Recognition of travel patterns for urban rail transit passengers based on spatiotemporal sequence similarity
Based on smart card data(SCD)of urban rail transit,a method was proposed to identify travel patterns by modeling the spatio-temporal sequences of individuals.Firstly,all stations visited by a passenger individual were extracted,and the similarity of stations was calculated in terms of inter-station travel frequency,inter-station distance,and the station activity duration,thus the main spatial activity areas of this individual were classified using a hierarchical clustering algorithm.Secondly,the spatio-temporal sequence was inferred based on the travel order of the individual,which is a set of discrete values characterizing the spatio-temporal state.PCA-KL and K-Means++were used to extract the similarity sequence structure to identify passenger travel patterns.Finally,using one-month SCD as an example to identify passenger travel patterns for Xi'an rail transit.The results show that the complex passenger flow has five travel patterns,among which three typical travel patterns are macroscopic commuter travel behavior,accounting for 79%of the total passenger flow.Thus,the pattern identified based on the similarity of individuals' travel spatiotemporal sequences fully reflects the particularity and universality of the research method and it is highly operable for different cities.
万方数据
维普
