TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (capturing temporal sequences and movement dynamics). Separate embeddings are generated for each modality and then concatenated to produce trajectory scene embeddings with rich semantic content which are further paired with a simple MLP classifier. We validate the proposed framework on Travel Mode Identification (TMI), a critical task for analyzing travel choices and understanding mobility behavior. Our experiments show that these embeddings achieve significant performance improvement, highlighting the advantage of our LLM-driven method in capturing deep spatio-temporal dependencies and reducing reliance on handcrafted features. This semantic enhancement promises significant potential for diverse downstream applications and future research in geospatial artificial intelligence. The source code and dataset are publicly available at: https://github.com/februarysea/TrajSceneLLM.
Code (1)
Tasks
Temporal SequencesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Holistic Semantic Representation for Navigational Trajectory Generation
Trajectory generation has garnered significant attention from researchers in the field of spatio-temporal analysis, as it can generate substantial synthesized human mobility trajectories that enhance user privacy and all…
Few-Shot LearningZero-Shot LearningDialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
Existing text summarization research has focused much on monologic information (e.g., newspaper articles, reports) without accounting for the interaction between speakers or authors. In contrast, dialogues are a rich com…
Text SummarizationSpotlight on Token Perception for Multimodal Reinforcement Learning
While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal reasoning neglect the critical role of visu…
Reinforcement LearningMultimodal ReasoningCapturing Context-Aware Route Choice Semantics for Trajectory Representation Learning
Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. Fr…
Representation LearningTravel Time EstimationCAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
Multimodal Graph Neural Networks (MGNNs) have shown strong potential for learning from multimodal attributed graphs, yet most existing approaches rely on tightly coupled architectures that suffer from prohibitive computa…
Representation LearningGraph Learning