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Pedestrian flow parameters are analysed in this study considering linear and non-linear relationships between stream flow parameters using conventional and soft computing approach. Speed-density relationship serves as a fundamental relationship. Single-regime concepts and deterministic models like Greenshield and Underwood were applied in the study to describe bidirectional flow characteristics on sidewalks and carriageways around transport terminals in India. Artificial Neural Network (ANN) approach is also used for traffic flow modelling to build a relationship between different pedestrian flow parameters. A non-linear model based on ANN is suggested and compared with the other deterministic models. Out of the aforesaid models, ANN model demonstrated good results based on accuracy measurement. Also these ANN models have an advantage in terms of their self-processing and intelligent behaviour. Flow parameters are estimated by ANN model using MFD (Macroscopic Fundamental Diagram). Estimated mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are 3.83 and 4.73 m/ min, respectively, less than those for the other models for sidewalk movement. Further estimated MAE and RMSE values of ANN model for carriageway movement are 4.02 and 4.98 m/min, respectively, which are comparatively less than those of the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters. Also when using linear regression model between observed and estimated values for speed and flow parameters, performance of ANN model gives better fitness to predict data as compared to deterministic model. R value for speed data prediction is 0.756 and for flow data prediction is 0.997 using ANN model at sidewalk movement around transport terminal.

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