FP7 Contract Number: 233786 - Transfeu · 16/11/2012 – Version Final ... WP5 – Development of...

TRANSFEU-WP5-D5.4

16/11/2012 – Version Final Security: Confidential /50

Medium scale Collaborative project

TRANSFEU

Transport Fire Safety Engineering in the European Union

FP7 Contract Number: 233786

WP5 – Development of numerical simulation tools for fire performance, evacuation of people and decision

tool for the train design

D5.4 – Numerical tool for simulation of the passenger's evacuation for the train scenarios

Document Information

Document Name: TRANSFEU-WP5-D5.4-Numerical tool for simulation of the

passengers evacuation for the train scenarios Document ID: TRANSFEU-WP5-D5.4 Version: Final Version Date: 16/11/2012 Authors: Terhi Kling (VTT), Joonas Ryynänen (VTT), Tuula Hakkarainen

(VTT), Esko Mikkola (VTT), Antti Paajanen (VTT), Simo Hostikka (VTT)

Security: Confidential

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Approvals

Name Organization Date Visa

Coordinator Alain Sainrat LNE 16.11.2012

Scientific panel Scientific Panel TRANSFEU 16.11.2012

Document history

Revision Date Modification reviewer version1 8/10/2010 Introduction to evacuation simulation

version2 18/2/2011 More analytical models added

Andreas Braetz (Siemens) Henning Weigel (Siemens)

version3 8/6/2011 Corrections according to review of Siemens Validation with train data added Description of the train scenarios 1A added

version4 13/9/2011 Description of the train scenarios 2B added Simulation results added

version5 3/1/2012 Last coach scenario added to 2B train Harmonization with the other wp’s Summary added

Eric Guillaume (LNE) Heinz Reimann (BT)

version6 23/2/2012 Corrections and additions made according to review by LNE and BT

Final 16/11/2012 Approval by Scientific Panel Scientific Panel

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Content

Section I - Executive summary .......................................................................................4 I.1 Description of the deliverable content and purpose .................................................................. 4 I.2 Brief description of the state of the art and the innovation brought .......................................... 4

I.3 Deviation from objectives ............................................................................................................ 5 I.4 If relevant: corrective actions ...................................................................................................... 5 I.5 Intellectual property rights ........................................................................................................... 5

Section II - Methodology ..................................................................................................6

II.1. RSET (Required Safe Escape Time).......................................................................................... 6 II.2. Analytical models for travel time tT ............................................................................................. 6

1. Basic equations ..................................................................................................................6 2. Togawa model....................................................................................................................7 3. Melinec and Booth model ...................................................................................................7 4. Pauls model .......................................................................................................................8 5. Predjetschenski and Milinski model ....................................................................................8

II.3. Simulation models ........................................................................................................................ 9 II.4. FDS+Evac .................................................................................................................................. 10

1. Agent movement model .................................................................................................... 11 2. Fire Human interaction .................................................................................................. 12 3. Exit selection .................................................................................................................... 13 4. Stochasticity ..................................................................................................................... 14 5. Other features .................................................................................................................. 14 6. Present limitations ............................................................................................................ 15

Section III - Verification and validation ..........................................................................17

III.1. FDS+Evac .................................................................................................................................. 17 1. Verification ....................................................................................................................... 17 2. Validation in the literature ................................................................................................. 17 3. Validation with train evacuation data ................................................................................. 19

Section IV - Simulations ..................................................................................................26

IV.1. Scenarios .................................................................................................................................... 27 1. Train scenario 1A, evacuation scenarios a and b .............................................................. 27 2. Train scenario 2B, evacuation scenarios a and b .............................................................. 28

IV.2. Data, parameters and methods ................................................................................................ 29 1. Train geometry ................................................................................................................. 29 2. Distribution and properties of the passengers ................................................................... 29 3. Detection time and passenger alarm................................................................................. 30 4. Reaction time ................................................................................................................... 30 5. Dwell time ........................................................................................................................ 31 6. RSET calculation .............................................................................................................. 32 7. Sensitivity analysis ........................................................................................................... 32

IV.3. Simulations with FDS+Evac ...................................................................................................... 33 1. Train scenario 1A, evacuation scenario a ......................................................................... 33 2. Train scenario 1A, evacuation scenario b ......................................................................... 35 3. Train scenario 2B, evacuation scenario a ......................................................................... 39 4. Train scenario 2B, evacuation scenario b ......................................................................... 42

Section V - Results and discussion ..............................................................................46

References 47

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Section I - Executive summary

I.1 Description of the deliverable content and purpose In task 5.4 we were studying the simulation of evacuation of people from trains during a fire. The goal was to develop a simulation tool for the prediction of the RSET (Required Safe Escape Time). RSET is the time required to perform a safe escape from the place of danger to a place of safety. Classically RSET is divided into detection time, alarm time, response time and travel time. The detection and alarm times mainly depend on stochastical (e.g. type/size of fire) and technical (e.g. detection and alarm system) factors. The response time is influenced by social and individual factors. The travel time is mainly dependent on the geometry and the amount and walking speed of the escaping passengers. Travel times can be calculated analytically by simple formulas describing a flow through a door or by more complicated ones that take into account more details like stairs or congestion, but to be able to take into account different geometries and the escape of individuals with different physical and psychological properties, simulation is needed. There are several simulation models that could be used to simulate the escape of passengers from trains, but very few simulations of train evacuations can be found in the literature. We decided to use FDS+Evac, because it is inside fire simulation model FDS, which is used for trains in TRANSFEU project. The coupling between the two programs makes it possible to use the same geometries and to take into account the fire effects in the evacuation simulation. Because of the project schedule (evacuation modelling was scheduled before fire modelling), we were not able to make any coupled simulations, but we used same detailed train geometries for the simulation of RSET that are being used for the ASET (Available Safe Escape Time) simulations (fire modelling) by FDS. FDS+Evac is an agent based simulation model, which treats each evacuee as a separate entity (agent), with its own personal properties and escape strategies. The equation of motion of each agent is solved in a continuous 2D space and time. The forces acting on the agents consist of both physical forces, such as contact forces and gravity, and psychological forces exerted by the environment and other agents. FDS+Evac is a stochastic model and takes into account the detection and reaction times of the passengers by user given distributions.

This report presents a variety of methods (analytical and numerical) for the determination of RSET. FDS+Evac is presented in detail with validation studies and example simulations. The simulation examples are chosen specifically for the purposes of the TRANSFEU project, and they don’t cover all the possible situations that can be needed in practical situations. It is necessary to make new simulations for specific geometries or situations. The examples presented here are the evacuation of a commuter train and a double decker, at a station and between stations. For each case several sensitivity studies are made to analyse the effect of having more passengers, different fire locations, people carrying suitcases or different heights of the exit step.

I.2 Brief description of the state of the art and the innovation brought Compared to analytical models, simulation gives more possibilities to look at the effects of different parameters on the evacuation times. There is a wide variety of evacuation simulation models, but very little experience about using them for trains. In this work some

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validation has been done with data about the effects of different exit step heights and luggage carrying, but further validation and data would be needed especially about the behaviour of people in train evacuation situations, moving speeds of passengers in narrow train environments as well as the effect of braking. Present model gives tools to take into account the detection and reaction time distributions, varying diameters and walking speeds of different passenger types as well as different geometries in the calculation of RSET. When using coupled fire and evacuation modelling with realistic yields of toxic substances also realistic exposures of the passengers can be studied. I.3 Deviation from objectives There is no deviation from the objective, which was to develop a numeric tool to estimate RSET for trains. I.4 If relevant: corrective actions No corrective actions are needed. I.5 Intellectual property rights

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Section II - Methodology

II.1. RSET (Required Safe Escape Time) RSET is the time required to perform a safe escape from the place of danger to a place of safety. The RSET value is classically divided into sub-times that are added:

Detection time tD, which is the delay required to detect the fire, either by automatic device or by people

Alarm time tA, which is the delay for the alarm process to be launched and completed

Response time tR, which is the delay for the people to understand the alarm signal, understand its importance, decide to leave their current activity and begin to evacuate

Travel time tT to the place of safety

The detection time and alarm time mainly depend on stochastical (e.g. type/size of fire) and technical (e.g. detection and alarm system) factors. Furthermore, the response time is influenced by social and individual factors. Those factors are not covered by the analytical model of some numerical simulation tools. They have to be determined by other means (e.g. by fire growth simulation or experimentally). Thus, tD, tA, tR will simply be added to the travel time tT which is calculated by the application of the analytical model of the egress simulation tool. In some egress simulation tools, like FDS+Evac [1] detection, alarm and reaction/response times can be included in the calculation through stochastical distributions, that are given by the user. RSET = tD + tA + tR + tT (1) In order to estimate RSET, software requires information such as:

What is the evacuation strategy and scheme?

