We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO₂ and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

中文翻译：

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实时交通信号控制拥塞和减排的非线性决策规则方法

我们提出了一种基于非线性决策规则（NDR）的实时信号控制框架，该框架定义了网络状态与信号控制参数之间的非线性映射，并根据主要流量条件对实际信号控件进行了非线性映射，并且这种映射是通过离线优化的线模拟。NDR由两个神经网络实例化：前馈神经网络（FFNN）和递归神经网络（RNN），它们在不久的将来具有不同的交通信息处理方式，并对其性能进行了比较。NDR在西格拉斯哥的现实网络的微观交通模拟（S-Paramics）中实现，NDR的离线训练相当于基于模拟的优化，旨在减少延迟，CO ₂和黑碳排放。排放量的计算基于模拟生成的高保真汽车动力学以及AIRE瞬时排放量模型。进行了广泛的测试以评估NDR框架，不仅是在降低上述目标方面的有效性方面，还是在本地与全球利益，延迟与排放之间的权衡，传感器位置的影响以及不同级别方面网络饱和度。结果表明，NDR是缓解拥塞和减少交通排放的有效，灵活和强大的方法。