Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI‐based traffic control systems.

中文翻译：

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用于多交集信号控制优化的学习误差分布内核增强神经网络方法

交通拥堵在很大程度上导致了严重的流动性和能源效率低下。在与基于人工智能 （AI） 的模型相关的交通信号控制和管理中发现了许多研究挑战。例如，开发 AI 驱动的动态交通系统模型来准确捕获高分辨率交通属性并制定强大的控制算法以实现交通信号优化是很困难的。此外，交通系统建模和控制过程中的不确定性会使交通信号系统的可控性进一步复杂化。为了部分解决这些挑战，本研究提出了一种新的混合神经网络模型，该模型通过概率密度函数核整形技术进行了增强，以更好地构建交通系统动力学并改进综合交通网络建模和控制。进行了数值实验测试，结果表明，所提出的控制方法优于基线控制策略，总体平均延迟降低了 11.64%。通过利用这种创新模型的能力，本研究旨在解决与交通拥堵和能源效率低下相关的重大挑战，以实现更有效和适应性更强的基于 AI 的交通控制系统。