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山东省横向干扰路段实际通行能力模型研究

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为明确横向干扰因素对公路路段通行能力的影响,实现通行能力计算模型本地化,针对山东省公路交通现状,以山东省横向干扰路段为研究对象,从交通流因素、道路基础设施因素、土地利用类型 3 个方面对公路基本路段通行能力横向干扰影响因素进行定性分析;采用皮尔逊相关性分析方法对横向干扰影响因素与影响系数进行相关性分析;根据相关性分析结果,选取建模自变量,分别通过SPSS、MATLAB分析软件构建基于多元线性回归和基于BP神经网络的公路基本路段通行能力横向干扰影响机理本地化模型.结果表明:基于BP神经网络的横向干扰路段通行能力本地化模型具有更高的精确性.
Modelling of actual capacity of horizontally disturbed road sections in Shandong Province
In order to clarify the influence of lateral interference factors on the capacity of highway road sections and achieve the localisation of the capacity calculation model,in view of the current situation of highway traffic in Shandong Province,and taking the lateral interference road sections in Shandong Province as the research object,the lateral interference influencing factors of the capacity of basic road sections were qualitatively analysed from the three aspects of the traffic flow factors,the road infrastructure factors,and the type of land use,and Pearson's correlation was used to analyse the influence factors of horizontal interference on the capacity of basic road sections.The Pearson correlation analysis method was used to correlate the influence factors and influence coefficients of lateral interference,and according to the results of the correlation analysis,the independent variables of modelling were selected,and the localization model of the influence mechanism of lateral interference of the capacity of the basic road sections based on multivariate linear regression and BP neural network was constructed through the analytical software of SPSS and MATLAB,respectively.The results showed that the BP neural network-based localisation model of the capacity of the transverse interference section has higher accuracy.It was of great significance to predict the actual capacity accurately to solve the traffic congestion problem and improve the road service level.

lateral interference influencing factorsroad section capacitymultiple linear regressionBP neural network

董昌乐、王修光、王雄

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山东交通学院 交通与物流工程学院,山东 济南 250357

山东正衢交通工程有限公司,山东 济南 250000

横向干扰影响因素 公路路段通行能力 多元线性回归 BP神经网络

2024

农业装备与车辆工程
山东省农业机械科学研究所 山东农机学会

农业装备与车辆工程

影响因子:0.279
ISSN:1673-3142
年,卷(期):2024.62(10)