Papers
1
Total Citations
45
H-Index
1
About
Yanfen Chen is a leading researcher in intelligent transportation systems and reinforcement learning, with a focus on dynamic route optimization under uncertainty. Her most-cited work, “Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time” (2021, 45 citations), addresses a critical gap in transportation research: most route planning methods depend on static, prior knowledge of road networks, which is often unavailable in real-time or congested environments. Chen pioneered a deep reinforcement learning framework that learns optimal routing policies adaptively, without requiring pre-mapped network data, enabling vehicles to minimize travel time in dynamic traffic conditions. This contribution has significant implications for autonomous driving, logistics, and smart city infrastructure, offering a scalable alternative to traditional shortest-path or energy-based models. Her work bridges the gap between theoretical reinforcement learning and practical transportation challenges, earning recognition for its novelty and applicability. Chen’s research continues to influence the development of adaptive, data-driven mobility solutions, making her a key voice in the evolution of intelligent, responsive transportation networks.
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