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

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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, 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.

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Notes

  1. U.S. Environmental Protection Agency (2012) Report to Congress on Black Carbon. Department of the Interior, Environment, and Related Agencies Appropriations Act. EPA-450/R-12-001.

  2. Transport ScotlandÕs Instantaneous Emissions Software AIRE. 2011. http://www.sias.com/2013/AIRE.html.

  3. Transport Scotland. AIRE (Analysis of Instantaneous Road Emissions) User Guidance In, Scotland, 2011.

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Acknowledgements

This study was partially supported by the EU 7th Framework Program project CARBOTRAF (28786), National Social Science Foundation of China (15BGL143), and the Zhejiang University/University of Illinois at Urbana-Champaign Institute.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2BU, UK

    Junwoo Song, Simon Hu & Ke Han

  2. School of Civil Engineering, ZJU-UIUC Institute, Zhejiang University, Haining, China

    Simon Hu

  3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China

    Ke Han & Chaozhe Jiang

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  1. Junwoo Song
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  2. Simon Hu
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Correspondence to Ke Han.

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Appendix: : the PSO Algorithm

Appendix: : the PSO Algorithm

Given the objective function to be minimized, denoted f(⋅), and the feasible domain S, the following pseudo code summarizes the PSO procedure.

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Song, J., Hu, S., Han, K. et al. Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation. Netw Spat Econ 20, 675–702 (2020). https://doi.org/10.1007/s11067-020-09497-3

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