Traffic signal control and flow management play a pivotal role in maintaining the structured flow and order of urban life. Such challenges, intrinsically complex in nature, become increasingly demanding with the rapid urbanization and growth of cities. Addressing this mixed integer nonlinear programming problem (MINLP) requires advanced optimization techniques. The generalized Benders Decomposition (GBD) algorithm emerges as a potential solution, providing more efficient means of arriving at optimal outcomes. Delving deeper into urban transportation dynamics, we develop a comprehensive linear mixed integer programming model. This model aims to optimize urban rail transit transportation organization, ensuring it aligns with real-world traffic demands. We offer a thorough examination of the model's structural nuances, intricacies, and inherent complexities. Furthermore, the Benders Decomposition algorithm is meticulously tailored to effectively decipher and solve the presented model. Leveraging MATLAB, we conducted simulation experiments, the results of which robustly attest to the algorithm's efficacy and superior performance.


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    Title :

    Traffic Management Optimization Based on Generalized Benders Decomposition(GBD): A Case Study of Traffic Signal Control


    Contributors:


    Publication date :

    2023-10-28


    Size :

    816765 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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