Abstract
This study analyzed the performance of transit services using data collected from Automatic Fare Collection (AFC) system in Seoul with respect to service measures such as schedule adherence of metro, occupancy (crowdedness), and the operational speeds of buses. In order to analyze the transit services performances, we developed data-mining logics and applied them to a case study. These logics successfully indicate the route segment with a low Level of Service (LOS) for metro punctuality and bus operational speeds during peak hours (i.e., 7 a.m. to 9 a.m.). Further, a relationship between vehicle occupancy and punctuality on certain transit route segments is easily found based on the logical analysis. These empirical applications are beneficial to transit agencies and planners for improving transit service performances by using massive amount of data.
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