Artificial Neural Network Based Modeling on Unidirectional and Bidirectional Pedestrian Flow at Straight Corridors

TL;DR

This paper introduces a neural network-based pedestrian movement model that accurately predicts pedestrian trajectories and lane formation in straight corridors, validated through experiments for both unidirectional and bidirectional flows.

Contribution

The paper presents a novel neural network model with two submodels for predicting pedestrian velocities, effectively simulating unidirectional and bidirectional flows with validated experimental accuracy.

Findings

Model's predictions align well with experimental data.

Lane formation phenomena are successfully simulated.

Prediction errors are approximately 0.2m in unidirectional and 0.15m in bidirectional flows.

Abstract

Pedestrian modeling is a good way to predict pedestrian movement and thus can be used for controlling pedestrian crowds and guiding evacuations in emergencies. In this paper, we propose a pedestrian movement model based on artificial neural network. In the model, the pedestrian velocity vectors are predicted with two sub models, Semicircular Forward Space Based submodel (SFSB-submodel) and Rectangular Forward Space Based submodel (RFSB-submodel), respectively. Both unidirectional and bidirectional pedestrian flow at straight corridors are investigated by comparing the simulation and the corresponding experimental results. It is shown that the pedestrian trajectories and the fundamental diagrams from the model are all consistent with that from experiments. And the typical lane-formation phenomena are observed in bidirectional flow simulation. In addition, to quantitatively evaluate the precision of the prediction, the mean destination error (MDE) and mean trajectory error (MTE) are defined and calculated to be approximately 0.2m and 0.12m in unidirectional flow scenario. In bidirectional flow, relative distance error (RDE) is about 0.15m. The findings indicate that the proposed model is reasonable and capable of simulating the unidirectional and bidirectional pedestrian flow illustrated in this paper.