arXiv:2607. 22691v1 Announce Type: new Abstract: Urban traffic congestion significantly increases fuel consumption, greenhouse gas emissions, and commuter delays, resulting in substantial economic losses and environmental harm in modern cities.
arXiv:2606. 30694v1 Announce Type: cross Abstract: 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.
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.
arXiv:2605. 29693v2 Announce Type: replace Abstract: Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution.
arXiv:2606. 27381v1 Announce Type: cross Abstract: Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity, obstructing upstream traffic and triggering cascading gridlocks.
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
arXiv:2607. 21831v1 Announce Type: new Abstract: We present a traffic-signal control interface in which a shared graph neural network assigns scores to individual traffic movements.
arXiv:2607. 18286v1 Announce Type: cross Abstract: 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.
arXiv:2606. 13698v1 Announce Type: cross Abstract: Urban traffic signal control at IoT-instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand.
arXiv:2606. 16558v1 Announce Type: new Abstract: 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.
arXiv:2607. 16930v1 Announce Type: cross Abstract: Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration.
arXiv:2608. 14332v1 Announce Type: cross Abstract: Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once--reaching a distant goal, tracking a route, avoiding obstacles, obeying signals--and a fixed objective gives no order in which to learn them.
arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.