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• W2952922520 abstract "Real-time traffic signal control with environmental objectives has been a difficult challenge due to (1) the highly dynamic and uncertain nature of road traffic and their emission profile; (2) the need for generating timely and robust decisions for large-scale networks; (3) the incorporation of multi-source and heterogeneous data; and (4) the balance between traffic and environmental objectives. To address these challenges, this paper proposes a real-time traffic signal control framework based on a nonlinear decision rule (NDR), encapsulating an artificial neural network (ANN),which is integrated with realistic traffic and emission modeling via agent-based microsimulation. The implementation of the NDR method is divided into an off-line module and an on-line module. The off-line module trains the ANN and the NDR using historical traffic data, which amounts to a stochastic optimization with traffic and environmental objectives. The on-line module efficiently implements the trained NDR, whose performance is guaranteed by the off-line training. Simulation study of the proposed signal control framework in a real network in West Glasgow demonstrates the advantage of the proposed method over the current signal control parameters in terms of alleviating congestion and reducing traffic emissions." @default.
• W2952922520 created "2019-06-27" @default.
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• W2952922520 date "2018-09-04" @default.
• W2952922520 modified "2023-09-26" @default.
• W2952922520 title "Real-time network traffic signal control for emission reduction based on nonlinear decision rule" @default.
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• W2952922520 hasPublicationYear "2018" @default.
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