Modeling Temporal Flow Assignment in Metro Networks Using Smart Card Data

Understanding passenger flow assignment patterns in a complex metro network is crucial to maintaining service reliability and developing efficient response during disruption. In reality passengers' perception of different cost attributes may vary with time. This paper focuses on quantifying the temporal variation of passenger route choice behavior and its impact on overall passenger flow assignment. In order to efficiently estimate model parameters, we modify a previous model to a missing data problem by introducing latent variable on route choice outcomes for each travel time observation. The revised model can be estimated using the Expectation-Maximization (EM) algorithm. We apply the proposed framework on Singapore's metro system and temporal grouped smart card transactions. We find that route choice coefficients vary substantially with time. The relative value of transfer time in terms of in-vehicle time ranges from 2 to 3, being higher at off-peak hours than during morning/evening peaks. The result suggests that passenger care more about total travel time during peak hours, whereas comfort (e.g., less transfer time) is of more concern to users during off-peaks. The proposed framework is general and can be applied on other networks.