A stochastic diagnostic model for subway stations

Performance of subway stations is a critical problem that faces public transit authorities worldwide. Although replacing subway stations is very expensive, the Société de Transport de Montreal (STM) and most transit authorities lack planning strategies because they do not have deterioration models for their infrastructure. The presented research in this paper assists in developing a stochastic Global Station Diagnosis Model (GSDM). The GSDM identifies and evaluates the weights of different functional condition criteria for subway stations. It also utilizes the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) integrated with the Multi-Attribute Utility Theory (MAUT) and Monte Carlo simulation in order to determine a stochastic Global Diagnosis Index (GDI). Data were collected from experts through questionnaires and interviews. A case study of subway stations from the STM network is selected to implement the designed model. Results show that the GDI for the case study stations ranges from 5.6 to 7.8 with a 95% probability. Performing sensitivity analysis, the 'Alarm and Security' criterion is found to be the most effective criterion on the GDI. This research is relevant to industry practitioners and researchers since it provides a stochastic diagnostic tool for subway stations.