| description abstract | Understanding travel behavior decisions is a fundamental aim of transportation planning. However, data from surveys or travel diaries that were traditionally used for travel mode-choice modeling are costly and have certain inaccuracies and cover limited populations. Therefore, recently, smart card data collected from automated fare collection systems have gradually become more popular for travel behavior analysis and modeling, but relatively little attention has been paid to investigating the daily variability in travel behavior decisions using more than 1-year smart card data, apart for some descriptive studies. In this study, mode-choice behaviors in public transit were investigated in Seoul using 20-month smart card data to investigate the daily variability in the ratio of the number of subway passengers depending on origin and destination. For this aim, the effects of temporal features such as weather and calendar events as well as the route information and built environments of origin and destination stations were considered on a daily basis for different time periods. To overcome the limitation that the purpose of travel cannot be identified from smart card data, this study attempted to precisely estimate subway connections and extract travel records for commuting from regular commuters’ cards. The models were trained using 1-year data and were validated using 8-month data, which verified that the selected factors explain the daily variability in mode-choice behaviors for public transportation. | |
