Yuanzhe Geng
Papers
2
Total Citations
48
H-Index
2
About
Yuanzhe Geng is a researcher focused on intelligent transportation systems, with a particular emphasis on dynamic route planning and optimization. His key research areas include deep reinforcement learning, traffic flow modeling, and real-time navigation algorithms. Geng’s major contribution lies in developing a deep reinforcement learning framework for dynamic route planning that minimizes travel time without relying on prior knowledge of road networks—a significant advancement over traditional shortest-path or safety-based methods. His most cited work, “Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time” (2021), has garnered 45 citations, reflecting its impact on adaptive traffic management. This approach addresses the critical challenge of real-time decision-making in uncertain environments, offering a scalable solution for smart city applications. Geng’s research bridges the gap between theoretical reinforcement learning and practical transportation engineering, demonstrating how autonomous agents can learn optimal routes through continuous interaction with traffic data. His work has implications for reducing congestion, improving fuel efficiency, and enhancing urban mobility, making him a notable contributor to the field of intelligent transportation.
Research Focus
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Top Papers
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