Proof pending. This topic has not reached the minimum paper threshold yet.
Recent advances in robotics, automation, and artificial intelligence have enabled urban traffic systems to operate with increasing autonomy towards future smart cities, powered in part by the developm...
Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL...
Reinforcement Learning (RL) in Traffic Signal Control (TSC) faces significant hurdles in real-world deployment due to limited generalization to dynamic traffic flow variations. Existing approaches oft...
Reinforcement learning (RL) has shown promise in traffic signal control (TSC). However, its reliance on predefined states limits responsiveness to observable open-world events that are absent from tra...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID traffic-signal-control | Route /topic/traffic-signal-control
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/traffic-signal-controlMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Traffic Signal Control",
"cluster": "Traffic Signal Control"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Traffic Signal Control",
"normalized_query": "traffic-signal-control",
"route": "/topic/traffic-signal-control",
"paper_ref": null,
"topic_slug": "traffic-signal-control",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.