A Study on Human Evacuation Behavior Involving Individuals with Disabilities in a Building

Nirdosh Gaire Ziqi Song

Keith Christensen Mohammad Sharifi

Utah State University Logan, UT 84322, United States

Anthony Chen Hong Kong Polytechnic

University Kowloon, Hong Kong, China

Pedestrian Evacuation

!  Immediate and urgent movement of people away from the threat or actual occurrence of hazard.

!  Ranges from small scale evacuation from a building to the large scale evacuation from the district.

!  Reasons for evacuation: "  Natural disasters "  Industrial accidents "  Fire "  Military attacks, etc.

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Image source: https://beijingolympicsblog.files.wordpress.com/2008/05/beichuan-evacuation.jpg?w=500

Image source: https://twistedsifter.files.wordpress.com/2012/09/911-boat-evacuation-tom-hanks-1.jpg?w=800&h=437

!  Pedestrian evacuation process should be planned properly

to avoid bad consequences.

!  Exit doors at the public facility plays a major role in the

evacuation process.

!  Many studies found in the literature on evacuation

modeling.

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Introduction

!  Empirical studies on Individuals with Disabilities still

missing in the literature.

!  Surprising because they consist of a large portion of the

population (12.6% of total population) in U.S. (Kraus,

2015).

!  Evacuation models mainly being developed using Stated

Preference rather than Revealed Preference.

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Introduction

This study is important because of two main reasons:

1.  Individuals with disabilities considered in the evacuation

model.

2.  Revealed Preference used for the study instead of Stated

Preference. Real life experiment used for the analysis.

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Significance

Literature Review

!  Pedestrian evacuation behavior in a room with single

or multiple exits have been investigated from

experiment and simulations.

!  Exit choice from a room has been studied under

different scenarios considering different parameters.

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Literature Review

!  Studies done on the evacuation behavior of individuals

without disabilities.

!  No study found on models based on individuals with

disabilities.

!  Heterogeneity in population not been studied in evacuation

models.

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Studies Done For Exit Choice Behavior During Emergency Evacuation

Author Method of study Factors considered for the exit choice

Relevant findings DE D RI F IWD HP Duives and

Mahmassani (2012)

Multinomial logit model √ √

Group behavior generally found in evacuation scenarios.

Fu et al. (2016) Discrete evacuation model √ √

Phenomenon like arching, clogging and irregular outflow seen during simulation.

Lovreglio et al. (2016)

Mixed logit model √ √ √ √

Density and distance had negative affect in the exit choice, whereas flow and room information had positive affect in exit choice.

Guo and Huang (2008)

Logit based model √ Information of exit has major role in exit choice.

Liu et al. (2009) Simulation √ √ √ Density plays an important role in exit choice. Unfamiliarity with the room features makes difficult to make exit choice.

Nilsson et al. (2008)

Unannounced evacuation experiment

√ Information like green flashlight can have positive influence in the exit choice.

Haghani et al. (2014)

Multinomial logit and mixed logit

models √ √ √

Distance, density and room information had positive affect in the exit choice behavior.

Fang et al. (2010) Experimental study √ √ √

During low density condition around exits, shortest exit chosen. During congestion, farthest exit chosen to avoid time wasting.

8 Note: DE = Distance to Exit; D = Density around exit; RI = Room Information; F = Flow at exit; IWD = Individuals with Disabilities; HP = Heterogeneous Population

Experiment Settings

!  Agscience building at USU used as the research setting.

!  4 exit doors at the ground floor which were accessible for all individual types.

!  Participants asked to evacuate with maximum comfortable speed after alarm went off.

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Exit Doors

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Radio Frequency Identification (RFID) Tracking

!  Automatic identification system that consists reader and tags.

!  Cost effective, small in size and capable to store more than enough information.

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Participants wearing RFID tags lanyards

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Radio Frequency Identification (RFID) Tracking

RFID receiver RFID tags

Experiment Settings

!  Microscopic data collected using RFID tracking technology complemented by video tracking methods.

!  Video cameras used for video tracking.

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Camera

RFID signal

Participants

!  47 participants in 16 different evacuation scenarios.

