Mining smart card data for transit riders travel patterns
We proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders using the transit smart card data. Transit riders trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. A Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit riders historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities.
smart card automatic fare collection system transit travel pattern k-means algorithm rough set theory