###### Abstract  
Identifying the factors that control the dynamics of pedestrians is a crucial step towards modeling and building various pedestrian-oriented simulation systems. In this article, we empirically explore the influential factors that control the single-file movement of pedestrians and their impact. Our goal in this context is to apply feed-forward neural networks to predict and understand the individual speeds for different densities of pedestrians. With artificial neural networks, we can approximate the fitting function that describes pedestrians' movement without having modeling bias. Our analysis is focused on the distances and range of interactions across neighboring pedestrians. As indicated by previous research, we find that the speed of pedestrians depends on the distance to the predecessor. Yet, in contrast to classical purely anisotropic approaches--which are based on vision fields and assume that the interaction mainly depends on the distance in front--our results demonstrate that the distance to the follower also significantly influences movement. Using the distance to the follower combined with the subject pedestrian's headway distance to predict the speed improves the estimation by 18% compared to the prediction using the space in front alone.  
artificial neural networks; pedestrian dynamics; distance headway; single-file movement; interaction range; modeling +
Footnote †: journal: Applied  
Furthermore, classical pedestrian interaction models are anisotropic, assuming that people in front influence the dynamics more than people behind. For instance, most force-based models include vision field mechanisms affecting a weight depending on the bearing angle in the motion direction [2; 3; 5; 6]. This hypothesis, despite the reasonable limits of human perception and notions of fields of vision, is in most cases assumed a priori without statistical evidence. In this article, we analyze the interaction range in single-file movement, including isotropic symmetric interaction models based on the distance to pedestrians behind as well. Recently, artificial neural networks have been used successfully to estimate the speed of pedestrians in different complex geometries [7]. They allow identifying (with no modeling bias) which variables are relevant to the pedestrian by analyzing prediction errors. In this context, we investigate several factors influencing the dynamics, namely the interaction range with pedestrians in front and behind, and the isotropic nature of the pedestrian dynamics. Hereby, we focus our analysis on the influence of the distance to the follower, predecessor, and second predecessor pedestrian on the prediction of the subject pedestrian speed.  
The rest of this article is organized as follows. In Section 2, we review and discuss several approaches proposed by researchers to predict pedestrians' movement characteristics using different methods and techniques. In Section 3, the single-file movement experimental dataset is introduced, and the data pre-processing methodology is described. Section 4 presents the structure of the artificial neural networks applied to investigate pedestrians' movement influential factors to predict future speeds. In Section 5, the speed prediction results using different input features are discussed. Finally, we summarize the article, make conclusions, and propose future work in Section 6.