Urban Rail Transit Fare Reconciliation Method Using Multi-Source Data

Abstract

Revenue reconciliation is an important problem in allocating the fare revenues to different lines and operators in urban rail transit systems. This paper proposes a data-driven fusion method for fare reconciliation in public transport using mobile signal, smart card, and train operation data. It makes the best use of the complementary advantages of two of these data sources in inferring the passenger travel paths within the metro system (mobile signal data) and journey time distributions of origin–destination (OD) pairs (smart card data). We propose a nonlinear programming optimization model to adjust the inferred path fractions from mobile data by minimizing the theoretically derived and truly observed OD journey time distributions. Case studies using both synthetic data and real-world data to validate the model performance for the metro system in Nanjing, China. The results show that the proposed information fusion model can well approximate the true path fractions and the observed OD journey time distributions. The model is robust against the biased model inputs, such as the priori path journey time distribution, with a relative path fraction estimation error of 3% for the biased standard deviation level up to 30%. In addition, the model performs consistently better than the current fare reconciliation practices using mobile signal data in estimating OD path fractions.