What are the places of safety, their status (relative, absolute) and the means to reach these places?

Geometry of the place to evacuate and the evacuation routes (including presence of slopes, stairs, doorways, and anything else that can modify the flow of people that are evacuating)

Type, number and position of people that evacuate (including physical and mental abilities, age pattern, social links etc.)

Distributions of the stochastical variables (detection time, alarm time, response time, walking speed, diameter of the people)

II.2. Analytical models for travel time tT

1. Basic equations

The flow of people F (pers/s) through an exit can be calculated as follows

WDvF 2

where v is the walking speed (m/s), D is the density of people (pers/m2) and W is the width of the doorway (m). The same thing is often expressed using the specific flow of people Fs (1/ms)

DvFs 3

The escape time te (s) that is needed for all people to go through an exit can be calculated as follows

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WFNt

se

4

where N is the number of people in the building.

To be able to calculate the tT for a room with one exit we need to add here the time for the first person to go to the exit tw (s), which is

vLtw

5

where L is the distance of the first person from the exit. Now we get the time required for escape.

ewT ttt 6

2. Togawa model Equation (6) is almost the same as the empirical Togawa [2, 3] equation for the total evacuation time Te (s)

Tse v

LWF

NT

7

where vT is the walking velocity of the crowd (m/s)

8.00DvvT 8

where v0 is unimpeded walking speed (1.3 m/s).

Togawa equation can be used for simple evacuation calculation of one carriage by assuming that people choose the nearest exit and dividing the interior of the carriage to sections with one door each. Now the tT is the longest Te of these sections.

3. Melinec and Booth model

Melinec and Booth [4] suggested an empirical model specifically for calculation of a tall building. The minimum evacuation time Tr (s) for the population of floor r and above can be calculated as follows [1b]

sis

n

rii

r rtWF

NT

9

where

Ni = Population of floor i

Wi = Staircase width between floor i-1 and i (m)

Fs = Specific flow of people down the stairs (1/ms)

n = number of floors

ts = Unimpeded walking time (s) from one floor to another (usually 16 s)

Melinec and Booth equation could be used for an evacuation calculation of two-storey railway carriage with n = 2.

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4. Pauls model

Pauls [5] developed a model that takes into account the effective width of a doorway. He noticed that

1. The usable "effective" width of a doorway starts at 150 mm distance from the wall or at 88 mm distance from a handrail.

2. The average dependence between the flow of people and the width of the stairs is linear

3. The average specific flow of people is non-linearly dependent on the amount of the escaping people compared to the effective width of the stairs.

Pauls equations for the specific flow of people Fs and the total evacuation time Te (min) are the following

27.0* 206.0 es NF 10

73.0081.068.0 ee NT )/800( mpersNe 11

ee NT 0133.070.0 )/800( mpersNe 12

5. Predjetschenski and Milinski model

Predjetschenski and Milinski [6] used equations with a density measure based on a ratio of the projected horizontal area of people in a crowd divided by the area of walkway surface

lWfND0

13

where

D0 = Dimensionless human density at the exit

N = Number of people in the building

f = Average area of a projection of a person

W = Width of the doorway

l = Length of the queue

They also used flow capacity Q (m2/s) and specific flow q (m/s)

WvDQ 0 14

vDq 0 15

where v (m/s) is the evacuation speed.

For the escape to be effective, the flow between successive rooms need to be continuous with Qi = Qi+1. If the width of the doorway after the room i+1 is smaller than the width of the doorway between the rooms i and i+1, q grows until it achieves a maximum value qD0,max , with maximum human density D0,max. If the width of the doorway after the room i+1 is smaller than that, a congestion will form with a speed v'

i

ii

iD

DD

qW

Wq

v,0max,0

1max,0

'

16

When the last person has arrived to the congestion point, the congestion will decrease with a speed v''

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i

iD b

bvv 1

max,0''

17

This model could be used for studying the congestion between the railway carriages in a situation where the evacuation takes place from one carriage to another before side evacuation.

.

II.3. Simulation models Different kinds of numerical models have been developed for the evacuation simulation during the last 30 years. Some examples are presented here:

BFIRES II [7] is a dynamical stochastic model that was developed at the United States almost 30 years ago. It can be used for simulation of escape during a fire.

EVACNET+ [8] is a network optimization model that can be used to find the fastest escape routes for the people in a building during a fire.

EXITT [9] is a deterministic model that nowadays is one of the modules in the HAZARD 1 simulation program.

SIMULEX [10] is an agent model that was developed at the University of Edinburgh in the middle of 1990s. It is able to simulate the escape of large amount of people from complicated buildings. It has been successfully applied for areas up to 50 000 m2 with up to 15 000 people. An agent model enables the escaping people to have individual properties and escape strategies. It has a graphic interface.

EXODUS [11] is an agent model that was originally developed at the University of Greenwich for the evacuation of airplanes (airEXODUS), but the new version buildingEXODUS is especially designed for buildings. It also has a graphic interface.

ASERI [12] was developed in co-operation between a German consulting firm (Integrierte SicherheitsTechnik GmbH) and a Norwegian research institute (SINTEF). Also a German fire college (Sachsen-Anhalt) participated in the development. It is an agent model with graphic interface and the building can be presented 3-dimensionally.

FDS+Evac [1] is an agent based evacuation simulation model that is embedded inside the FDS fire simulation program [13]. FDS+Evac was developed at VTT Technical Research Centre of Finland. Fire and evacuation simulations can be made simultaneously (coupled fire evacuation interaction) or separately and the results can be visualized 3-dimensionally using Smokeview program [14].

PedGo [15] is a multi-agent simulation model based on data gained by the research project BYPASS [16]. Individual and stochastical factors influence the agents’ way finding behaviour. PedGo is a fast processing simulation tool and allows 2D or 3D visualization. It is certified by the International Maritime Organization and the RIMEA project [17].

STEPS [18] is an optimization evacuation model, including queuing. The model supports travel through a variety of egress routes as generated within the model for simulation by the modeller. People types and groups can be established with different speeds and behaviours.

There are some examples of train evacuation simulations in the literature:

Kangedal and Nilsson [19] applied SIMULEX [10] to simulate evacuation times of a two-car train suitable for intercity and interregional travel. Two different cases, with different times until the doors open, were studied. In the first case the train was assumed to stop and the doors were opened as soon as possible, when the fire was detected onboard. In the second case, the doors of the train were opened 15 minutes after the ignition. The main limitation of the SIMULEX program in this context wass that it could not take into account the climb of approximately 1.3 meters to the railway embankment. Therefore hand calculations were made to complement the results.

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Because of some limitations (vertical movement cannot be simulated) of the program, hand calculations were made to complement the results.

Klügl et al.[20] presented an agent-based evacuation model applied to a particular scenario where trains are stopped in a tunnel due to a fire. The simulation objective was to test emergency layout under realistic conditions. It turned out that agent-based approach with agents capable of communication and flexible decision making is necessary for tackling this simulation objective. Thus, this contribution described a successful application that would not be possible with simpler crowd simulation approaches.

Capote et al. [21] investigated evacuation of passengers from different fire scenarios and several evacuation conditions. The analysis was made using the egress model STEPS [18]. The analysis was divided to two stages of the evacuation process considering two different high speed trains : 1) The movement and behaviour of passengers in fire scenarios inside the vehicle before the train stops, and 2) The analysis of train evacuation under different conditions. The results allowed to determine the influence of the limitations of the different train geometries under different evacuation conditions, give an estimation of the evacuation times and analyse the impact of human parameters considered in the evacuation process.

Capote et al. [22] presented a stochastic evacuation model specifically for high speed trains. They proposed an object oriented model in which passengers are represented using a cellular automata method and the train space by a fine network of 0.5 m x 0.5 m cells. The model was based on Monte Carlo –methods in order to simulate the probability and effects of passengers’ actions and decisions during the evacuation process. The stochastic variables were as follows :

- Unimpeded walking speed Ws

- Personal response time Tpr

- Time to prepare blocking the aisle T1 ; Probability of occurrence of T1

- Time to pick up baggage T2 ; Probability of occurrence of T2

- Personal exit flow T3 – the time spent by each passenger to negotiate the exit steps

The datasets used as default by the model were taken from video recordings of evacuation drills and virtual experiments conducted at the University of Cantabria.

