## 2 Related Work
[13] develop the Social LSTM (S-LSTM) algorithm to predict the future trajectories of pedestrians depending on their past positions and the interactions with their neighbors. To model the social interaction, [PERSON] uses a social-pooling layer to allow sharing each neighboring pedestrian's LSTM hidden state to predict the subject pedestrian's future positions. The [PERSON] et al. algorithm improved the prediction of the next position by a factor of approximately 21% compared to the force-based model (SF) [3]. [PERSON] et al. [20] develop a trajectory-prediction algorithm, called the Bi-prediction algorithm, based on the S-LSTM and considering the importance of pedestrians' intended destinations in predicting their future trajectories. This two-stage prediction model employs bidirectional LSTM architecture to forecast multiple possible trajectories with different probabilities in the scene. In other research [21], the authors propose the MX-LSTM model, which adds to the previous models a new variable (direction of the pedestrian head) to improve the trajectory predictions (the model improves the prediction by approximately 19% compared to the SF classical model). All the aforementioned data-based approaches have been used to describe low-density situations using specific datasets (UCY [22], ETH [18], etc.) where social interactions techniques for collision avoidance take up to several meters. Other researchers have focused on developing algorithms based on artificial neural networks to predict a pedestrian's speed. For instance, the study proposed by [PERSON] et al. [7] applies feed-forward neural networks (FFNN) to predict the speed of pedestrians walking on different types of facilities (corridors and bottlenecks). Several FFNNs are presented to approximate the fitting function with different combinations of input features (relative positions, relative velocities, and mean distance to the nearest ten neighbors in front), hidden layers, and hidden neurons. The results of FFNN show improvement by 20% compared to the classical approach (Weidmann fitting model [23]) evaluated with mixed data (corridor and bottleneck). In another study by [PERSON] et al. [24], the authors develop a system that simulates pedestrians' behavior during the evacuation process. The proposed system uses FFNN to predict how people act during evacuations. The acceleration and average velocity are used to predict each pedestrian's horizontal and vertical speeds. Another study by [PERSON] et al. [25] proposes an approach based on a multilayer perceptron artificial neural network for simulating pedestrians' behavior. The authors train the artificial neural network using pedestrians' actual movement data to encapsulate and predict their future behaviors. To verify the correctness of the proposed simulation system, the authors compared the simulation results of pedestrian counter-flow in a road-crossing situation and pedestrian collision avoidance with the actual experiments. The simulation results in both studies show that the proposed models based on artificial neural networks provide greater prediction accuracy by learning from actual experimental data rather than other models. For brevity's sake, our focus in this article is to apply a FFNN to investigate and analyze empirically the impact of distance interaction range on dynamics of pedestrians without modeling bias. Unlike most current research work, we aim to analyze single-file movement in different homogeneous and heterogeneous gender flows to predict the pedestrian's speed.