Splitting travel time based on AFC Data: Estimating walking, waiting, transfer, and in-vehicle travel times in metro system

Abstract

The walking, waiting, transfer, and delayed in-vehicle travel times mainly contribute to route's travel time reliability in the metro system. The automatic fare collection (AFC) system provides huge amounts of smart card records which can be used to estimate all these times distributions. A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. Finally, based on AFC data in Guangzhou Metro, the estimations show that all parameters can be estimated endogenously and identifiably. Meanwhile, the truncated property of the travel time is significant and the threshold tested by the surveyed data is reliable. Furthermore, the superiority of the proposed model over the existing model in estimation and forecasting accuracy is also demonstrated.