摘要
应用模拟-优化方法研究城市交通拥塞源的位置识别及数据推演问题。首先,构建一个假想城市交通拥塞案例,引入地下水污染质运移模型,并结合城市交通拥塞特征,改进模型适应性,利用Cholesky分解方法建立描述城市交通拥塞非均质性的城市交通流出率连续场。其次,采用Kriging和BP (Back Propagation)神经网络建立城市交通拥塞数值模拟模型的替代模型,通过平均相对误差、确定性系数及均方根误差检验替代模型精度。最后,利用麻雀搜索算法(SSA)和遗传算法(GA)求解优化模型,通过平均相对误差检验反演识别结果。研究结果表明:利用Cholesky分解方法,得到城市交通流出率分布不均,符合城市交通拥塞异质性特征,且均值为322.15,处于中等扩散水平;Kriging替代模型精度更高,平均相对误差为0.98%;应用SSA和GA均能快速准确地识别城市交通拥塞源的位置及交通扩散量,SSA相较于GA的交通拥塞源位置的整体相对误差提高1.68%,交通量的整体相对误差提升2.52%。综上,基于Kriging和SSA方法耦合的模拟-优化模型可以有效识别城市交通拥塞源和交通扩散交通量,且识别精度较高,可为城市交通拥塞源控制及交通扩散管控方案提供重要参考。
This paper applies simulation-optimization methods to identify the location of urban traffic congestion sources and data derivation.First,a hypothetical urban traffic congestion scenario is established and the model is improved by introducing the groundwater contamination mass transport model and considering the urban traffic congestion characteristics.The continuous field of urban traffic outflow rate is proposed to describe the inhomogeneity of urban traffic congestion based on the Cholesky decomposition method.Then,the Kriging and back propagation(BP)neural network are used to develop an alternative model for the numerical simulation model of urban traffic congestion.The accuracy of the alternative model is tested by the mean relative error,deterministic coefficient and root mean square error.The optimized model is solved through the Sparrow Search Algorithm(SSA)and Genetic Algorithm(GA),and the inverse identification results are tested by the average relative error.The results show that(i)using the Cholesky decomposition method,the urban traffic outflow rate is unevenly distributed and consistent with the characteristics of urban traffic congestion heterogeneity.The obtained mean value is 322.15.The result is at a medium diffusion level.(ii)The Kriging alternative model is more accurate than Back Propagation(BP)neural network alternative model,and the average relative error is 0.98%.(iii)Both the SSA and GA can be applied to identify the location of urban traffic congestion sources and traffic diffusion volume fast and accurately.Compared to the GA,the SSA improves the overall relative error of traffic congestion source location by 1.68%and improves the overall relative error of traffic volume by 2.52%.The proposed method can effectively identify urban traffic congestion sources and traffic diffusion traffic volumes with high accuracy,which can provide important references for urban traffic congestion source control and making traffic diffusion control schemes.
作者
赵雪亭
胡立伟
ZHAO Xue-ting;HU Li-wei(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2023年第2期74-83,共10页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(42277476,61863019)。
关键词
城市交通
反演识别
模拟-优化方法
城市交通拥塞
连续场划分
urban traffic
inverse identification
simulation-optimization methods
urban traffic congestion
continuous field classification
