Nataly Zhukova
Papers
1
Total Citations
56
H-Index
1
About
Nataly Zhukova is a leading researcher in intelligent transportation systems and deep learning, whose work focuses on the intersection of urban mobility and advanced neural network architectures. Her most-cited paper, "Urban traffic flows forecasting by recurrent neural networks with spiral structures of layers" (2020, 56 citations), introduces an innovative approach to traffic prediction by employing recurrent neural networks with spiral layer configurations. This contribution addresses the critical challenge of accurately forecasting urban traffic flows, which is essential for smart city planning and real-time traffic management. Zhukova's work demonstrates how novel neural network designs can capture complex spatiotemporal dependencies in traffic data, offering more precise and scalable solutions compared to traditional models. Her research has significant implications for reducing congestion, optimizing route planning, and enhancing urban sustainability. With 56 citations, this paper underscores her impact in the field, making her a notable figure in the development of AI-driven transportation systems. Zhukova's achievements highlight her ability to bridge theoretical advances in machine learning with practical, real-world applications, inspiring further exploration into dynamic neural structures for urban analytics.
Research Focus
Key Achievements
Top Papers
- 1