In the field of transportation planning and management, passenger flow analysis is a significant problem with a wide range of applications. The prediction performance of forecast models is hence cardinal to any software analytic system. A predominant source of metro data is the automated fare card (AFC) system from which it is possible to gather a tremendous amount of information connected to passenger flow. Passenger flow represents a process whose dynamics are highly stochastic and dependent on a number of extrinsic and intrinsic parameters. This paper presents a restricted and simple model to study the intrinsic statistical influences governing the dynamics. These influences are either spatial or temporal. The feature space in which analysis algorithms run will be more effective if there is a collation of information from both spatial and temporal dimensions. The passenger flow parameter is fed into the layers of the deep neural network using the ST-LSTM (Spatio-Temporal Long Short-Term Memory) architecture. The architecture is evaluated with passenger movement data collected from the AFC information from the Kochi metro rail. To reduce the impact of irregular flow, the design uses the SVM-based outlier detection and elimination algorithm. A higher precision has been reached by the approach in comparison with SVR,ANN, LSTM algorithms.