a study on human evacuation behavior involving individuals ......pedestrian evacuation process...
TRANSCRIPT
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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
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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
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! 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
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! 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
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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
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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
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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
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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
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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
1
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RFID Data
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! RFID data used for trajectory analysis.
! Data recorded: 2 seconds interval
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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
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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
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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
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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
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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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