Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors

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.

A Theoretical Investigation on Training of Pipe-Like Neural Network Benchmark Architectures and Performance Comparisons of Popular Training Algorithms

“…Artificial neural networks have been widely used for the black-box modeling of the physical and biological systems and they have been implemented in the model-based prediction problems (Chen et al, 2020;Zhao et al, 2020). Due to higher level of complexity and chaotic dynamics, biological system modeling benefits from highly nonlinear function approximation skills of the artificial neural networks.…”

Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network

“…In previous works, trajectory position [15] and speed in the horizontal and vertical directions [13,20] of a pedestrian at the next time step are usually chosen as the outputs. In the study of Zhao et al [21], two submodels were developed to learn the magnitude and direction of pedestrian movement velocity, respectively. Inspired by this, two submodels are proposed to predict the velocity displacement and velocity direction angle of a pedestrian at each time step, respectively.…”

“…If unnecessary more neurons are present in the network, then "overfitting" may occur [22]. Followed by the "rules of thumb" used in the study of Zhao et al [21], the number of neurons in the hidden layer is estimated by the following equation:…”

“…While the pedestrian moves down the corridor from right to left, θ t i is the angle rotating from vector (0, 1) to direction vector v →t−Δt i , as shown in Figure 4. To quantify the spatial distribution of the neighboring pedestrians, square grids were usually used in previous studies [20,21]. In this submodel, we adopt a radial-based method to divide a pedestrian's perception area.…”

The influence of weather and temperature on pedestrian walking characteristics on the zigzag bridge

“…Congestion formation can vary widely in different architectural and geometrical contexts (Shi et al 2018 ). Many studies have identified the effects of straight corridors on the pedestrian flow (Ren et al 2019 ; Zhao et al 2020 ; Zhang et al 2011 ; Jin et al 2021 ; Heliövaara et al 2012 ). However, angled corridors exist in most walkways in public places.…”