Capote et al. [23] used evacuation modelling to study the impact of crew procedures on evacuating two high-speed trains under different fire scenarios. Input data for the simulations is obtained from an evacuation drill. The paper analyses the effects of passenger pre-evacuation activities and train crew procedures. For each scenario multiple simulations are carreid out to capture the stochastic variations in egress times. Finally, recommendations for managing emergencies are given.

II.4. FDS+Evac We decided to use FDS+Evac [1] for the train evacuation simulations, because FDS is being used for the fire simulations in this project. Connected fire and evacuation simulation makes it possible to take into account the effect of fire related conditions to the evacuation and to study the actual dose of toxic substances that the people are being exposed during the fire and evacuation. It is also practical, that the same geometry can be used for both fire and evacuation simulations.

FDS+Evac is an agent based simulation model for evacuation of humans. It treats each evacuee as a separate entity (agent), which has its own personal properties and escape strategies. The equation of motion of each agent is solved in a continuous 2D space and time. The forces acting on the agents consist of both physical forces, such as contact forces and gravity, and psychological forces exerted by the environment and other agents.

The evacuation module is embedded inside the FDS [13], and the fire and evacuation processes can be simulated simultaneously or separately. Input is given through a text file and the results can be visualised 3-dimensionally using the Smokeview program [14]. FDS+Evac is available for free; the source code is in the public domain: http://fire.nist.gov/fds/. The home page for FDS+Evac is http://www.vtt.fi/proj/fdsevac/ where you can find the manual [1] and information on the last changes. The home page also contains several validation and verification test cases.

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1. Agent movement model

In the agent movement model the shape of the human body is approximated by a combination of three overlapping circles (Figure II-1) [24]. Pre-defined and user specified human types can be used. The body diameter and moving speed distributions for the pre-defined types Male, Female, Child and Elderly categories (Table II-1) are the same as in the Simulex program [10].

Figure II-1 : The shape of the human body in the agent movement model

Table II-1 : Pre-defined human types

Human movement is based on the "panic" model of Helbing et al. [25, 26], where a so-called "social force" is introduced to keep reasonable distances to walls and other agents (Figure II-2). FDS+Evac uses the laws of mechanics to follow the trajectories of the agents during the calculation. Each agent follows its own equation of motion

)()()(

2

2

ttfdt

txdm ii

ii

where xi(t) is the position of the agent i at time t, fi(t) is the force exerted on the agent by the surroundings, mi is the mass, and the last term i(t) is a small random fluctuation force. The velocity of the agent vi(t) is given by dxi/dt.

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Figure II-2: The "social force" model.

The force on the agent i has many components

k

attik

w

ciw

sociw

ij

attij

cij

socijii

i

ii ffffffvv

mf 0

where the first sum describes agent-agent interactions, the sum over w describes agent-wall interactions, and the term in the last sum, att

ikf , may be used for other agent-environment interactions like the fire-agent repulsion. The first term on the right hand side describes the motive force on the evacuating agent. Each agent tries to walk with its own specific walking speed, 0

iv , towards an exit.

The relaxation parameter i sets the strength of the motive force, which makes an agent to accelerate towards the preferred walking speed.

Each agent has also its rotational equation of motion

ttMdt

tdI z

izi

izi 2

2

where ti is the angle of the agent i at time t, tzI is the moment of inertia, tz

i , is a small random

fluctuation torque, and tM zi is the total torque exerted on the agent by its surroundings.

isoci

ci

zi MMMM

where ciM , soc

iM and iM are the torques of the contact, social, and motive forces, respectively.

2. Fire Human interaction

Fire influences evacuation conditions; it may incapacitate humans and block exit routes. On the other hand humans may influence the fire by opening the doors or actuating various fire protection devices. For now, the effect of smoke on the movement speeds of agents and the toxic influence of the smoke are implemented in movement algorithm of FDS+Evac. The exit selection algorithm of the agents uses smoke density to calculate the visibility of the exit doors and to categorise the doors to different preference groups. The smoke density can also be used to trigger the detection of fire in addition to the user given detection time distribution.

The walking speed in smoke is reduced in FDS+Evac along the lines given by experiments conducted by Frantzich and Nilsson [27]. It is assumed that the relative walking speed in smoke is the same for

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all agents whilst the unimpeded walking speed during the absence of smoke is an agent-individual value. Thus FDS+Evac reduces the walking speed of an agent i in smoke, si Kv0 , using the formula

siisi KvvMaxKv 1, 00min,

0

where the minimum walking speed of agent i is 00min, 1.0 ii vv by default, i.e., the agents are not

stopping due to a thick smoke, they continue to move with a slow speed until they are incapacitated by the toxic effects of the fire products. Ks (1/m) is the extinction coefficient and the values of the experimental parameters are =0.706 m/s, = -0.057m2/s.

The toxic effects of gaseous fire products are treated by using Purser's Fractional Effective Dose (FED) concept [28]. The present version of FDS+Evac uses only the concentrations of the gases CO, CO2, and O2 to calculate the FED value as

22 OCOCOtot FEDHVFEDFED

where

tCFED COCO036.1710607.4

where t is the time in seconds and CCO is the CO concentration (ppm). The fraction of an incapacitating dose of low O2 hypoxia is calculated as

2

2 9.2054.013.8exp60 OO C

tFED

where t is time in seconds and 2OC is the O2 concentration (volume per cent). The carbon dioxide

induced hyperventilation factor is calculated as:

1.70004.2193.0exp

2

2

COCO

CHV

where 2COC is the CO2 concentration (percentage). An agent is considered to be incapacitated when

the FED value exceeds unity.

Fire-human interaction is also taken into account in the exit selection algorithm, which is explained in the next chapter.

3. Exit selection

FDS+Evac uses game theoretic reaction functions and best response dynamics to model the exit route selection of evacuees [29, 30, 31]. Each evacuee observes the locations and actions of the other evacuees and selects the exit through which the evacuation is estimated to be the fastest. The estimated evacuation time consists of walking time and the estimated time of queuing, which is a function of the actions and locations of other evacuees. There are also other factors that influence the decision making of an agent: fire related disturbing conditions, the familiarity of the exit, and the visibility of the exit. According to these factors the exits are divided to groups that are given an order of preference (Table II-2). The familiarity of each exit for each agent can be determined in the input file. The visibility of an exit is determined by taking into account the blocking effect of the smoke and obstacles. The existence of disturbing conditions is estimated from the fire related data of FDS on the visible part of the route to the exit.

The exit selection algorithm consists of two phases: first the exits are divided to the preference groups, then an exit is selected from the most preferred preference group by minimising the estimated evacuation time.

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PREFERENCE VISIBLE FAMILIAR DISTURBING CONDITIONS

1 yes yes no 2 no yes no 3 yes no no 4 yes yes yes 5 no yes yes 6 yes no yes

No preference no no no No preference no no yes

Table II-2 : Order of preference of exits.

4. Stochasticity

The evacuation part of the FDS+Evac is stochastic, i.e. it uses random numbers to generate the initial positions and properties of the agents, including detection and reaction times. In addition, there is a small random force on each agent's equation of motion. For this reason, one should always do several egress simulations to see the variation of the results. To speed up this process, several egress calculations can be done per one fire simulation and the calculation of the evacuation flow fields used to guide agent movement need to be calculated only once for each given geometry.

The following statistical distributions can be used:

Uniform

Truncated normal

Gamma

Normal

Log-normal

Beta

Triangular

Weibull

Exponential

Gumbel

The model can also be linked with an Excel-based PFS-tool (Probabilistic Fire Simulation) [32] to create Monte Carlo -simulations, with automatic analysis tools. This makes it possible to add more stochasticity in the scenario; for example distributions could be used for:

Fire location, HRR (heat release rate), yields of the toxic gases

Number and type of the people

Stopping time of the train

Reliability of operation of detection, alarm or doors

Using stochastic approach makes RSET and ASET stochastic variables. As a result of the study we get a probability for RSET< ASET and a question arises: "Is this an acceptable risk level?". Stochastic approach can be modified back to deterministic by using average values for RSET and ASET.

5. Other features

Counterflow

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The original model of Helbing et al. [25, 26] is not well suited for situations, where there are agents going to different directions and their paths are crossing or opposite of each other. To avoid collisions, a short range counterflow model [33] was introduced in FDS+Evac version 2.2.0. In the counterflow model, the area in front of each agent is divided into three overlapping sectors. The basic idea is to always choose the sector with least counterflow.

