, and changes over time, and these properties shape the traffic phenomena we observe. Most models, however, fix in advance which behavioral variables interact and how. Behavior outside that form is absorbed as noise, while models flexible enough to …

• Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control

  Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli · 26 de agosto de 2026

  Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. Traditional practice relies on Eulerian variable speed limits (VSL) which regulate traffic through roadside signs, but suffer from infrequent updates and limite…

• SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive Control for Robust, Dependable Traffic Management

  Pratham Payra, Jagadish B, Tanmay Sen, Tanujit Chakraborty · 20 de agosto de 2026

  Traffic signal control is a complex sequential decision-making problem requiring real-time adaptation and trade-offs among throughput, delay fairness, signal stability, and emergency vehicle priority. Existing RL methods often fix objectives, ignore dynamic priority changes, and fail to generalize a…

• A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks

  Bertil Braun · 27 de julio de 2026

  We present a traffic-signal control interface in which a shared graph neural network assigns scores to individual traffic movements. Each junction converts these scores into its own variable-sized set of legal signal phases using a deterministic incidence matrix. Directed corridor nodes provide traf…

• Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority

  Philip-Roman Adam, Stefanie Schmidtner · 22 de julio de 2026

  Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles. Existing reinforcement-learning (RL) approaches to TSP typically encode transit-aware features (e.g., occup…

• Twisted Schr\"odinger Bridge Matching

  Maxence Noble, Marie Scheid, Yazid Janati, Eric Moulines, Alain Durmus · 21 de julio de 2026

  Over the past few years, diffusion-based Schr\"odinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with successful applications to generative modeling. More precisely, these methods aim to estimate a path measure whose ini…

• Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving

  Merlin Paul, Anup Aprem · 16 de julio de 2026

  Interactive driving, wherein an intelligent lead vehicle equipped with real-time traffic data coordinates route choices of connected vehicles, offers a promising approach to dynamic traffic management. To address the challenge of harmonising decisions, this paper considers the strategic information …

• Explainable Reinforcement Learning for Adaptive Traffic Signal Control

  Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter · 7 de julio de 2026

  Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traffic control, the opaque, black-box nature of deep RL models poses challenges for transportation agency acceptance, regulatory compliance, operationa…

• Autonomous discovery of traffic laws with AI traffic scientists

  Xingyuan Dai, Yue Liu, Xiaoyan Gong, Qinghai Miao, Junyou Shang, Yutong Wang, Chao Guo, Yonglin Tian, Yizhang Chai, Chao Xiang, Yisheng Lv, Fei-Yue Wang · 3 de julio de 2026

  Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management and control. Their discovery, however, remains expert-driven, requiring candidate regularities to be identified from het…

• DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning

  Qian Hu, Haoyang Peng, Songan Zhang, Ming Yang, Hongtei Eric Tseng · 1 de julio de 2026

  Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand. With the emergence of connected and automated vehicles (CAVs), trajectory-level coordination has emerged as a …

• OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections

  Mingyuan Li, Boyang Huang, Tianqi Jiang, Chenpu Li, Chunyu Liu, Yang Li, Ruimin Li, Qiang Wu · 29 de junio de 2026

  Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity, obstructing upstream traffic and triggering cascading gridlocks. Prevailing traffic signal control (TSC) algorithms, primarily optimized for throughput, often fail to address ov…

• Offline Reinforcement Learning for Warehouse SLAM Throughput Control

  Tina Dongxu Li, Mouhacine Benosman, Rajat Kumar, Kevin Tan, Ken Meszaros, Trevor Dardik · 24 de junio de 2026

  We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment. SLAM (Scan/Label/Apply/Manifest) throughput directly influences system congestion and operational efficiency. Our RL-based control approach dynamically recommend…

• Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections

  Yousef AlSaqabi · 23 de junio de 2026

  Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic signal systems are increasingly deployed as networked cyber-physical components within smart-city infrastructures, where distributed sensing and edge intelligenc…

• A Generative Model for Closed-Loop Microsimulation of Signalized Intersections

  Yash Ranjan, Rahul Sengupta, Anand Rangarajan, Sanjay Ranka · 23 de junio de 2026

  Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed …

• Platooning Connected, Autonomous, and Human-Driven Vehicles: A Deep Reinforcement Learning-based Approach

  Zhen Qina, Dong-Fan Xie, Heng Ma, Xiaomei Zhao, Zhengbing He · 23 de junio de 2026

  Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and connected human-driven vehicles. Non-connected vehicles, such as non-connected autonomous or human-driven vehicles, are not incorporated. As a result, the…

• ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning

  Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger, Werner Huber, Stefanie Schmidtner · 16 de junio de 2026

  Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry. We present ROSA-RL -- u…

• Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments

  D\'enes Toth, George Ambroladze, Edwin Sundberg, Ali Beikmohammadi, Alfreds Lapkovskis · 15 de junio de 2026

  Urban traffic signal control at IoT-instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand. Conventional controllers degrade under these conditions, and learned policies remain difficult to audit. To address these challenges, we propose…

• Transforming Police-Car Swerving for Mitigating Isolated Stop-and-Go Traffic Waves: A Practice-Oriented Jam-Absorption Driving Strategy

  Zhengbing He · 9 de junio de 2026

  Stop-and-go traffic waves, a major form of freeway congestion, impose severe and persistent adverse impacts, including reduced traffic efficiency, increased safety risks, and elevated vehicle emissions. Among various freeway traffic management strategies, jam-absorption driving (JAD), in which a ded…