SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.g., visitors' profiles and goals) beyond raw spatial paths to better understand why people move in certain ways. However, analyzing semantic trajectories in real-world scenarios remains challenging, as collecting high-quality data is costly and often lacks rich semantic information. Meanwhile, existing simulation tools require substantial technical expertise, which makes them difficult for practitioners to adopt. To address th
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “SenseWalk: Agent-Based Semantic Trajectory Simulation Powere” ≈ “AgentCore-8B””
- PossiblePossibly related (embedding) · 54%LAMDA-NeSy/ChinaTravel →
- PossiblePossibly related (embedding) · 46%carla-simulator/carla →
- PossiblePossibly related (embedding) · 57%Yuqi-Zhou/LRAT →
- PossiblePossibly related (embedding) · 50%typedef-ai/fenic →
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “SenseWalk: Agent-Based Semantic Trajectory Simulation Powere” ≈ “zhayujie/CowAgent””
- FuzzySimilar title/name (fuzzy) · 59%NousResearch/hermes-agent →
“Fuzzy title match (0.73): “SenseWalk: Agent-Based Semantic Trajectory Simulation Powere” ≈ “NousResearch/hermes-agent””
- FuzzySimilar title/name (fuzzy) · 59%2FastLabs/agent-squad →
“Fuzzy title match (0.73): “SenseWalk: Agent-Based Semantic Trajectory Simulation Powere” ≈ “2FastLabs/agent-squad””