Stairs

In FDS+Evac there are three different ways of describing a staircase.

1) A simple corridor model, with a known length and relative movement speed, where the agents are just moved from one floor to another. Only one way movement is possible. The amount of smoke and toxic gases is linearly interpolated from the values in the floors.

2) An incline model, where agents actually seem to move along an incline in the graphical interface (Smokeview). The smoke and toxicity data are taken from the actual height in the fire model.

3) An entire staircase can be defined, with several stairs and landings that are connected to different floors. The smoke and toxicity are not yet taken into account in this model. This feature is being added in the near future.

6. Present limitations

FDS+Evac can be used to simulate many egress scenarios, but there are also limitations that need to be taken into account when using the program.

The mesh is rectilinear, which can limit the use of certain geometries

The grid cell size determines the finest details, e.g. the width of the doors

Spaces where agents are allowed to move should be at least 0.7 m wide

No time dependent geometries can be used (but doors can be opened time dependently)

Density of the agents is maximum 4 persons/m2

The reduction of the walking speed due to smoke is assumed to be equal to all agents, although it in the reality is individual

The effects of heat radiation and gas temperature on the agents are not yet implemented in the program

The stairs models do not include the option for agents to turn back, when the smoke concentration becomes too high

The incapacitation model (FED) doesn't include other gases but CO, CO2 and O2.

The fire detection cannot be connected to the control logic of FDS, e.g. the smoke/heat detectors in FDS calculation cannot be used to trigger the movement of the agents

It is not possible to divide the evacuation calculation to different processors using the parallel version of FDS5, which may limit the size and complexity of the simulated scenario.

When used for train scenarios problems might occur in the following situations:

More than 4 persons/m2 are supposed to be located in the model as initial condition.

This kind of situation cannot be simulated with FDS+Evac. The problem could be partly solved by using a reserve of people ( Figure II-3) that are originally outside the model but come inside as soon as there is room for them.

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Figure II-3: A reserve of people.

There are significant amounts of toxic gases that are not included in the FED calculation

This could be a problem if FED calculations are used. Other gases will be taken into account in the future versions of FDS+Evac (FDS6 ).

Queuing behind a closed door

The present official version of FDS5 does not have this feature. In the new subversions that can be loaded and compiled from FDS-homepage there is a logical keyword LOCKED_WHEN_CLOSED in the EXIT/DOOR namelist. When this is set to .TRUE. the agents stop at the door line until the door is opened.

Group behaviour

Group behaviour like families etc. cannot be taken into account yet in FDS+Evac simulations. According to socio-psychological literature, a crowd consists of small groups, like families, that tend to act together [34, 35]. A method for modelling this grouping behaviour has been developed [29] and will be added to FDS+Evac in the future.

Braking

Braking effect is not taken into account in the FDS+Evac simulation. We don’t actually know how the braking affects the evacuating people. One way to take into account the braking would be to assume that the people don’t move at all during the braking. The braking time could be added to the reaction time of the people.

Picking up outerwear or luggage

In the present version of the program there is not possible for the agents to have an intermediate target and stop, so we cannot take this into account in the simulations. If we would have data about the delay that this behaviour causes, we could add this to the reaction time of the passengers.

Narrow corridors

In the simulations agents are not able to move through corridors or doors that are narrower than 0.7 m. This problem could be solved by defining 0.7 m wide doors or corridors and by taking into account the narrowness in the walking speed of the passengers.

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Section III - Verification and validation

Verification is the process of determining that the implementation of a calculation method accurately represents the developer's conceptual description of the calculation method and the solution to the calculation method [33, 34]. In other words, in the verification process, it is tested, how the model works as a computer code. The verification tests do not necessarily tell how well the model is modelling the actual evacuation scenarios. This is done in the validation process. Validation is determined as the process of determining the degree to which a calculation method is an accurate representation of the real world from the perspective of the intended uses of the calculation method [36]. Validation typically involves [37]: 1. Comparing model predictions with experimental data 2. Quantifying the differences in light of uncertainties in both the measurements and model inputs 3. Deciding if the model is appropriate for the given application The validation case studies only help with 1 and 2. Number 3 is the responsibility of the model user. III.1. FDS+Evac 1. Verification

Although FDS+Evac is a stochastic modelling program, for the qualitative verification it is enough just to run the model once for each scenario. Same is true for the numerical verification of the sub-models [8]. These tests give confidence on how the model is working and how accurate the model equations are solved numerically. Verification of FDS+Evac agent movement algorithm is largely based on the International Maritime Organization (IMO) document "Guidelines for Evacuation Analyses for New and Existing Passenger Ships" [38], where eleven different test cases are listed [39]. IMO sees that "At this stage of development there is insufficient reliable experimental data to allow a thorough quantitative verification of egress models. Until such data becomes available the first three components of the verification process are considered sufficient", where the first three components are component testing, functional verification, and qualitative verification. The archive of the verification tests of FDS+Evac can be found at the FDS+Evac web pages. The numerical convergence checks are reported by Korhonen et al. [40] and the properties of the exit selection algorithm by Ehtamo et al. [31]. 2. Validation in the literature

FDS+Evac manual [8] lists three test cases, where the FDS+Evac predictions are compared to experimental data on human flows on horizontal paths and stairs. In the first test case, the specific flow rates given by FDS+Evac code are compared to experimental walking velocities on horizontal floors in corridor. The predicted flow rates are compared against experimental results for pedestrian flows taken from Daamen's thesis [41]. The FDS+Evac simulations were performed with two different parameter sets labelled "default" and "fast". The results can be seen in Figure III-1.

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Figure III-1 : Specific flows in corridors [8].

In the second test case an evacuation experiment at a large office building [42] is modelled using FDS+Evac. The experimental observations are compared to the simulation results obtained by using the simple staircase model (type 1, corridor). The simulations were run several times corresponding to different values of the staircase speed reduction parameter. Reducing the unimpeded walking speed by a factor 0.5 seems to give a good agreement with the observations. When using more sophisticated staircase model (type 2, incline) reducing the unimpeded walking speed by a factor 0.7 seems to give a good agreement with the observations. When using the most sophisticated staircase model (type 3, complicated), reducing the walking speed by a factor 0.6 seems to give a good agreement with the observations. As a third test case an observed evacuation experiment of a public library [42] was simulated to study the capability to predict the entire movement phase of the evacuation, consisting of movement inside the floor, queuing to the staircase and finally movement through a narrow staircase to the exit. The staircase was modelled using the model type 2. The decision making processes were not modelled. Instead the people were allocated for the doors according to a ratio observed in the experiment. The predicted flow rates agreed in general with the experiments. Korhonen et al. [33] validated the counterflow model with two different data sets. Isobe et al. [43] ran experiments with university students in a 12 m by 2 m corridor. Initially 50% of the students were randomly located in the left half of the corridor and the other 50% in the right half. The students in the right half tried to walk to the left and vice versa. The same experiment was run with different numbers of students to analyse the effect of population density on the flow rates. In the simulation the body properties and walking speeds of the agents were selected to match the properties of the students participating the experiment. The simulation results match the observations very well. Kretz et al. [44] ran counterflow experiments in a corridor in a slightly different setting. Kretz had the two groups standing 20 meters apart each other. The flows measured were significantly faster than those of Isobe et al., because the persons were able to form lines already before the two groups encounter. Simulations [33] produced 60% slower flows than the experimental results. The conclusion is that the presented model is able to prevent the occurrence of unrealistic jams and it gives realistic results in dense crowds. For a longer range collision avoidance a more complicated approach would be needed. Further study is also needed to validate the model for narrow spaces and smaller amount of people.

