Abstract

Overcrowding in metro systems can exert a negative impact on the travel experience of the passengers and on the system's capacity. This can affect passengers in different ways, worsening their perception of safety, altering their behaviour, decreasing their satisfaction, and leading to higher travel-time sensitivities. One of the causes of overcrowding can be attributed to some metro cars being selected more often than others, and thus, the distribution of passengers inside the train not being homogeneous. This study adopts a disaggregated approach based on the observed behaviour to understand why some specific metro cars are preferred. The characteristics of passengers and metro cars that generate heterogeneity in the load profiles are identified and the motives behind passenger choices are understood by formulating a hybrid latent-class choice model. The behavioural models consider sociodemographic characteristics, level-of-service information, and attitudinal latent variables. Once a metro car choice model was obtained, an operational micro-simulator of the metro was programmed to evaluate how passengers' choices would be affected by altering variables related to the design of the stations, assessing it based on the current situation. Subsequently, interventions to station layouts are made based on the forecast of the behavioural models, to affect passenger choices and produce more homogeneous passenger distributions in the train. The intervention presented in this study reduced 46.49 h of total travel time during the morning peak hours; the impact was greater when considering individual perceptions. Incentives can be developed for optimising the distribution of passengers in metro cars by understanding passenger choice using a behavioural approach, and therefore, they can improve the passenger experience.