Decentralized Autonomous Traffic Management through Corridor Networks

• URL: http://arxiv.org/abs/2606.23585v1
• Date: Mon, 22 Jun 2026 16:49:53 GMT
• Title: Decentralized Autonomous Traffic Management through Corridor Networks
• Abstract summary: Dedicated Advanced Air Mobility (AAM) corridors have been proposed for organizing high-density autonomous traffic flows.<n>We extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management.
• Score: 3.178189994883855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomous traffic flows. The desire to scalably provide autonomous aircraft flexibility in trajectory planning motivates the development of decentralized approaches to traffic management in AAM corridors. In this work, we extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management in air corridor networks. We test policies trained in a single-corridor setting on increasingly complex multi-corridor networks with combinations of merges and splits in a zero-shot manner. Experimental results demonstrate that learned behaviors transfer well to scenarios with varying traffic density, network geometry, and heterogeneous vehicle performance, without needing centralized coordination or model retraining. We evaluate system-level performance in terms of conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation. We find that although our policies require only locally coordinated entry, traversal, and exit behaviors, they collectively produce desirable traffic flows through the corridor network.

Related papers

• Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors [3.5719670523509954]
  We show that it is possible for autonomous aircraft to learn to self-organize into corridor flows in decentralized settings.<n>We find that in decentralized settings with only local information, the aircraft are able to conform to the corridor boundaries more than 94% of the time.
  arXiv  Detail & Related papers  (2026-06-22T18:15:55Z)
• CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control [15.468107943726617]
  CoordLight is a framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents)<n>We propose an advanced MARL algorithm, named Neighbor-aware Policy Optimization (NAPO), which integrates an attention mechanism that discerns the state and action dependencies among adjacent agents.<n>We empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows.
  arXiv  Detail & Related papers  (2026-03-25T14:46:31Z)
• Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach [5.2437780355984165]
  We propose MA2B-DDQN, a human-centric multi-agent action-branching double Deep Q-Network (DQN) framework.<n>Our key contribution is an action-branching discrete control formulation that decomposes corridor control into local, per-intersection actions.<n>We also design a human-centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers.
  arXiv  Detail & Related papers  (2026-02-03T00:56:03Z)
• Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing [1.4273866043218153]
  We address dynamic vehicle routing through a multi-agent reinforcement learning (MARL) framework for coordinated, network-aware fleet navigation.<n>We first propose Adaptive Navigation (AN), a decentralized MARL model where each intersection agent provides routing guidance based on local traffic and neighborhood state.<n>To improve scalability in large networks, we further propose Hierarchical Hub-based Adaptive Navigation (HHAN), an extension of AN that assigns agents only to key intersections (hubs)<n> Experiments on synthetic grids and real urban maps (Toronto, Manhattan) show that AN reduces average travel time versus SPF and learning baselines, maintaining 100% routing success.
  arXiv  Detail & Related papers  (2025-10-30T02:49:46Z)
• An LLM-Powered Cooperative Framework for Large-Scale Multi-Vehicle Navigation [10.549493962440804]
  Multi-vehicle dynamic navigation requires simultaneously routing large fleets under evolving traffic conditions.<n>Existing path search algorithms and reinforcement learning methods struggle to scale to city-wide networks.<n>We propose CityNav, a hierarchical, LLM-powered framework for large-scale multi-vehicle navigation.
  arXiv  Detail & Related papers  (2025-10-09T06:14:29Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving [17.659812774579756]
  We propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability.
  arXiv  Detail & Related papers  (2024-03-18T16:13:02Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning [57.24340061741223]
  We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
  arXiv  Detail & Related papers  (2023-06-09T20:12:02Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World [49.80905654161763]
  The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP) The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur. The ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible.
  arXiv  Detail & Related papers  (2021-03-30T17:13:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.