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At the FDS+Evac web pages there are some validation tests listed, where FDS+Evac is compared against other evacuation simulation programs. Details of these tests can be found in the manual [1]. 3. Validation with train evacuation data

Following data was used for further validation of FDS+Evac specifically for train scenarios. Case 1: Norén & Winér [45] have collected data on train evacuation. They studied flow of people when leaving the train through the normal exits. Recordings were made at 4 railway stations during normal operation; that is, with normal travellers not participating a test or a drill. Two other tests were planned evacuation exercises, that is, the participants knew what was going to happen. The train tests provide data on egress capacity, mainly as a function of door width and as a function of the vertical distance between the train floor and the platform. In addition, the effect of carrying a luggage was studied. Case 2: Oswald et al. [46] investigated evacuation of passengers from a metro train in a simulated tunnel situation with a focus on the geometry of the passage between the metro cars and the tunnel walls. Furthermore trains for underground transportation usually have a high floor which means that the people have to overcome a height of 1.0 to 1.2 m to the surrounding ground adopting different types of exiting strategies. In addition people who try to leave the train at the same time, have to interfere and react to each other in some way. In addition people who try to leave the train at the same time have to interfere and react to each other somehow. The major findings were that the specific flows at the doors are in the range of 0.25 pers/min m in the tunnel situation, the exit flow rate is strongly influenced by the situation outside (open/narrow/congestion/no congestion) and that there are three different types of exiting the train: "Jumper" (45%), "Sider" (28%) and "Sitter" (27%). Case 3: Capote et al.[22] have developed a stochastic evacuation model specifically for high speed trains. In the development work they have used data from video recordings of evacuation drills and virtual experiments conducted at the University of Cantabria. The results of the model are compared with other validated evacuation models (STEPS, PathFinder, FDS+Evac). Comparison with the results from FDS+Evac simulations could be used for validation. At the moment they have published only an abstract and a presentation at the 5th International Conference on Pedestrian and Evacuation Dynamics, March 8-10, 2010 in Maryland, USA. Conference proceedings are in press. Case 4: In a Swedish research project METRO (2009-2012) [47] sub-models for existing egress models are developed and calibrated for the evacuation from trains, underground stations and tunnels. Small-scale, medium-scale and full-scale evacuation experiments will be performed. Special attention will be focused on the needs of senior citizens and persons with disabilities. This project was followed, but the data was not published early enough to be used for FDS+Evac calibration/validation during the TRANSFEU project. Effect of different exit widths and heights for flows of people with and without luggage Train exit flows were validated using data from cases 1 and 2 [45, 46]. Passengers with and without luggage were considered separately. For agents without luggage, a fractional downwards velocity (FAC_V0_DOWN -property in FDS+Evac) is proposed to achieve results similar to the evacuation studies. For agents with luggage a relation between agent body dimensions and velocity is proposed. The addition of luggage was only considered to slow the overall velocity, and thus the same fractional downwards velocity is proposed for both cases.

An empty room with a short stairway leading to a door was used as the simulation model. In all the simulations 125 agents were placed randomly inside the room and only the agent properties and stairway dimensions were altered. In luggage simulations the room was enlarged to allow space for all the agents and the door was set to open 10 seconds after the simulation was started to allow the slower agents to pack behind the door. The average flow of people, beginning from the first person and ending to the last person exiting, was measured for each simulation and used in comparison. The simulation model is shown in Figure III-2.

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Figure III-2 : Simulation model for testing flows in exits with different widths and heights.

The first goal was to study if there was a fractional downwards velocity that would result in realistic simulation results regardless of stair properties. Luggage was not considered as it was assumed to only have an effect on the overall velocity. The effect of fractional downwards velocity on flow of people was evaluated by varying the door width and the stairs height. Both cases were evaluated separately.

Effect of door width was evaluated by setting the stairs height to 0.3 m and stairs length to 0.5 m, and then varying the door width and the fractional downwards velocity of the agents. The evacuation studies used in comparison had the most variance in door width with approximately 0.3 m high stairs, which is why the height was chosen. The results of the evacuation tests carried out in Schiphol and Utrecht stations [45] were used in comparison.

For agents without luggage, the simulation was run with door widths from 0.7 to 1.4 m, and fractional downwards velocity multipliers from 0.2 to 0.5. The results for 'Adult' agents are shown in Figure III-3. The LS-fit -line shown in the figure is a least squares fit of the experimental results. It is quite close to the results of the simulations with fractional downwards velocity 0.25, and not too far from the results of velocity 0.2. Thus judging from these results, a fractional velocity between 0.2 and 0.25 is proposed.

Effect of exit height was evaluated by setting the door width to 1.3 m and stairs length to 0.5 m, and then varying the stairs height and the fractional downwards velocity of the agents. The evacuation studies used in comparison had the most variance in stairs height with approximately 1.3 m wide doors, which is why the width was chosen. All the evacuation tests with approximately 1.3 m wide doors were used in comparison [45].

For agents without luggage, the simulation was run with stair heights from 0 to 1.2 m, and fractional downwards velocity multipliers from 0.2 to 0.5. The results for 'Adult' agents are shown in Figure III-4. The LS-fit -line shown in the figures is a least squares fit of the experimental results. It is very close to the results of the simulations with fractional downwards velocity 0.25. Combined with the earlier results a fractional downwards velocity of 0.25 is proposed. The tests identified "Young commuters" and "Healthy men" in Figure III-4 were excluded from line fitting as their demographics didn't represent average travellers.

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Figure III-3 : Correlation of door width and fractional downwards velocity with agent type

'Adult'.

Figure III-4 : Correlation of stairs height and fractional downwards velocity with agent type 'Adult'.

Simulations were run by varying stairs height, length and door width to determine how big an effect stairs length has on the flow of people. The proposed 0.25 was used as fractional downwards velocity. The results are shown in Figures III-5 and III-6. The effect of stairs length seems to be significant only with higher flows. Also as there are relatively little differences in typical train stairs lengths, the variations observed here are considered insignificant.

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Figure III-5 : Correlation of stairs length and door width with agent type 'Adult'.

Figure III-6 : Correlation of stairs length and height with agent type 'Adult'.

FDS+Evac doesn't currently have functionality to simulate actual luggage carrying, and thus alternative methods were used to achieve similar behaviour and results. As there were only two real world experiments, the evacuation tests carried out in Schiphol and Kastrup stations [45], to compare the simulations to, only those two tests were simulated. The tests had stair heights of 0 and 0.3 m and they both had a door width of 1.3 m.

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To achieve realistic behaviour, the velocities and body dimensions of agents were varied and compared to results of the experiments. The different body types simulated are shown in Figure III-7 : Tested agent body types depicting agents with luggage compared to default body types of 'Female', 'Adult' and 'Male'. The diameter of torso was set to the default of agent type 'Adult', and only the shoulder and total person diameters were varied.

The results of simulations with wide range of parameters are shown in Figures III-8 and III-9. The dashed lines show the flow speed measured in the experiments, and thus the closer the flow speed of a simulation is to the dashed line, the closer the result is to the flow speed measured in the experiment. The dashed rectangles show the parameter intervals which were examined more closely by running additional simulations. The results of the additional simulations are shown in Figure III-10.

Figure III-7 : Tested agent body types depicting agents with luggage compared to default body types of 'Female', 'Adult' and 'Male'.

Figure III-8 : Correlation of person diameter and velocity with 0 m stairs height.

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Figure III-9 : Correlation of person diameter and velocity with 0.3 m stairs height.

.

Figure III-10 : Correlation of person diameter and velocity with 0 m stairs height, additional simulations.

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Figure III-11 : Correlation of person diameter and velocity with 0.3 m stairs height, additional simulations.

By cross-examining the results of the two tests, the quality of each parameter pair (body dimensions and velocity) was further evaluated. This was done by addition of the square sum of the errors of each parameter pair, which was defined to depict the quality of the results. The error sums are shown in Figure III-12. The lower the error is, the better the results are compared to the experiments.

Figure III-12 : Quality of simulated parameters compared to experiments.

By selecting the best parameter pairs (the pairs that resulted in lowest errors), a relation between velocity and body dimensions can be established that results in realistic simulation results. The relation is shown in Figure III-13. Second order least squares fit was applied to the error graphs of Figure III-12 to counter variation and to better reveal the actual relation of body dimensions and

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velocity to realistic flow speeds. The recommended FDS parameters for luggage carrying agents are shown in Table III-1. Person diameter represents the mean of DIA_LOW and DIA_HIGH parameters, and shoulder diameter represents the D_SHOULDER_MEAN parameter.

Figure III-13 : Relation of body dimensions and velocity that result in realistic flow speeds.

Person diameter (m) Shoulder diameter (m) Velocity (m/s) 0.75 0.250 1.05 0.77 0.255 1.1 0.79 0.260 1.15 0.81 0.265 1.15

Table III-1 : Recommendations for agent body dimensions and velocity depicting agents carrying luggage

From Figure III-12 it can be concluded that person diameter 0.81 m gives the best results and even allows variation in person velocities without too much of an increase in error. With diameters below 0.75 m and above 0.81 m the error increases rapidly and thus only diameters between 0.75 m and 0.81 m are recommended.

