6 citations · 11 across the 19 of their papers we have counts for
20 papers
EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation
Jie Zhao, Jie Feng, Can Rong +3
Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience. Existing deep OD models trained on routine mobility often…
DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
Jie Zhao, Xianqi Dai, Jie Feng +2
Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key ch…
SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes
Tianhui Liu, Jie Feng, Zhiheng Zheng +6
Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into actions in everyday 3D environme…
TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control
Yuwei Du, Jun Zhang, Jie Feng +3
Traffic simulation is important for transportation optimization and policy making. While existing simulators such as SUMO and MATSim offer fully-featured platforms and utilities, u…
RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
Jun Zhang, Xin Zhang, Jie Feng +5
Multimodal large language models (MLLMs) have demonstrated powerful capabilities in general spatial understanding and reasoning. However, their fine-grained spatial understanding a…
CityRiSE: Reasoning Urban Socio-Economic Status in Large Vision-Language Models via Reinforcement Learning
Tianhui Liu, Hetian Pang, Xin Zhang +3
Urban socio-economic sensing plays a vital role in advancing global sustainable development goals. With the advent of Large Vision-Language Models (LVLMs), new opportunities have e…
AMFT: Aligning LLM Reasoners by Meta-Learning the Optimal Imitation-Exploration Balance
Lixuan He, Jie Feng, Yong Li
Large Language Models (LLMs) are typically fine-tuned for reasoning tasks through a two-stage pipeline of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL), a pr…
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Shengyuan Wang, Jie Feng, Tianhui Liu +2
Large language models (LLMs) possess extensive world knowledge, including geospatial knowledge, which has been successfully applied to various geospatial tasks such as mobility pre…
UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding
Jie Feng, Shengyuan Wang, Tianhui Liu +2
Urban research involves a wide range of scenarios and tasks that require the understanding of multi-modal data. Current methods often focus on specific data types and lack a unifie…
CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation
Yuwei Du, Jie Feng, Jian Yuan +1
Human mobility simulation plays a crucial role in various real-world applications. Recently, to address the limitations of traditional data-driven approaches, researchers have expl…
Open-Set Living Need Prediction with Large Language Models
Xiaochong Lan, Jie Feng, Yizhou Sun +5
Living needs are the needs people generate in their daily lives for survival and well-being. On life service platforms like Meituan, user purchases are driven by living needs, maki…
Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory System
Lixuan He, Haoyu Dong, Zhenxing Chen +3
Vision-and-Language Navigation (VLN) in large-scale urban environments requires embodied agents to ground linguistic instructions in complex scenes and recall relevant experiences…
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning
Yuan Yuan, Yukun Liu, Chonghua Han +2
Human mobility is a fundamental pillar of urban science and sustainability, providing critical insights into energy consumption, carbon emissions, and public health. However, the d…
LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan
Xiaochong Lan, Jie Feng, Jiahuan Lei +2
Large language models (LLMs) have exhibited remarkable capabilities and achieved significant breakthroughs across various domains, leading to their widespread adoption in recent ye…
CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing
Tianhui Liu, Hetian Pang, Xin Zhang +5
Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introdu…
UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Models
Yu Zheng, Longyi Liu, Yuming Lin +4
The advent of Large Language Models (LLMs) holds promise for revolutionizing various fields traditionally dominated by human expertise. Urban planning, a professional discipline th…
TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration
Yuwei Du, Jie Feng, Jie Zhao +1
Trajectory modeling, which includes research on trajectory data pattern mining and future prediction, has widespread applications in areas such as life services, urban transportati…
AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction
Jie Feng, Yuwei Du, Jie Zhao +1
Next location prediction plays a crucial role in various real-world applications. Recently, due to the limitation of existing deep learning methods, attempts have been made to appl…
CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Jie Feng, Tianhui Liu, Yuwei Du +3
Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generati…
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
Jie Feng, Jun Zhang, Tianhui Liu +6
As large language models (LLMs) continue to advance and gain widespread use, establishing systematic and reliable evaluation methodologies for LLMs and vision-language models (VLMs…