Estimation of Load Factors of Trains Using Multi-source data for Complex Metro Systems

Metro systems play a significant role of urban transportation in large cities. It is critical for metro system managers to understand load factors of trains. Although the wide deployment of automated fare collection (AFC) system brings us a lot of convenience, only the information likes tap-in and tap-out time stamp and stations of each trip can be directly obtained from AFC records, the train and the route chosen by the passengers are uncertain. The information is necessary for achieving the load factors of trains. Considering that AFC records contain the no-transfer trips, one-transfer trips and multi-transfer trips, the problem becomes particularly complicated. Benefiting from machine learning, we propose an effective data-mining method to better understand the train and route chosen by a passenger with considering the impacts of time and space. We also classify passengers based on the similarity of passenger travel characteristics, which can automatically select the initial cluster centres in data. We validate our approach using a large-scale dataset collected from the Shanghai Metro system, and results demonstrate that our approach works effectively.