Section IV - Simulations

A few basic scenarios were chosen as examples of train evacuation simulations. The train scenarios are based on the fire scenarios that were determined in WP4 of TRANSFEU project. The scenarios 1A and 2B were chosen for evacuation simulation. In those scenarios the idea was to simulate the evacuation of one coach. The scenarios have stochastical variables (location of people, individual detection and reaction times, walking speed and diameter of people), meaning that the scenario needs to be simulated several times with different parameter values that are randomly chosen from statistical distributions. In addition there are subscenarios about some details like height of the exit step, different fire location, different amount of people, part of the people carrying luggage or a smaller number of exit doors available. The evacuation was simulated separately from fire modelling, which means that no spreading of fire or specific yields of toxic gases from train materials were used in the evacuation simulations.

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IV.1. Scenarios

1. Train scenario 1A, evacuation scenarios a and b

Train scenario 1A (commuter train) is simulated with the following two evacuation situations. a) Passenger alarm takes place, when the train is at a platform. b) Passenger alarm takes place, when the train is between the stations in full speed.

The details of the scenarios are the following: Train type

o Commuter train, open passenger area 150 m3, no evacuation to adjacent vehicle o Operation category 1

No underground sections, tunnels or elevated structures Can be stopped with minimum delay, after which immediate (in 40 sec)

side evacuation to a place of ultimate safety is possible Geometry

o French coach MS61 o Height of the exit step:

At the station 0 m (a) Between stations 0 m (b)

Ventilation o HVAC (Heating, Ventilating, Air Conditioning) in function during dwell time o Max air flow 0.5 m3/s injected by the sailing

Fire o Fire model 5 acc. to TS 45545-1 o Located where the luggage can be placed: on the floor between the seats close to

the wall Detection and alarm and reaction time

o No automatic fire detection o Detection and reaction times are individual o Passenger alarm occurs 30 s after ignition

Activation of the brake o At the station activation of a passenger alarm leads to a direct application of the

emergency brake, resulting in a complete stop (a). o Between stations automatic braking starts in 10 seconds after activation of the

passenger alarm. Braking takes 25 seconds (b). Communication with the driver

o When passenger alarm is activated, the driver knows that something happens but no communication is possible

Door opening o Passengers can open the doors, when the train is stopped

Type, number and position of people o 75 passengers randomly positioned in the area o 75% adults (15-60 years) ,10% children, 10% elderly, 5% other people with reduced

mobility (PRM) including one wheelchair user (based on TSI 2008/164/EC [48] and metro statistics in Finland)

Pre-movement behaviour o People are already wearing outerwear o People don’t carry luggage during the evacuation (base case)

Places of safety o At the station (a) people are evacuated to the platform o Between stations (b) people are evacuated to the escape walkway

Sensitivity analysis o Sensitivity analysis is made for the following cases

50% of the people are carrying luggage Fire is located in front of a door There is double amount of passengers (150) Different heights of the exit step (0m, 0.35m, 1.24m)

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2. Train scenario 2B, evacuation scenarios a and b

Train scenario 2B (double decker) is simulated with the following two situations: a) Passenger alarm takes place, when the train is at a platform b) Passenger alarm takes place, when the train is between stations, and shall be driven to a

safe place, which will take 15 minutes.

The details of the scenarios are the following: Train type

o Intercity, double deck car, 4 doors (2 on each side) with gangway for open access to the adjacent vehicle

o Operation category 3 There can be underground sections, tunnels and elevated structures Side evacuation available to a place of ultimate safety (reachable within a

long running time (15 minutes) Geometry

o Italian coach o Height of the exit step (base case):

At the station 440 mm (a, c) Between stations 990 mm (b)

Ventilation o Automatic shut down

Fire o Fire model 5 acc. to TS 45545-1 o Located where the luggage can be placed: on the floor between the seats close to

the wall Detection, alarm and reaction time

o Automatic fire detection is available o Detection and reaction times of the passengers are individual o Passenger alarm occurs 30 s after ignition

Activation of the brake a) Activation of a passenger alarm leads to a direct application of the emergency brake, resulting in a complete stop. b) The driver overrides the braking action initiated by the passenger alarm and drives the train to a safe place, which will take 15 minutes

Communication with the driver o When passenger alarm is activated, the driver knows that something happens but

no communication is possible Door opening

o Passengers can open the doors, when the train is stopped Type, number and position of people

o 150 passengers randomly positioned in the area (base case) o 75% adults (15-60 years) ,10% children, 10% elderly, 5% other PRM including one

wheelchair user (based on TSI 2008/164/EC and metro statistics in Finland) Pre-movement behaviour

o People put on their outerwear before starting to move towards the exit o People don’t carry luggage during the evacuation (base case)

Places of safety o At the station (a) people are evacuated to the platform, which is a place of absolute

safety o Between stations (b) people are evacuated to the adjacent vehicle, which is a place

of relative safety Sensitivity analysis

o Sensitivity analysis is made for the following cases 50% of the people are carrying luggage Fire is located in front of an exit There is double amount of passengers (300) Different heights of the exit step (230 mm, 440 mm, 550 mm)

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A last coach scenario

IV.2. Data, parameters and methods 1. Train geometry

Scenario 1A Train geometry for the scenario 1A is based on French coach MS61 (Figure IV-1). 10% of the seats are priority seats for the use of PRM [48] and there is one wheelchair place near one door in each coach (conservative assumption). The priority seats are located near the doors at each end of the coach.

Figure IV-1 : Train geometry (French coach MS61) for the scenario 1A.

Scenario 2B Train geometry for the scenario 2B is based on Italian coach (Figure IV-2). 10% of the seats are priority seats for the use of PRM [48] and there is one wheelchair place near one door in each coach (conservative assumption). The priority seats are located near the doors at each end of the coach.

Figure IV-2 : Train geometry (Italian coach) for the scenario 2B.

2. Distribution and properties of the passengers

Distribution and properties of the passengers are presented in the Table IV-1. PRM (people with reduced mobility) means all the people who have difficulty when using trains or the associated infrastructure. This includes the following categories [48]:

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Wheelchair users Other mobility impaired including:

people with limb impairment; people with ambulant difficulties; people with children; people with heavy or bulky luggage; elderly people; pregnant women.

Visually impaired. Blind people. Hearing impaired. Deaf people. Communication impaired . People of small stature (including children).

In the simulations it is assumed that 25% of all people are PRM (10% children, 10% elderly, 5% other PRM) and that there is one wheelchair place and one wheelchair user in each coach (conservative assumption).

Type of people Distribution(1 Rd(m)(2 Speed(m/s)(3

Adult 15-60 years 75% 0.255 ± 0.035 0.250 ± 0.020

Child 10% 0.210 ± 0.012 0.90 ± 0.30

Elderly 10% 0.250 ± 0.020 0.80 ± 0.30

Other PRM 5% (1 person/coach with wheelchair)

0.250 ± 0.020 0.78 ± 0.34

(0.69 ± 0.35)

1) Distribution of people is determined on the basis of Finnish metro statistics and TSI 2008/164/EC [48] 2) For the body diameter (Rd) FDS+Evac default values [1] are used 3) For the walking speed FDS+Evac default values [1] are used except for the PRM people the values are determined on the basis of experimental data [49]

Table IV-1 : Distribution and properties of the passengers

3. Detection time and passenger alarm

We assume that passengers in the fire carriage discover the fire from the smoke and/or flames, moving and shouting of other passengers and latest from the passenger alarm. In the simulations the detection time is individual and has a uniform probability distribution tdet=[1,30]s. Detection time is the starting point of the reaction time of each passenger. The driver discovers the fire from the passenger alarm, which is assumed to happen 30 s after ignition, which is selected on the basis of experimental results (Table IV-2). The passenger alarm is the starting point of the dwell time. EN 50553 [50] gives guidance to determination of the detection time of a smoke detector. It says that “the detector should activate and cause an alarm to be given within 2 minutes”. Now we assumed that the detection time is shorter than that, because the escaping passengers are in the same cabin with the fire.

4. Reaction time

Reaction time is a delay between the moment, when a person notices the situation and the moment, when he/she starts to move towards the door. During the reaction time people do

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different things (Table IV-3) that delay their reaction. In the simulations reaction time is individual and assumed to have a uniform probability distribution treac=[1,60]s, which is selected on the basis of experimental results (Tables IV-2 and IV-3).

