Robust signal timing optimization with environmental concerns
Robust signal timing optimization with environmental concerns
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DOI：
10.1016/j.trc.2013.01.003
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发表时间：
2013-04
影响因子：
8.3
通讯作者：
Lihui Zhang;Yafeng Yin;Shigang Chen
中科院分区：
文献类型：
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作者：
Lihui Zhang;Yafeng Yin;Shigang Chen
本文建立了一个双目标优化模型，以确定协调的交通信号沿着干线，以最大限度地减少交通延误和与人类接触的交通排放的风险的配时计划。基于交通动力学的单元传输表示，模态敏感的排放方法被用来估计尾气排放率的信号控制的动脉的每个单元。然后，采用基于单元的高斯羽流空气扩散模型来捕捉空气污染物的扩散，并计算路边污染物浓度。进一步定义了平均过量暴露的度量，以表示在风的不确定性下与人类暴露于交通污染物相关的风险。建立了信号配时优化模型，对周期长度、偏移量、绿色分裂和相位序列进行优化，使系统总延迟和平均过量曝光同时最小化。通过基于模拟的遗传算法求解双目标优化模型，以找到一组Pareto最优解。最后给出了一个数值例子来验证该模型。
This paper formulates a bi-objective optimization model to determine timing plans for coordinated traffic signals along arterials to minimize traffic delay and the risk associated with human exposure to traffic emissions. Based on a cell-transmission representation of traffic dynamics, a modal sensitive emission approach is used to estimate the tailpipe emission rate for each cell of a signalized arterial. A cell-based Gaussian plume air dispersion model is then employed to capture the dispersion of air pollutants and compute the roadside pollutant concentrations. A measure of mean excess exposure is further defined to represent the risk associated with human exposure to traffic pollutants under the wind uncertainty. A signal timing optimization model is formulated to optimize the cycle length, offsets, green splits and phase sequences to minimize the total system delay and the mean excess exposure simultaneously. The bi-objective optimization model is solved via a simulation-based genetic algorithm to find a set of Pareto optimal solutions. A numerical example is presented to demonstrate the model.