王益, 荣建, 周晨静, 常鑫, 刘思杨. 考虑大车比例与车道宽度因素交互影响的饱和流率修正[J]. 北京工业大学学报, 2021, 47(2): 154-161. DOI: 10.11936/bjutxb2019080006
    引用本文: 王益, 荣建, 周晨静, 常鑫, 刘思杨. 考虑大车比例与车道宽度因素交互影响的饱和流率修正[J]. 北京工业大学学报, 2021, 47(2): 154-161. DOI: 10.11936/bjutxb2019080006
    WANG Yi, RONG Jian, ZHOU Chenjing, CHANG Xin, LIU Siyang. Revision of Saturation Flow Rate With Interaction Between Lane Width and Percentage of Heavy Vehicles Factors[J]. Journal of Beijing University of Technology, 2021, 47(2): 154-161. DOI: 10.11936/bjutxb2019080006
    Citation: WANG Yi, RONG Jian, ZHOU Chenjing, CHANG Xin, LIU Siyang. Revision of Saturation Flow Rate With Interaction Between Lane Width and Percentage of Heavy Vehicles Factors[J]. Journal of Beijing University of Technology, 2021, 47(2): 154-161. DOI: 10.11936/bjutxb2019080006

    考虑大车比例与车道宽度因素交互影响的饱和流率修正

    Revision of Saturation Flow Rate With Interaction Between Lane Width and Percentage of Heavy Vehicles Factors

    • 摘要: 为了进一步提高信号交叉口饱和流率的估算精度,在现有的饱和流率模型基础上,提出一种考虑因素之间交互影响的饱和流率修正方法.以分析大车比例与车道宽度2个因素间的交互影响为目标,基于北京市22个信号交叉口35条直行车道实测数据,利用双因素方差分析法对因素间的交互作用进行检验,借助多元线性回归方法构建考虑交互作用后的综合修正系数模型,并通过新采集的北京市3个交叉口6条直行车道实测数据对提出的模型进行适用性分析.结果表明,考虑交互作用的饱和流率修正模型平均误差均小于15%,明显优于HCM2016模型的31.50%与GB50647—2011模型的28.49%;增加交互项(误差分别为11.68%、11.67%)的模型精度高于无交互项(误差为12.52%)的模型.交互项形式的差异对模型性能影响较小.大车比例与车道宽度对饱和流率有交互影响,考虑交互影响后饱和流率修正模型精度显著提升,有助于模型进一步本地化.

       

      Abstract: To improve the accuracy of the estimated saturation flow rate (SFR) at signalized intersection, a method considering interaction between adjustment factors was proposed based on the current SFR model. Considering two factors (percentage of heavy vehicles and lane width), the data on 35 through lanes at 22 signalized intersections were collected. First, a two-way ANOVA method was used to verify the interactions and quantify how much two factors relate. Then, the multiple linear regression (MLR) built the interaction models (considered lane width and heavy vehicle). Finally, with collecting 6 through lanes at 3 signalized intersections in Beijing, the performance and applicability of proposed models were analyzed. Results show that the errors of SFR prediction models with considering interactions are below 15%. They are significantly smaller than the Highway Capacity Manual model error 31.50%, GB50647-2011 model error 28.49%, respectively. The models with interaction terms (error of 11.68% and 11.67%, respectively) are higher than that without interaction (error of 12.52%). Different forms of interaction terms have less impact on model performance. The relationship between lane width factor and heavy vehicles factor is not independent. The accuracy of the estimated SFR is improved by considering the interaction. This is contributed to model localization.

       

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