Impact of bus alighting information on fine classification of transit riders.

The widely used bus one ticket system lacks off boarding information, and the passenger classification based on the dataset of one-ticket bus system needs to be refined to evaluate the classification effect. This study uses the large-scale bus ticket data with alighting information to compare and analyze the transit rider classification models with and without the alighting information to explore the impact of the missing alighting information on the fine classification of transit riders. The classification indexes and combined clustering method are adopted to refine the classification of bus passengers. A case study of Beijing conventional buses which contain boarding and alighting data of 28 consecutive days is presented and used to analyze the impact of bus alighting data and classification indexes on the transit rider classification results. The result indicates that both models successfully complete the fine classification of the riders and reflect the spatio-temporal characteristics of each type of riders. The model with alighting information is able to identify the distance-concerned type with few samples such as the long-distance transit riders accounting for 0.25%. The standard deviation of the average proportion of categories in the model without alighting information is much smaller than that in the model with alighting information. The results obtained from the model without alighting information are more balanced, and the regularity of various categories of passengers in multiple dimensions is significantly different, which can better reflect the macro characteristics of classification. [ABSTRACT FROM AUTHOR]