Spatial Autoregressive Model for Estimation of Visitors' Dynamic Agglomeration Patterns Near Event Location.

Takumi Ban,Toshiyuki Yamamoto,Tomotaka Usui

doi:10.3390/s21134577

Takumi Ban, Toshiyuki Yamamoto + Show 1 more

Open Access

https://doi.org/10.3390/s21134577

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Abstract

The rapid development of ubiquitous mobile computing has enabled the collection of new types of massive traffic data to understand collective movement patterns in social spaces. Contributing to the understanding of crowd formation and dispersal in populated areas, we developed a model of visitors’ dynamic agglomeration patterns at a particular event using dynamic population data. This information, a type of big data, comprised aggregate Global Positioning System (GPS) location data automatically collected from mobile phones without users’ intervention over a grid with a spatial resolution of 250 m. Herein, spatial autoregressive models with two-step adjacency matrices are proposed to represent visitors’ movement between grids around the event site. We confirmed that the proposed models had a higher goodness-of-fit than those without spatial or temporal autocorrelations. The results also show a significant reduction in accuracy when applied to prediction with estimated values of the endogenous variables of prior time periods.

Highlights

For Model 2, which considered the population in the target mesh at time t − 1, Akaike’s information criterion (AIC) was further improved over its values in Models 0 and 1
The circumstances of people moving in the area surrounding the event location were revealed from this data by plotting time series
After the closing of the event, the peak timestamp of population movement near event location showed some delay. Based on these observed delays and geometrical relations between meshes, we confirmed the movement of event participants who left the event location

Summary

Introduction

Several traffic accidents have occurred in the vicinity of certain public events. In such cases, participants’ movements do not follow normal traffic patterns, leading to an unexpected demand of transportation infrastructure. Pedestrian or automobile traffic jams frequently occur. During the Akashi pedestrian bridge accident of 2001, a stampede by a crowd of pedestrians caused a fatal accident [1]. It is likely that the pedestrian bridge became a bottleneck and unexpected congestion developed

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