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Run Location Total IDs Number of IWDs 1 Class 40 7 2 Class 37 6 3 Class 40 8 4 Class 37 7 5 Computer Lab 41 8 6 Computer Lab 42 9 7 Computer Lab 43 10 8 Both 44 11 9 Both 40 9 10 Both 44 11 11 Class 41 7 12 Lecture hall 43 11 13 Lecture hall 31 4 14 Lecture hall 45 11 15 Computer lab 41 9 16 All places 41 11

Individuals with disabilities

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Total participants

47 Individuals without disabilities

34

Visual disability

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Wheelchair movement

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RFID Data

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!  RFID data used for trajectory analysis.

!  Data recorded: 2 seconds interval

Exit Doors Identification

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Discrete Choice Model

!  Evaluate alternatives measured by the utility function. !  Let Ui be the utility that determines the discrete outcome i. Where, Ui = True utility (unknown to analyst). Vi = Deterministic component (measurable).

ξ i = Stochastic component (unmeasurable).

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Ui = Vi + ξ i

Discrete Choice Model

V↓i = ∑𝑖=1↑𝑘▒𝛽↓𝑖 𝑋↓𝑖   k = number of attributes used for the utility function.

𝛽↓𝑖  = parameter that will define the weightage of the attribute.

𝑋↓𝑖  = attribute for the selection.

Pn(i/𝐴↓𝑛 ) = Probability that individual ‘n’ will choose alternative ‘i’ from the choice set 𝐴↓𝑛  = {1,2,…, i, j, …..M}

= Prob ( 𝑈↓𝑖  > 𝑈↓𝑗 )

= Prob ( 𝑉↓𝑖  + ξ↓𝑖  > 𝑉↓𝑗  + ξ↓𝑗 )

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Choice Probability

!  If the error terms are modeled as Gumbel distribution, then we have the well known logit model:

P↓i  = exp (V↓i )/ ∑𝑙∈𝐴↑▒exp (V↓l )   !  Binary choice model Probability of selecting a door from 2 doors:

𝐏↓𝐝𝐨𝐨𝐫𝟏  = 𝐞𝐱𝐩 (𝐕↓𝐝𝐨𝐨𝐫𝟏 )/𝐞𝐱𝐩 (𝐕↓𝐝𝐨𝐨𝐫𝟏 ) + 𝐞𝐱𝐩 (𝐕↓𝐝𝐨𝐨𝐫𝟐 ) 

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Binary logit model

!  Two different utility functions were created for the individuals with and without disabilities.

!  After preliminary analysis of the model, three important variables were selected for modeling the exit choice.

𝐕↓𝐝𝐨𝐨𝐫𝟏  = CONS1 + BETA1 * Dd1 + BETA2 * Ke1 + BETA3 * Nd1

𝐕↓𝐝𝐨𝐨𝐫𝟐  = BETA1 * Dd2 + BETA2 * Ke2 + BETA3 * Nd2 Dd1 & Dd2= distance of the individual’s initial position from the doors (meters). Ke1 & Ke2 = exit density at the two doors. Nd1 & Nd2 = number of individual with disabilities at doors at different time intervals.

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Individuals Without disabilities

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Statistical parameters from the model.

Final model:

Door 1: -0.38 – 0.23 * Dd1– 0.08 * Ke1 – 0.33 * Nd1

Door 2: – 0.23 * Dd2 – 0.08 * Ke2 – 0.33 * Nd2

Validation?? Done with 10% of the remaining data. Model: VALID

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Individuals With Disabilities

Statistical parameters from the model.

Final model:

Door 1: -0.26 – 0.21 * Dd1 – 0.16 * Ke1 + 0.39 * Nd1

Door 2: – 0.21 * Dd2 – 0.16 * Ke2 + 0.39 * Nd2

Validation?? Done with 10% of the remaining data. Model: VALID

Exit Choice

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-0.23

-0.08

-0.33

-0.21 -0.16

0.39

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Distance to exit Exit density Number of IWDs at door

Coe

ffici

ents

val

ue

Variables

without disabilities with disabilities

Policy Implications

!  Individuals with disabilities have trust for the other individuals with disabilities, which makes them choose the same exit as the other IWDs.

!  Building codes like ADAAG, IBC primarily focus on the visual signs which turns out not to be very important for IWDs.

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Image source: http://fire-safety.typepad.com/.a/6a01a3fd21d25e970b01bb08e12b5b970d-pi

Recommendations

!  Visual signs not helpful for individuals with visual disabilities.

!  Not only visual signs, also audible indicators if provided might be more helpful for individuals with visual disabilities.

!  Assistance required for the individuals with visual disabilities during evacuation for finding the exit doors.

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Thank you!

QUESTIONS?

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