Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

Menghang Liu,Qiang Li,Luning Li,Yu Bai,Cheng Hu

doi:10.3390/ijgi10070455

Abstract

Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.

Highlights

Accepted: 1 July 2021With the ever-enriching city lives, open public places, such as pedestrian streets, commercial streets, parks, and squares, have gradually become an important part of people’s lives [1]
We conduct a quantitative evaluation of the graph convolutional network (GCN) and six baseline models over the collected pedestrian flow and compare the changes of metrics results to validate the performance of the model
The model, in which the detectors are regarded as nodes, and edges represent the relationship of the road network, can capture the pedestrian flow characteristics hidden in the topological structure

Summary

Introduction

With the ever-enriching city lives, open public places, such as pedestrian streets, commercial streets, parks, and squares, have gradually become an important part of people’s lives [1]. These open places, without definite space boundary, are likely to cause overcrowding with the inrush of massive pedestrians in a short period, which could arise evacuation problems, leading to the occurrence of stampedes [2,3]. It is a great concern to understand the dynamics of pedestrian flow in open public places It can help city managers implement prevention strategies to alleviate road congestion, and provide useful information for travelers to choose appropriate travel routes and improve travel efficiency

Methods

Results

Discussion

Conclusion