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Maritime University of Szczecin

Akademia Morska w Szczecinie

2010, 20(92) pp. 82–86 2010, 20(92) s. 82–86

Problems of opposite flow of people during evacuation

from passenger ships

Problemy ruchu przeciwbieżnego ludzi podczas ewakuacji

ze statków

Dorota Łozowicka

Maritime University of Szczecin, Faculty of Navigation, Institut of Marine Navigation Akademia Morska w Szczecinie, Wydział Nawigacyjny, Instytut Nawigacji Morskiej 70-500 Szczecin, ul. Wały Chrobrego 1–2

Key words: opposite flow, evacuation, danger Abstract

This article analyzes the problem of opposite flow that may occur during the evacuation of people from passenger ships. The causes of evacuation opposite flow and methods of its investigation are given. Besides, the methods of taking account of opposite flow in evacuation models are analyzed. It is proposed to search for the critical value of density depending on the number of persons moving in the opposite direction.

Słowa kluczowe: ruch przeciwbieżny, ewakuacja, niebezpieczeństwo Abstrakt

Artykuł porusza zagadnienie ruchu przeciwbieżnego, który może wystąpić podczas ewakuacji ludzi ze stat-ków pasażerskich. Podane są przyczyny i metody szacowania wpływu ruchu przeciwbieżnego na przebieg ewakuacji oraz sposoby uwzględniania tego ruchu w modelach ewakuacji. Przedstawione są teoretyczne za-łożenia do metody poszukiwania krytycznej wartości zagęszczenia osób poruszających się w przeciwnych kierunkach.

Introduction

Time indispensable for a complete process of evacuation from the vessel can be divided into a number of components:

– time indispensable for a recognition of the situa-tion

– time indispensable for a making a decision – time indispensable for movement to assembly

stations

– time for getting access to and launching life-saving appliances.

Each of the components is affected by many fac-tors, outlined in figure 1.

It is assumed in an analysis of the movement of people during evacuation that they will move in one specific direction. However, the process of evacua-tion is often disturbed so that the flow ceases to be ordered. One factor that may delay evacuation is

Evacuation

Recognition of situation

Making a decision

to start an action Movement

Abandoning vessel – alarm system efficiency – knowledge of alarm signals – experience of evacua-tors – familiarity with danger – access-ability of emergency exits – evacuation procedures – design factors – behaviour of people in emer-gency – efficiency of placing people in life-saving appliances – speed of launching life-saving appliances

Fig. 1. An outline of indispensable time components for carrying out the evacuation

The opposite flow of people is taken into account, to a different extent though, in evacuation analyses recommended by the IMO. Theoretical analyses of real trials of evacuating passenger vessels (aimed at model verification) are conducted by research institutions in cooperation with mari-time administration, industry and transport. The development of evacuation analyses is coordinated by the IMO. One of the two calculation models may be used for analysis – the simplified, or the advanced one. The simplified model takes account of opposite flow exclusively by a coefficient increasing the calculated time of evacuation. The advanced evacuation method consists in computer simulation of each person’s movement, taking consideration of the vessel’s construction system, mutual interaction of persons and the effect of the surroundings on their behaviour.

The verification of computer models is the basic condition of their development and usefulness in practical applications. It also includes quantitative verification. The opposite flow test is part of the quantitative verification (Test 8: Opposite flow – two rooms connected via a corridor).

To take an accurate account of opposite flow in evacuation models we should analyze the factors which cause opposite flow.

After passengers are notified of the need to evacuate, some of them do not proceed directly to their assembly stations. They tend to look for their family members or retreat to their cabins to pick up their belongings. Such behaviour may be a reason for opposite flow to occur.

The position [1] draws attention to the fact that life jackets are placed in the cabins on passenger ships, therefore passengers are forced to go to their cabins before proceeding to assembly stations. Such a scenario is quite realistic when the evacuation takes place at daytime when most passengers stay outside their cabins in public spaces. The risk of congestions is particularly high in narrow stair-cases.

Fire onboard is another reason for opposite flow formation. Firefighters move in the direction oppo-site to evacuees, thus the movement of the latter may be slowed down.

Methods of opposite flow investigation

A Ship Evacuation Behaviour Assessment faci-lity SHEBA, an evacuation simulator, was used for experimental research study of people moving in opposite directions. SHEBA provides data on the behaviour of people during evacuation. The data

were utilized for the development of EXODUS, a commercial simulator of ship evacuation.

The position [2] describes tests of opposite flow carried out by experiment and simulations (de-pending on density). The tests were performed in a tunnel 2 metres wide and 12 metres long, with no obstructions inside. The movement of students participating in the experiment was recorded by video cameras. The recorded movement of people is presented in figure 2.

Fig. 2. Diagram of simulated people movement records [2] Rys. 2. Schemat zapisu ruchu ludzi w badaniach symulacyj-nych [2]

The diagram (Fig. 3) shows the mean speed at which people move depending on their density. The experiment results have been compared with the models developed: original lattice gas model with no back step (model 1), simple lattice gas model with back step (model 2), original lattice gas model with back step (model 3).

Another type of experiment aimed at testing an opposite flow of people moving “on all fours”, which may take place when evacuation continues as the smoke layer under the ceiling intensifies so that evacuees cannot move in an upright position [3]. Figure 4 presents the scheme of the experiment.

In the publication [4], in turn, a simulated eva-cuation through escape routes from large spaces is presented. Evacuation time strongly depends on the position of a person occupies at the moment of starting the evacuation and on the egress width. The movement of people is a system with strong inte-ractions between individuals. In case of opposite flow, congestions appear as the density increases. Below the critical value of density people move smoothly passing others. When the density exceeds the critical value, blocked by others people are not able to pass. M N Left region N Center region N Right region

Fig. 4. Schematic presentation of the experiment [3] Rys. 4. Schemat eksperymentu [3]

Opposite flow in evacuation models

Løvås [5] proposes a number of methods for modelling person’s movement (random choice,

lity, memorized random choice, planned paths, directional choice, shortest local path, modified local path, shortest global path, frequently used path). Given below are assumptions for two selected models to show that opposite flow can be taken account of.

