出租车数据的时间序列谱聚类分析

城市空间运行的出租车产生大量的OD数据，数据的时序呈现周期性特点，客观反映人们的出行行为模式，本文采用谱聚类算法对北京五环区域内各空间单元的出行特征及其相似性进行探究。由于空间单元的时空行为特征受空间邻域和功能区划的影响，研究添加邻域因子和功能区因子以改进时间序列的相似性度量方法，从而实现时间序列谱聚类算法的空间和功能区拓展，进而增加人们对不同时空条件下出行行为特征的了解，以便对不同空间单元提供差异性服务，如不同时段公交的发车频次、动态调整商场营业时间、不同时空环境出租车候车点的实时变换、调控和优化不同区域服务保障等，将有助于降低城市能耗，更加合理地利用资源，也有助于居民实现智慧生活。

Time series spectral clustering analysis of taxi data

Taxis running in urban space generate a lot of OD data. The time-series of data presents periodic characteristics and objectively reflects people's travel behavior patterns. The spectral clustering algorithm is used to explore the travel characteristics and similarities of spatial units in the Fifth Ring Road area of Beijing. Because the temporal-spatial behavior characteristics are affected by the neighborhood and functional zoning, the study adds the neighborhood factor and the functional area factor to improve the time series similarity measurement method, and realizes the space and functional area expansion of the time series spectrum clustering algorithm. Furthermore, it can increase people's understanding of travel behavior characteristics under different time and space conditions, so as to provide different services for different spatial units, such as the frequency of bus in different time periods, the dynamic adjustment of the business hours of malls, the real-time transformation of taxi waiting points in different time and space environments, and the regulation and optimization of service guarantees in different regions, etc. The research helps to reduce urban energy consumption, makes more rational use of resources, and helps residents to realize smart life.