Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Abstract

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, CO 2 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.

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“…The proposed BRFGTS performance is compared with other techniques like the time‐slot signal scheme method with the incorporation of generalized cycle‐based structure (TSS‐GCS), 19 real‐time based signal control approach dependent upon nonlinear‐decision rule (RSC‐NDR), 20 adaptive neuro‐FIS (ANFIS) 26 and fuzzy logic system (FLS) 27 . The performance comparison was performed to determine the proposed methods effectiveness.…”

“…The nonlinear decision approach was invented by Song et al 20 for real‐time traffic signal control. Here, a feedforward neural system was used to analyze the recurrent traffic.…”