Passenger flow modeling and station dwelling time estimation are significant elements for railway mass transit planning, but system operators usually have limited information to model the passenger flow. In this paper, an artificial-intelligence technique known as fuzzy logic is applied for the estimation of the elements of the origin-destination matrix and the dwelling time of stations in a railway transport system. The fuzzy inference engine used in the algorithm is based in the principle of maximum entropy. The approach considers passengers' preferences to assign a level of congestion in each car of the train in function of the properties of the station platforms. This approach is implemented to estimate the passenger flow and dwelling times of the recently opened Line 1 of the Panama Metro. The dwelling times obtained from the simulation are compared to real measurements to validate the approach.

In this work a methodology for the management of partial services on Line 1 of Panama Metro is proposed. To validate this proposal results are presented in a series of simulations that allow characterizing the model proposed with this methodology at a railway engineering level.

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The application of engines in confined spaces requires configurations and algorithms to properly manage the effects of saturation. These nonlinear effects depend on the distance to walls and the water surface. This paper presents an algorithm for real-time address these effects based on redundant configurations are proposed boosters.