Road Network
Road networks are the focus of extensive research aiming to improve their efficiency, safety, and understanding through data analysis and modeling. Current research emphasizes developing advanced algorithms, including graph neural networks, transformers, and deep learning models, for tasks such as traffic forecasting, route optimization, and automated road network extraction from various data sources (e.g., historical maps, satellite imagery, GPS traces). These advancements have significant implications for urban planning, transportation management, autonomous driving, and logistics, enabling more efficient resource allocation, improved safety measures, and better infrastructure design. The development of large, publicly available datasets is also a key focus, facilitating more robust model training and evaluation.
Papers
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Kan Wu, Yuhao Du, Fuliang Li, Zhenhui Li
Mastering Spatial Graph Prediction of Road Networks
Sotiris Anagnostidis, Aurelien Lucchi, Thomas Hofmann