Experiment Reaction time (s) Classroom1 [51] 1-25 Classroom2 [52] recognition: 17 (average)

response: 29 (average) Retail stores [53] 18-45 Waiting room of a hospital [53] 16-43

Table IV-2 : Reaction time in evacuation experiments

Delaying factor Mean (s) St. dev. (s) Notify others 10 3 Call the brigade 30 9 Inaction 60 18 Collect belongings 30 9 Telephone others 30 9 Close/open doors/windows 5 1.5 Shut down equipment 20 6 Rescue 30 9 Got dressed 60 18 Woke up 60 18

Table IV-3 : Delaying factors in evacuation experiments [53]

5. Dwell time

With the dwell time we mean the time between the alarm and the moment when the doors open and people can evacuate. For example in the scenario 1Ab, where an alarm in a commuter train takes place between stations the dwell time is 40 seconds (Figure IV-3). The delays that are included in the dwell time are presented in the Table IV-4.

Figure IV-3 : Dwell time for the scenario 1Ab.

Table IV-4 : Delays that are included in the dwell time

Scenario Driving to a safe place (s)

Brake activation (s)

Braking (s)

Doors open (s)

1Aa - - - 5 1Ab - 10 25 5 2Ba - - - 5 2Bb 900 - - 5

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6. RSET calculation

RSET is the time required to perform a safe escape from the place of danger to a place of safety. In the simulations RSET is the time, when all the passengers have left the wagon. Because of the stochastic nature of the simulations, several realizations are always simulated for each scenario. For n realizations we get RSET1, RSET2, RSET3...RSETn. For a set of simulations an average RSET can be calculated, with a 95% confidence interval

nsX

nsX 96,1;96,1

where X = average RSET n = number of simulations s = standard deviation

7. Sensitivity analysis

Sensitivity analysis is made by running several simulations per each subscenario and comparing the average results. For the graphic presentation of the sensitivity analysis "the average amount of people inside at time t" is calculated as presented in Figure IV-4. It should be noted that the average RSET is not the same as the time at which "the average amount of people inside" goes to zero. Sensitivity analysis could be made for one realization only by using NOT_RANDOM -parameter (set NOT_RANDOM = .TRUE. in PERS -line), but this method is not used in this study.

Figure IV-4 : 10 realizations of train 1A being evacuated at a platform. The green curve in the figure represents "the average amount of people inside at time t", which is used in the graphical presentation of the sensitivity analysis as the base case.

Evacuation of a commuter train 1A - at a platform

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0 20 40 60 80 100 120

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Peop

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no luggage(average of10 runs)

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IV.3. Simulations with FDS+Evac 1. Train scenario 1A, evacuation scenario a

A situation is simulated where a commuter train (1A) is evacuated at a platform (Figure IV-5). The results of 10 realizations are presented in Figure IV-6. RSET (average of 10 runs) is 82 seconds. Results of the sensitivity analysis are presented in Figures IV-7 - 9. The results are summarized in Table IV-5. It can be seen that when 50% of the passengers carry luggage, the average door flow decreases about 25% but the average RSET increases only about 5% compared to the base case. With one door unusable the door flow decreases only in the beginning of the evacuation phase, towards the end the gap is decreasing and the difference in the average RSET is only about 7%. With double amount of passengers the door flow is about the same in the beginning of the evacuation phase but with the time it gets faster than in the base case. In all of the cases the personal detection and reaction times have a significant influence on the final results as well as the walking speed of the slowest people. The last person leaving the train defines the RSET and in many cases there are 5-10 seconds between the last two people leaving the train.

Figure IV-5 : Train 1A is evacuated at a platform.

Figure IV-6 : 10 realizations of train 1A being evacuated at a platform. Alarm takes place at t=30s and the doors open at t=35s. RSET (average of 10 runs) is 81 s (95% confidence interval ±3s) .

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Figure IV-7 : Effect of luggage carrying was simulated based on the data from experiments by Norén & Winér (2003). For the case of 50% of the passengers carrying luggage the average RSET= 86s (95%confidence interval ±5s)

Figure IV-8 : Situations with different fire locations were simulated. The case is symmetrical so that the situations with fire in front of doors 3 and 4 are identical with 2 and 1 respectively. For the cases, where one door was blocked, the average RSET = 88s (95% confidence interval ±4s).

Evacuation of a commuter train 1A at a platform - effect of luggage carrying

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40

5060

70

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50% have luggage

Evacuation of a commuter train 1A at a platform- effect of fire location

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Fire in the middleFire in front of door1Fire in front of door2

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Figure IV-9 : Situations with different amounts of passengers were simulated. The average RSET for the case with 150 passengers is 101 seconds (95% confidence interval ±3s).

Table IV-5 : Results for a commuter train evacuated at a platform (1Aa)

Scenario Average RSET (s) 95% confidence interval (s) Base case 82 ±3 50% of the passengers carry luggage 86 ±5 Fire in front of one door 88 ±4 Double amount of passengers 101 ±3

2. Train scenario 1A, evacuation scenario b

A situation is simulated where a commuter train (1A) is evacuated between stations (Figure IV-10). The results of 10 realizations are presented in Figure IV-11. RSET (average of 10 runs) is 89 seconds. Results of the sensitivity analysis are presented in Figures IV-12 - 15. The results are summarized in Table IV-6. Now the people are first moving towards the doors while waiting for the train to stop. During the braking they are assumed not to move. When the train stops, there is already a queue behind each door, which makes the actual evacuation phase much faster that in the scenario a, where the train was evacuated at a platform. It can be seen that 50% of the passengers carrying luggage has about the same effect to the door flow as one door being unusable, but the luggage carrying makes the slowest people even slower which influences the RSET. The exit step height seems to have a significant influence to the door flow and to the final RSET. With double amount of passengers the door flow is about the same as in the base case during the whole evacuation phase, which shows that all the people are queuing before going through the exits. Now the alarm and dwell times as well as the door capacity have the most significant influence on the RSET.

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Figure IV-10 : Train 1A is evacuated between stations.

Figure IV-11: 10 realizations of train 1A being evacuated between stations. Alarm takes place at t=30s and the doors open at t=70s. The average is 89 seconds (95% confidence interval ±6s).

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Figure IV-12 : Effect of luggage carrying was simulated based on the data from experiments by Norén & Winér (2003). The average RSET for the case of 50% of the passengers carrying luggage is 99 seconds (95% confidence interval ±8s).

Figure IV-13 : Effect of different heights of the exit step was simulated based on the data from experiments by Norén & Winér (2003) and Oswald et al. (2010). The average RSET for the case of 0.35m exit step was 96s (95% confidence interval ±2s) and for the case of 1.24m exit step was 106s (95% confidence interval ±6s).

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Figure IV-14 : Situations with different fire locations were simulated. The case is symmetrical so that the situations with fire in front of doors 3 and 4 are identical with 2 and 1 respectively. The average RSET for the case of door1 being blocked was 95s (95% confidence interval ±2s) and for the case of door2 being blocked was 93s (95% confidence interval ±2s).

Figure IV-15 : Situations with different amounts of passengers were simulated. The average RSET for the case with 150 passengers was 102s (95% confidence interval ±3s).

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Table IV-6 : Results for a commuter train evacuated between stations (1Ab)

Scenario Average RSET (s) 95% confidence interval (s) Base case 89 ±6 50% of the passengers carry luggage 99 ±8 Fire in front of one door 93-95 ±3 Higher (0.35m) exit step 96 ±2 Much higher (1.24m) exit step 106 ±6 Double amount of passengers 102 ±3

3. Train scenario 2B, evacuation scenario a

A situation is simulated where a double deck train (2B) is evacuated at a platform (Figure IV-16). The results of 10 realizations are presented in Figure IV-17. RSET (average of 10 runs) is 181 seconds. Results of the sensitivity analysis are presented in the Figures IV-18 - 21. The results are summarized in Table IV-7. It can be seen that when 50% of the passengers carry luggage, the average door flow decreases about 25% but the average RSET increases about 36% compared to the base case. This is due to some extremely slow people among the 150 passengers. The differences in the exit step heights are now small, so the influence to the RSET is only about 5%. Fire in front of one exit has now a significant influence to the results (41%), because there are only two exits available in the first place. It can be seen (Figure IV-20) that at t=100s a queue has formed in front of the only available exit. With one door unusable the door flow decreases only in the beginning of the evacuation phase, towards the end the gap is decreasing and the difference in the average RSET is only about 7%. With double amount of passengers a queue is formed in front of both exits also at about t=100s (Figure IV-21).

