A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties
A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties
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一种基于随机仿真的不确定性下公平高效的全网信号配时优化方法
DOI：
10.1016/j.trb.2019.03.001
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发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
Ran Bin
中科院分区：
文献类型：
--
作者：
Zheng Liang;Xue Xinfeng;Xu Chengcheng;Ran Bin
路权公平性是交通管理与控制中的一个重要课题。综合考虑阿特金森指数和平均出行时间分别评价交通公平性和效率，提出了不确定性条件下基于双目标信号配时仿真的优化（SO）模型，并采用基于双目标随机仿真的优化（BOSSO）方法进行求解。在该方法中，使用两种类型的代理模型（即回归克里金模型和二次回归模型）分别在整个变量域和局部信赖域中捕获决策变量与双目标之间的复杂映射关系。同时，全局回归克里金模型和自适应选择器的结合有助于预测未测试样本的双目标值并重新估计局部信赖域中的模拟样本，这可以节省大量的计算成本并平滑随机噪声。此外，决策者的非交互角色被用来围绕他/她期望的双目标值生成更多帕累托最优解决方案。通过对基准双目标随机优化问题的算法比较，验证了所提出的 BOSSO 方法在相同的模拟成本下优于其他三种对应方法（即 NSGA-II、BOTR 和 BOEGO）。在实际现场实验中，使用 VISSIM 对中国长沙市拥有 15 个信号交叉口和 5 个非信号交叉口的城市道路网进行了建模。经过对微观交通仿真模型的良好标定，BOSSO解决了有协调和无协调的全网双目标信号配时随机SO问题。数值结果表明，与实际情况相比，优化的非协调信号方案的平均行程时间和阿特金森指数分别最多减少了13.48%和23.49%，优化的协调信号方案的平均行程时间和阿特金森指数分别最多减少了25.58%和2.83%。进一步验证了在交通量变化的情况下，非协调信号方案能够很好地提高交通效率和公平性，而协调信号方案能够较大程度地提高交通效率，但会牺牲交通公平性。此外，平衡分析表明双目标之间存在竞争关系，并且证实BOSSO在相同预算模拟下搜索更好的Pareto最优信号方案时优于NSGA-II、BOTR和BOEGO。总之，BOSSO 有望解决以评估成本高、维度高和随机噪声为特征的双目标优化问题。
The equity of right-of-way is an important topic in traffic management and control. With the balance consideration of traffic equity and efficiency, which are respectively evaluated by the Atkinson index and average travel time, this study proposes a bi-objective signal timing simulation-based optimization (SO) model under uncertainties, and solve it by a bi-objective stochastic simulation-based optimization (BOSSO) method. In this method, two types of surrogate models (i.e., regressing Kriging model and quadratic regression model) are used to capture the complicated mapping relationship between decision variables and bi-objectives, respectively in the whole variable domain and in the local trust-region. Meanwhile, the incorporation of the global regressing Kriging model and an adaptive selector helps to predict bi-objective values of untested samples and re-estimate simulated samples in the local trust-region, which can save great computational costs and smooth stochastic noises. Moreover, the non-interactive role of a decision maker is taken to generate more Pareto optimal solutions around his/her desired bi-objective values. Through the algorithm comparison for a benchmark bi-objective stochastic optimization problem, the proposed BOSSO method is validated to outperform three other counterparts (i.e., NSGA-II, BOTR and BOEGO) under the same simulation costs. In real-field experiments, an urban road network with 15 signalized and five non-signalized intersections in Changsha, China is modeled by VISSIM. After the well calibration of the microscopic traffic simulation model, the network-wide bi-objective signal timing stochastic SO problems with and without coordination are solved by BOSSO. Numerical results indicate that compared with the real-field case, the average travel time and Atkinson index are reduced respectively by at most 13.48% and 23.49% for optimized non-coordinated signal plans, and respectively by at most 25.58% and 2.83% for optimized coordinated ones. It is further validated that under variable traffic volumes, the non-coordinated signal plan can well improve both traffic efficiency and equity, and the coordinated one is capable to improve traffic efficiency at a larger degree but sacrifice traffic equity. Moreover, the balance analyses show the existence of competing relationship between bi-objectives, and BOSSO is confirmed to outperform NSGA-II, BOTR and BOEGO in searching the better Pareto optimal signal plans under the same budged simulations. In conclusion, BOSSO is promising to address bi-objective optimization problems characterized by costly evaluation, high dimensions and stochastic noises.
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DOI：
10.1016/j.procs.2011.04.153
发表时间：
2011
期刊：
--
影响因子：
--
作者：
Sujin Kim;Jong-hyun Ryu
通讯作者：
Sujin Kim;Jong-hyun Ryu
影响因子：
7.5
作者：
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通讯作者：
J. Fitzpatrick;J. Grefenstette
DOI：
10.4236/jtts.2013.32a006
发表时间：
2013-05
期刊：
Journal of Transportation Technologies
影响因子：
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作者：
A. S. Kesten;M. Ergun;T. Yai
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DOI：
10.1016/j.trc.2015.07.014
发表时间：
2015-09-01
影响因子：
8.3
作者：
Khondaker, Bidoura;Kattan, Lina
通讯作者：
Kattan, Lina
DOI：
10.1016/j.tra.2004.12.004
发表时间：
2006-12
期刊：
--
影响因子：
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作者：
D. Levinson;Lei Zhang
通讯作者：
D. Levinson;Lei Zhang