Donghang Li

Baoshen Guo, Donghang Li, Zhiqing Hong et al.

Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on static attributes of individual places, while ignoring the mobility patterns that reveal how places are functionally connected. To address this gap, we explore whether mobility networks can elicit the geospatial capabilities of foundation models by explicitly encoding connectivity among urban entities. We propose \textit{MobFusion}, a modular mobility-enhanced foundation model fusion paradigm, and instantiate it through three complementary designs: (i) mobility networks as contexts for zero-shot LLM prompting, (ii) as graph connectors for fusing geospatial visual embeddings with textual embeddings, and (iii) as structured tokens for multimodal LLM reasoning. Using anonymized large-scale mobility datasets from three U.S. metropolitan areas, we find that \textit{MobFusion} improves urban prediction tasks (e.g., median household income, population density, and crime prediction) across three instantiations, demonstrating that incorporating human mobility can effectively improve the socioeconomic understanding of foundation models.

Rongchang Lu, Changyu Li, Donghang Li et al.

Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these issues, we propose our Omni-Scale RWKV Super-Resolution (OmniRWKVSR) model which presents a novel approach that combines the Receptance Weighted Key Value (RWKV) architecture with feature extraction techniques such as Visual RWKV Spatial Mixing (VRSM) and Visual RWKV Channel Mixing (VRCM), aiming to overcome the limitations of existing methods and achieve superior SISR performance. This work has proved able to provide effective solutions for high-quality image reconstruction. Under the 4x Super-Resolution tasks, compared to the MambaIR model, we achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM.

Gangcheng Zhang, Yeshuo Shu, Keyi Liu et al.

The widespread use of e-bikes has facilitated short-distance travel yet led to confusion and safety problems in road traffic. This study focuses on the dual characteristics of e-bikes in traffic conflicts: they resemble pedestrians when interacting with motor vehicles and behave like motor vehicles when in conflict with pedestrians, which raises the right of way concerns when potential conflicts are at stake. Using the Quantal Response Equilibrium model, this research analyzes the behavioral choice differences of three groups of road users (vehicle-pedestrian, vehicle-e-bike, e-bike-pedestrian) at right-turn-on-red crossroads in right-turning lines and straight-going lines conflict scenarios. The results show that the behavior of e-bikes is more similar to that of motor vehicles than pedestrians overall, and their interactions with either pedestrians or motor vehicles do not establish a reasonable order, increasing the likelihood of confusion and conflict. In contrast, a mutual understanding has developed between motor vehicles and pedestrians, where motor vehicles tend to yield, and pedestrians tend to cross. By clarifying the game theoretical model and introducing the rationality parameter, this study precisely locates the role of e-bikes among road users, which provides a reliable theoretical basis for optimizing traffic regulations.