为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇...为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性。展开更多
通勤是具有周期性和稳定性的城市居民出行行为,是城市发展规划和公共交通管理的重要研究内容。出租车GPS(Global Position System,全球定位系统)轨迹数据在一定程度上反映了城市交通状况和市民出行模式。针对出租车区域性通勤模式识别问...通勤是具有周期性和稳定性的城市居民出行行为,是城市发展规划和公共交通管理的重要研究内容。出租车GPS(Global Position System,全球定位系统)轨迹数据在一定程度上反映了城市交通状况和市民出行模式。针对出租车区域性通勤模式识别问题,本文提出一种基于改进K-means算法的通勤交通小区识别方法。该方法主要包括3个步骤:划分交通小区、生成交通小区之间的流量转移矩阵和识别通勤交通小区对。参考现有的交通小区划分方法,本文提出一种基于细粒度单元的自下而上的交通小区划分方法。在通勤交通小区对识别模型中,以高峰时段的流量及其离散系数作为输入特征,基于改进K-means算法识别通勤交通小区对。最后,基于重庆市出租车GPS数据集进行实验验证,结果表明该方法效果显著。展开更多
This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this p...This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.展开更多
文摘为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性。
文摘通勤是具有周期性和稳定性的城市居民出行行为,是城市发展规划和公共交通管理的重要研究内容。出租车GPS(Global Position System,全球定位系统)轨迹数据在一定程度上反映了城市交通状况和市民出行模式。针对出租车区域性通勤模式识别问题,本文提出一种基于改进K-means算法的通勤交通小区识别方法。该方法主要包括3个步骤:划分交通小区、生成交通小区之间的流量转移矩阵和识别通勤交通小区对。参考现有的交通小区划分方法,本文提出一种基于细粒度单元的自下而上的交通小区划分方法。在通勤交通小区对识别模型中,以高峰时段的流量及其离散系数作为输入特征,基于改进K-means算法识别通勤交通小区对。最后,基于重庆市出租车GPS数据集进行实验验证,结果表明该方法效果显著。
基金supported by the National Key Research and Development Program of China(2018YFB1201500)
文摘This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.
