Research on pedestrian escape route without visibility based on Markov chain model

Abstract

It is a pioneering work to use a Markov chain model to study the pedestrian escape route without visibility. In this paper, based on the Markov chain probability transition matrix, the algorithms with random numbers and the spatial-grid, an escape route in a limited invisible space is obtained. Six pace states (standing, crawling, walking, leaping, jogging, and running) are applied to describe the characteristics of pedestrian behaviors. Besides, eight main direction changes are used to describe the transition characteristic of a pedestrian. At the same time, this paper analyzes the escape route from two views, i.e., pedestrian pace states and directions. The research results show that the Markov chain model is more realistic as a means of studying pedestrian escape routes.

A Markov Jump Approach to Modeling and Analysis of Pedestrian Dynamics

“…Once a new state has been chosen, new target values for speed and direction are randomly picked up from two independent probability distribution. Although many scholars have made some i8mprovements to the Markov pedestrian model, they have not solved the inherent defects well [53]. For example, it can't adapt to the changeable walking environment, and show the subjective characteristics of the pedestrian [20].…”