关键词: 城市交通, 乘客分类, 两步聚类算法, 自动售检票数据, 城市轨道交通

Abstract:

Traditional passenger classification methods based on traffic survey have drawbacks on limited sample and subjective standard, this paper constructs a new method and indexes in perspective of "consumer behavior" by using automatic fare collection (AFC) data. In order to meet the computation requirements of large data set, the SPSS Modeler is used to cluster the passengers. In case study, one month's AFC data of Beijing rail transit is applied and results shows that it is the best to cluster passengers in five classes, and the stability is verified by comparison with the clustering results in five consecutive days. The departure time transferring elasticities of different passenger types under pre-peak discount pricing strategy of Beijing transit are also analyzed. This study improves the objectivity of passenger classification and provides method support for traffic policy formulation and operation strategy evaluation.

Key words: urban traffic, passenger classification, two-step clustering algorithm, automatic fare collection data, urban rail transit