Dealing with Emergencies: The Case of a Heavy Disruption of the Mexico City Metro System

Abstract

The paper presents the results of a forecasting model associated with the affluence of users of the metro line-B of Mexico City's metro system. It also presents in a way a retrospective analysis of the metro incident that occurred on September, 2011, in the same metro line; the incident affected seven metro stations and about 17 thousand commuters. The approach has been the use of Artificial Neural Networks (ANN). The main conclusions may be summarized as follows: (i) the metro incident has illustrated the fact that different modes of urban transport are highly interdependent; (ii) the proposed ANN model has the potentiality to be used to forecasting the affluence of users for any metro line for the case of Mexico City's metro system; (iii) the above (ii) can be used as input to the decision process in order to implement the required number of coaches to assist the affected commuters; (iv) Both (ii) and (iii) should be part of an emergency response plan to mitigate the impact of cascading failures due to interdependencies amongst the different modes of urban transport.