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Network traffic control based on a mesoscopic dynamic flow model

机译:基于介观动态流模型的网络流量控制

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The paper focuses on Network Traffic Control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文重点介绍基于总流量流变量的网络流量控制,其目标是与一天之内的流量动态一致的信号设置。所提出的优化策略基于两个连续步骤:第一步是指每个单个路口优化(绿色时序),第二步是网络协调(偏移量)。这两个优化问题都是通过元启发式算法解决的:绿色计时的优化是通过多准则遗传算法进行的,而偏移量优化是通过单准则爬坡算法实现的。为了保证正确的排队和溢出仿真,在网络优化方法中嵌入了高级的介观交通流模型。所采用的介观交通流模型还包括链路水平队列建模。将通过建议的优化框架获得的结果与通过基准工具获得的结果进行比较。 (C)2015 Elsevier Ltd.保留所有权利。

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