OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
arXiv:2606.27381v1 Announce Type: new Abstract: 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 overflow during peak hours, exacerbating congestion and creating safety hazards. We propose OverFlowLight, a real-time framework designed to preemptively resolve overflow and enhance overall TSC performance. It first introduces a mechanism to accurately
The increasing sophistication of AI algorithms and the growing need for efficient urban infrastructure management are driving the development of real-time traffic optimization solutions.
Efficient traffic management leveraging AI can significantly reduce economic losses from congestion, improve public safety, and decrease carbon emissions in urban areas.
This development suggests a move from reactive to proactive traffic signal control, potentially revolutionizing urban mobility and infrastructure planning.
- · Smart City Technology Providers
- · Urban Dwellers
- · Logistics and Delivery Companies
- · Municipal Governments
- · Traditional Traffic Management Systems
Reduced traffic congestion and commute times in urban environments.
Increased urban livability and productivity, potentially freeing up labor hours and reducing fuel consumption.
Re-evaluation of urban planning and infrastructure investment as AI-driven solutions optimize existing road networks, potentially delaying or altering large-scale infrastructure projects.
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Read at arXiv cs.LG