# Traffic Signal Control

Traffic signal control has relied on structured traffic states from dedicated perception pipelines, or on converting simulator images into intermediate representations before deciding. Traffic-VL is an eight-billion-parameter vision-language model that maps synchronized multi-view images and natural-language phase descriptions directly to phase choices, trained with SynTra-V, which turns physics-based simulation into semantically audited photorealistic surveillance imagery. It matches the structured-state Traffic-R1 on five closed-loop benchmarks, reaches 82.1% phase-selection accuracy on real surveillance images, and over three weeks at ten intersections reduced average queue length by 10.4% relative to historical manual operation.

Traffic signal control has relied on structured traffic states from dedicated perception pipelines, or on converting simulator images into intermediate representations before deciding. Traffic-VL is an eight-billion-parameter vision-language model that maps synchronized multi-view images and natural-language phase descriptions directly to phase choices, trained with SynTra-V, which turns physics-based simulation into semantically audited photorealistic surveillance imagery. It matches the structured-state Traffic-R1 on five closed-loop benchmarks, reaches 82.1% phase-selection accuracy on real surveillance images, and over three weeks at ten intersections reduced average queue length by 10.4% relative to historical manual operation.

Network signal control no longer compresses the whole road network into one shared latent vector. Policies trained under nominal flow often cannot tell a brief fluctuation from a sustained capacity loss when blockages, corridor failures or rain arrive. Here each intersection re-weights its neighbours from their joint state, then reads a history window to judge whether a change persists, cutting waiting-time recovery from 329 to 245 seconds under random blockages and by 82 seconds under a central closure.

Network signal control no longer compresses the whole road network into one shared latent vector. Policies trained under nominal flow often cannot tell a brief fluctuation from a sustained capacity loss when blockages, corridor failures or rain arrive. Here each intersection re-weights its neighbours from their joint state, then reads a history window to judge whether a change persists, cutting waiting-time recovery from 329 to 245 seconds under random blockages and by 82 seconds under a central closure.