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.
Commuting Traffic Analysis Zone Recognition Using Improved K-means Algorithm
Commuting is a periodical and stable travel behavior of urban residents,which is an important research content of ur-ban development planning and public transportation management.Taxi GPS trajectory data reflects urban traffic conditions and citizens'travel patterns to a certain extent.Aiming at the problem of taxi regional commuting pattern recognition,a commuting traffic analysis zone recognition method based on improved K-means algorithm is proposed.This method mainly includes three steps:dividing traffic analysis zones,generating flow transfer matrix between traffic analysis zones,and identifying commuting traffic analysis zone pairs.Referring to the existing traffic analysis zones division methods,a bottom-up division method based on fine-grained elements is proposed.In the recognition model of commuting traffic analysis zone pairs,the traffic flow and its dis-persion coefficient during peak hours are taken as input features,and the commuting traffic analysis zone pairs are identified based on the improved K-means algorithm.Finally,an experimental verification is carried out based on the Chongqing taxi GPS data set,and the experimental results show that the method is effective.
GPS trajectory dataimproved K-means algorithmcommuting traffic analysis zone recognition
万方数据
维普