Figure IV-16: Train 2B is evacuated at a platform.

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Figure IV-17 : 10 realizations of train 2B being evacuated at a platform. Alarm takes place at t=30s, doors open at t=35s. The average RSET was 168s (95% confidence interval ±6s).

Figure IV-18 : Effect of luggage carrying was simulated based on the data from experiments by Norén & Winér (2003). The average RSET for the case of 50% of the passengers carrying luggage was 229s (95% confidence interval ±21s).

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Figure IV-19 : Effect of different heights of the exit step was simulated based on the data from experiments by Norén & Winér (2003) and Oswald et al. (2010). ). The average RSET for the case of 0.23m exit step was 158s (95% confidence interval ±9s) and for the case of 0.55m exit step was 174s (95% confidence interval ±11s).

Figure IV-20 : Situations with different fire locations were simulated. In the base case the fire is located in the middle of the passenger area downstairs, in the other case the fire is located in front of one exit. The average RSET for the case with fire in front of exit was 237s (95% confidence interval ±15s).

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Figure IV-21 : Situations with different amount of passengers were simulated. The average RSET for the case with 300 passengers was 248s (95% confidence interval ±6s).

Table IV-7 : Results for a double decker evacuated at a platform (2Ba)

Scenario Average RSET (s) 95% confidence interval (s) Base case 168 ±6 50% of the passengers carry luggage 229 ±21 Lower (0.23 m) exit step 158 ±9 Higher (0.55 m) exit step 174 ±11 Fire in front of one door 237 ±15 Double amount of passengers 248 ±6

4. Train scenario 2B, evacuation scenario b

A situation is simulated where a double deck train (2B) is evacuated between stations (Figure IV-22). The train shall be driven to a safe place, which takes max 15 minutes. The results of 10 realizations are presented in Figure IV-23. RSET (average of 10 runs) is 236 seconds. Results of the sensitivity analysis are presented in Figures IV-24 - 27. The results are summarized in Table IV-8. Now the people are escaping through corridors at both ends of the carriage. It can be seen that when 50% of the passengers carry luggage, the average RSET increases about 35% due to some extremely slow people. Fire in front of one exit has now even more significant influence to the results (55%), because the capacity of the exit corridors is lower than the capacity of the front doors. The double amount passengers gives the highest RSET (444s) of these simulations although the worst case is the one, where the coach is assumed to be the last one, so that people can only escape from one end, and because the fire is downstairs, part of the people escape first to the wrong end. In scenario 2Bb the exit capacity and some extremely slow passengers had the most significant influence on the results.

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Figure IV-22: Train 2B is evacuated between stations.

Figure IV-23 : 10 realizations of train 2B being evacuated between stations. Alarm takes place at t=30s. The average RSET was 236s (95% confidence interval ±13s).

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Figure IV-24 : Effect of luggage carrying was simulated based on the data from experiments by Norén & Winér (2003). The average RSET for the case of 50% of the passengers carrying luggage was 316s (95% confidence interval ±62s).

Figure IV-25 : Situations with different fire locations were simulated. In the base case the fire is located in the middle of the passenger area downstairs, in the other case the fire is located in front of one exit. The average RSET for the case with fire in front of an exit was 365s (95% confidence interval ±9s).

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Figure IV-26 : Situations with different amount of passengers were simulated. The average RSET for the case with 300 passengers was 444s (95% confidence interval ±25s). The front doors don't open during the simulation.

Figure IV-27 : The last coach –case was simulated, where only one exit door was available for escape. The fire was located in the middle of the passenger area downstairs, so that people could not bypass, instead they had to first escape to the vestibule at the other end of the coach, from which they could walk through the passenger area upstairs to the other end with the open exit door. The average RSET for the case with 300 passengers was 403s (95% confidence interval ±10s). The front doors don't open during the simulation.

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Table IV-8 : Results for a double decker evacuated between stations (2Bb)

Scenario Average RSET (s) 95% confidence interval (s) Base case 236 ±13 50% of the passengers carry luggage 316 ±62 Fire in front of one exit 365 ±9 Double amount of passengers 444 ±25 Last coach -scenario 403 ±10

Section V - Results and discussion

RSET is the time required to perform a safe escape from the place of danger to a place of safety. Classically RSET is divided into detection time, alarm time, response time and travel time. The detection and alarm times mainly depend on stochastical (e.g. type/size of fire) and technical (e.g. detection and alarm system) factors. The response time is influenced by social and individual factors. The travel time is mainly dependent on the geometry and the amount and walking speed of the escaping passengers. Travel times can be calculated analytically by simple formulas describing a flow through a door or by more complicated ones that take into account more details like stairs or congestion, but to be able to more realistically describe complex geometries and the escape of individuals with different physical and psychological properties, simulation is needed.

There are several simulation models that could be used to simulate the escape of passengers from trains, but very few simulations of train evacuations can be found in the literature. We decided to use FDS+Evac for the train evacuation simulations, because FDS is being used for the fire simulations in this project. Verification and validation of the general features of FDS+Evac can be found in FDS+Evac web pages, in the manual and in scientific journals, in this work validation has been done with experimental train data about the effects of different exit door widths, exit step heights and luggage carrying on the exit flows. Recommendations to the FDS parameters values for the agent dimensions and waking speeds in the exit stairs are given and a specific agent type is created to simulate a luggage carrying person. Further validation and data would be needed especially about the behaviour of people in train evacuation situations, moving speeds of passengers in narrow train environments as well as the effect of braking.

A few basic scenarios were chosen as examples of train evacuation simulations. The simulated train scenarios 1A (commuter train) and 2B (double decker) are based on the fire scenarios that were determined in WP4 of TRANSFEU project. For both cases two subcases were simulated: evacuation at a platform and evacuation between stations. For the double decker, evacuation between stations means evacuation to the next coach, because the train will be driven to a safe place, which may take 15 minutes. The commuter train can be evacuated to the escape walkway immediately after braking. For each of the four cases several sensitivity studies were made to study the effects of luggage carrying, different fire locations, amount of passengers, heights of the exit step etc.

In the simulations RSET (from ignition to the moment when the coach is empty) of 75 passengers from a commuter train to a platform was average 82 s, when the individual detection and reaction times were assumed to be 1-30 s and 1-60 s respectively. For a double amount of passengers RSET was 101 s. Between stations ,with a realistic 0.35 m exit step, the simulations of 75 escaping passengers gave an average RSET of 96 s. Between the stations the dwell time (from alarm to the moment when the doors open) was assumed to be 40s and the passengers were assumed not to move during braking , which took 25s.

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For a double decker with 150 passengers the average result of the simulations was RSET=168s at a platform and RSET=236s between stations. The longest RSET (=444s) was simulated for the case, where 300 passengers were evacuated between stations from a double decker to the next coach. Relatively long RSET (=403s) was simulated for only 150 passengers, when the coach was assumed to be the last one so that the passengers first escaped to the “wrong end” from which they had to walk through the upper cabin to the other end, because they could not get through the lower fire cabin.

The simulation examples were chosen specifically for the purposes of the TRANSFEU project, and they don’t cover all the possible situations which occur in practice. It is necessary to make new simulations for specific geometries and situations. We have created a procedure that can be followed, with modifications tailored for each case. There are some specific situations concerning trains that cannot be simulated with the present version of FDS+Evac. A list of these situations and possible solutions are presented in detail in the chapter II.4.6.

The evacuation part of the FDS+Evac is stochastic, i.e. it uses random numbers to generate the initial positions and properties of the agents, including detection and reaction times. In addition, there is a small random force on each agent's equation of motion. For this reason, one should always do several egress simulations to see the variation of the results. Using stochastic approach makes RSET and ASET stochastic variables. To avoid using probabilities for RSET< ASET stochastic approach can be modified back to deterministic by using average values for RSET and ASET.

The coupling between FDS and FDS+Evac makes it possible to use the same geometries and to take into account the fire effects in the evacuation simulation. Fire influences evacuation conditions; it may incapacitate humans and block exit routes. On the other hand humans may influence the fire by opening the doors or actuating various fire protection devices. The effect of smoke on the movement speeds of agents and the toxic influence of the smoke are implemented in movement algorithm of FDS+Evac. The exit selection algorithm of the agents uses smoke density to calculate the visibility of the exit doors and to categorise the doors to different preference groups. The smoke density can also be used to trigger the detection of fire in addition to the user given detection time distribution. When using coupled fire and evacuation modelling with realistic yields of toxic substances also realistic exposures of the passengers can be studied.

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