Impact of Traffic Sign on Pedestrians’ Walking Behavior

Hui Xiong,Pingfu Yao,Chenglong Chu,Wuhong Wang,Xuedong Guo

doi:10.1155/2015/826152

Abstract

To study the impact of traffic sign on pedestrian walking behavior, the paper applies cellular automaton to simulate one-way pedestrian flow. The channel is defined as a rectangle with one open entrance and two exits of equal width. Traffic sign showing that exit is placed with some distance in the middle front of the two exits. In the simulation, walking environment is set with various input density, width of exit, width and length of the channel, and distance of the traffic sign to exit. Simulation results indicate that there exists a critical distance from the traffic sign to exit for a given channel layout. At the critical distance, pedestrian flow fluctuates. Below such critical distance, flow is getting larger with the increase of input density. However, the flow drops sharply when the input density is over a critical level. If the distance is a little bit further than the critical distance, the largest flow occurs and the flow can remain steady no matter what input density will be.

Highlights

Pedestrian’s walking behavior is much more complicated compared with the vehicle traffic due to the fact that there is no designated trail to constrain pedestrians’ moving space
Instead of describing individual pedestrian’s behavior, this type of models treats crowd as a whole and applies the conservation law to capture the relationship among speed, flow, and density of pedestrian flow
Simulation results indicate that a critical distance of the traffic sign to exit exists for a given channel layout

Summary

Introduction

Pedestrian’s walking behavior is much more complicated compared with the vehicle traffic due to the fact that there is no designated trail to constrain pedestrians’ moving space. In the past several decades, modeling pedestrian flow has attracted considerable attention and numerous models were proposed. Pedestrian models can be in a macroscopic nature or microscopic nature. Macroscopic models are often in the form of partial differential equations. Instead of describing individual pedestrian’s behavior, this type of models treats crowd as a whole and applies the conservation law to capture the relationship among speed, flow, and density of pedestrian flow. Hughes [1] derived partial differential equations for flows with single or multiple pedestrian types. Colombo and Rosini [4] proposed another type of partial differential equation model for pedestrian flow with a new parameter called characteristic density that revealing the maximal density in panic. Jiang et al [5]

Objectives

Results

Conclusion