  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.
