断面车路通讯环境下的恒速车流运行模型
发布时间：2018-11-26 21:31
【摘要】：从流量角度,在假设车速恒定的基础上建立了车流运行模型,并以该模型预测和实际到达的流量分布之间的最大流量区间变异系数为评价指标,分析了路段上游数据收集断面位置、路段长度、交通流量和不同转向车流对预测效果的影响.与静态匀速模型的对比分析结果表明:数据收集断面位置和路段长度对模型的预测效果影响很小,且不同交通流量和转向车流情况下模型预测存在一定规律性.最后结合实际调查数据,证实了模型的有效性.
[Abstract]:From the point of view of flow, a traffic flow operation model is established on the basis of the assumption of constant speed, and the maximum flow interval variation coefficient between the prediction of the model and the actual flow distribution is taken as the evaluation index. The effects of section location, length of section, traffic flow and different steering vehicle flow on the prediction results are analyzed. The results of comparison with the static uniform speed model show that the location of the section and length of the section of the data collection have little influence on the prediction effect of the model, and there are some regularity in the prediction of the model with different traffic flow and steering vehicle flow. Finally, the validity of the model is verified by the actual investigation data.
【作者单位】： 西南交通大学交通运输与物流学院;西南交通大学综合交通运输智能化国家地方联合工程实验室;同济大学交通运输工程学院;
【基金】：2015年国家大学生创新创业训练计划(201510613045) 2016省级创新训练项目(2016152)
【分类号】：U491
[Abstract]:From the point of view of flow, a traffic flow operation model is established on the basis of the assumption of constant speed, and the maximum flow interval variation coefficient between the prediction of the model and the actual flow distribution is taken as the evaluation index. The effects of section location, length of section, traffic flow and different steering vehicle flow on the prediction results are analyzed. The results of comparison with the static uniform speed model show that the location of the section and length of the section of the data collection have little influence on the prediction effect of the model, and there are some regularity in the prediction of the model with different traffic flow and steering vehicle flow. Finally, the validity of the model is verified by the actual investigation data.
【作者单位】： 西南交通大学交通运输与物流学院;西南交通大学综合交通运输智能化国家地方联合工程实验室;同济大学交通运输工程学院;
【基金】：2015年国家大学生创新创业训练计划(201510613045) 2016省级创新训练项目(2016152)
【分类号】：U491
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