UPTM-LLM: Large Language Models-Powered Urban Pedestrian Travel Modes Recognition for Intelligent Transportation System
Published in Applied Soft Computing, 2025
With the progression of urbanization and information technology, Intelligent Transportation Systems (ITS) are increasingly crucial for improving urban efficiency and reducing traffic congestion. A key element within ITS is Urban Pedestrian Travel Mode (UPTM) recognition, which aids traffic management through data-driven optimization and congestion mitigation. Deep learning (DL) has advanced transportation research but struggles with the complex semantics of pedestrian movement, including temporal context (e.g., peak/off-peak hours, workdays/weekends), origin-destination POI categories, and the underlying characteristics of GPS trajectories (encompassing kinematics and GIS features). The emergence of large language models (LLMs), known for their large-scale parameters and deep architectures, has enhanced the capacity to interpret such complex semantics. Leveraging this capability, we propose UPTM …
Recommended citation: Y Li, Y Zhan, M Liang*, Y Zhang, J Liang. (2025). UPTM-LLM: Large Language Models-Powered Urban Pedestrian Travel Modes Recognition for Intelligent Transportation System. Applied Soft Computing, 113999. https://www.sciencedirect.com/science/article/pii/S1568494625013122