Evaluating rail transit assignment models in the temporal dimension: The problem and its solution

Abstract

Passenger flow is the foundation for urban rail transit (URT) operations. However, its calculated results from assignment models may deviate from the actual situation in both spatial and temporal dimensions, which arouses more attention and needs to be evaluated in particular. On the other hand, onboard video data from URT trains provides a potential way for model evaluation. This study defines the evaluation problem and proposes a methodological solution for evaluating rail transit assignment models in the temporal dimension, including qualitative validation and difference quantification. A suitable time granularity is determined for the best effectiveness and onboard video data is used for actual passenger flow extraction. The gap between the actual and the calculated by the model is identified with nonparametric statistical techniques (NPSTs) and quantified with time series similarity measurement (TSSM) methods. A case study on the Shanghai metro demonstrates the performance of the proposed approach, and several practice implications for URT operation agencies are also discussed.