1. Random choice – the assumption is that the evacuee does not know the surroundings, (which does not mean he will make a “blind choice” as that person may combine his knowledge with a random choice). The number of evacuees is denoted as X, which can also represent the level of risk in a given vertex or arc of the system describing the lattice of escape routes. The term pk (i, j, X) denotes the

pro-bability that a person k with his personal attributes will move from vertex i to j, while the whole sys-tem is in state X. If the number of paths connected to vertex i is denoted as δi, then pk (i, j, X) = 1/δi.

2. Modified random choice – assumes a possi-bility that an evacuee will come back to his pre-vious path. In this case, if nk denotes the number

of last vertex visited by person k, 0 ≤ ω ≤ 1 is a parameter determining whether a person will return, then:





The study [6] defines a model of person’s movement, where the person is characterized by

Fig. 3. Mean speed of people moving depending on the density in opposite flow: a) arrival time, b) mean velocity [2]

Rys. 3. Średnia prędkość przemieszczania się osób w zależności od zagęszczenia w ruchu przeciwbieżnym: a) czas przybycia, b) średnia prędkość [2]

a) b)

0 0.1 0.2 0.3 0.4 0.5

Density [persons / site]

0 0.1 0.2 0.3 0.4 0.5

Density [persons / site]

Arriv al ti m e [s] M ea n ve lo cit y [m /s] Simulation model 1 Simulation model 2 Simulation model 3 Experiment Simulation model 1 Simulation model 2 Simulation model 3 Experiment 12 m t = 0 [s] t = 10 [s] t = 20 [s] t = 40 [s] 2 m

The values of these parameters are selected from experimental data. The experiments take account of the effect of opposite flow. The speed of persons’ movement decreases by as much as 30–60%.

An interesting concept of people’s movement using the theory of bionics is presented in [7]. It contains a description of “cellular automata” model of evacuation from large spaces with one or two evacuation exits based on the description of interactions between people. The model makes use of the chemotaxis process. Insects create trajec-tories to show the way to food to other individuals. People also create trajectories, but unlike insects, the human trajectory is virtual and exists only in human mind. The main assumption of the model is the division of a surface into cells (full or empty). Each individual may move to the adjacent empty cell in the time t → t + 1 with a specific probability. Certain phenomena connected with crowds are pointed out. Where opposite flow appears, there is a self-regulation of the flows of people moving in opposite directions. With opposite flow in bottle-necks, the shuttle movement takes place.

Use of the theory of genetic algorithms in modelling opposite flow movement

The genetic algorithms make up a method inspired by Darwin’s theory of evolution. This procedure used for finding the function maximum (minimum) consists in imitating live organisms, which through evolutionary processes such as natural selection and inheritance adapt to the changing natural environment. Each successive generation is better adapted than its predecessors, while weaker and worse adapted individuals have lower chances of survival and reproduction.

This author proposes to use the method of genetic algorithms for searching for the critical value of the density depending on the number of persons moving in the opposite direction. In the first step an initial population is created, consisting of a specific number of chromosomes. Each gene in the chromosome represents one evacuee and its attributed value 0 or 1 depending on at which end of the corridor he is.

For instance, for ten evacuees the chosen chro-mosome will have this form: [1000111000].

It represents a solution, in which 4 persons move in the direction opposite to 6 persons. The next step comprises the objective function, i.e. calculation of the times of movement and passing the other per-sons is based on one of the available models. Then, using genetic operations and making selections we search for such configurations of initial distribution

of the examined population for which movement will not be possible (moving people cannot pass through blocked by others).

The particular steps of the method proposed herein are given in figure 5.

Fig. 5. Diagram of the genetic algorithm operation Rys. 5. Schemat działania algorytmu genetycznego

During the optimization unfavourable scenarios of evacuation are determined, those leading to con-gestions. On this basis we can verify the layout of corridors, escape exits, staircases, lifts, the shapes of rooms, corridors and exits and the functions of spaces and rooms. The proposed procedures can serve for designing as well as reduction of the exist-ing narrow passages by appropriate modifications.

Summary

More accurate methods of evacuation time esti-mation can be developed by accurate analyses tak-ing account of a possibly large number of parame-ters that may affect the process of evacuation. The parameters important for safe evacuation are a sub-ject of research studies in many centres at home and abroad. To carry out correct analyses of evacuation time one has to draw upon various fields of know-ledge. A comprehensive approach allows to deter-mine accurately the time of evacuation by taking into consideration the parameters that may hamper evacuation, i.e. make it last longer.

This article, presenting the knowledge on the subject, proposes the method of looking for the critical value of density in relation to the number of persons moving in the opposite direction, or oppo-site flow. At present, many of the problems being solved require a lot of computing time and re-sources if satisfactory results are to be obtained. As the genetic algorithms, with simple methods of encoding and reproduction, turn out to be an effec-tive tool, they are widely used in finding solutions to complex problems. The genetic algorithms can

Creation of initial population Calculation of objective function Selection and reproduction

Based on a known model

Critical value of density exceeded

0 1 1 1 1 0 1 0 0 0 1 0 1 0 1

Assessment of new generation

be applied to solve a variety of problems, for which functions assessing the solutions can be formulated. Therefore, it is justified to use the presented method to solve the problem of opposite flow in evacuation.

References

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simu-lation of pedestrian counter flow. Physica 2004, A 336, 638–650.

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