A SimPy-based discrete event simulation model for large-scale disaster evacuation systems. The model has capability to describe and simulate detailed transportation networks and alternative modes of transportation.

1. ANALYSIS OF EMERGENCY
EVACUATION USING LARGE-SCALE
SIMULATION
Presented by:
Ahsanur Rahman
Thesis supervisor:
Dr. Suleyman Karabuk

2. Presentation Outline
• Research goals
• Why Python ?
• SimPy based simulation model
– Program Logic
– Simulation Execution
– A sample network
• Model analysis
• Future research
Analysis of emergency state evacuation using simulation using large scale simulation 2

3. Research Goals
• Reproduce an existing Mesoscopic transportation
evacuation model (DOE_EVAC) using a general
purpose programming language like Python
• Extend the model and analyze its behaviour with
different demand loading model
Analysis of emergency state evacuation using simulation using large scale simulation 3

4. Why Python ?
Python and SimPy:
Python is a general purpose programming language .
And SimPy is an object oriented, process based discrete event
simulation language based on Python.
Advantage of using SimPy:
It can integrate other software tools and mathematical
programming models with lot more flexibility than a proprietary
software environment like ARENA
Analysis of emergency state evacuation using simulation using large scale simulation 4

5. Traffic simulation approaches
• Microscopic simulation
 Simulates detailed behaviour of every individual vehicle
 Not suitable for a large network simulation
• Macroscopic simulation
 Considers platoons of vehicles together instead of individual
vehicles
 Simulates traffic flow in brief time increment
 Suitable for large network simulation
• Mesoscopic simulation
 It follows a middle path of the Microscopic and Macroscopic
simulation
 It can simulate individual vehicles as Microscopic simulation
 It also represents the aggregate traffic dynamics like
Macroscopic simulation
Analysis of emergency state evacuation using simulation using large scale simulation 5

6. SimPy Based Simulation Model
Model highlights
1. It follows the Mesoscopic simulation approach
2. It can effectively simulate the alternative modes of transportation
3. It facilitates its users to investigate the ‘what-if scenarios’ with
statistical analysis capabilities.
4. It can implement and analyze different kind of traffic control
policies.
Analysis of emergency state evacuation using simulation using large scale simulation 6

7. Program Logic
Analysis of emergency state evacuation using simulation using large scale simulation 7

8. Simulating traffic signals
1) Non electronic signals
Example: STOP sign
RED
2) Electronic signals
Yellow Green
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9. Simulating traffic signals
Simulation example for 3 way links
Analysis of emergency state evacuation using simulation using large scale simulation 9

10. Simulating traffic
• Vehicles
– They take the shortest route from origin to destination to get out of
the disaster area
– The shortest route is based on the free flow times of the links
– On their route they move from one link to another link they check for
available space
• Pedestrians
– They take the shortest route from origin to transit center. The transit
centers have infinite capacity
– The shortest route is based on the distance of the links
– Then they get into the public transport to get out of the disaster area
Analysis of emergency state evacuation using simulation using large scale simulation 10

11. Simulating traffics
Analysis of emergency state evacuation using simulation using large scale simulation 11

12. Simulation execution
Assumptions
– The affected area is known or at least the tentative affected area is
sorted out.
– Locations of the destinations are selected prior to the evacuation.
– The evacuation planner as prior knowledge of the transportation
network very well.
– Evacuation planners arrange sufficient public transports for the people
who do not own a vehicle.
– The destinations and the transit centers have infinite capacity.
– The pedestrians while walking to the transit centers do not hamper
the usual traffic flow.
– Evacuees’ uncertain behaviour is not taken into account
Analysis of emergency state evacuation using simulation using large scale simulation 12

13. Sample geographical area
Risk area
The geographical area that has the possibility to be affected by disaster. The
whole area can be identified by their Traffic Analysis Zone (TAZ) codes
Analysis of emergency state evacuation using simulation using large scale simulation 13

14. Sample transportation network
• Nodes
• Links
• Intersections
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15. A sample network
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16. Network parameters for simulation
Property name TAZ 1055 TAZ 1061
Number of pedestrians 16 315
Number of vehicles own by the 715 7785
evacuees
Evacuees loading parameter for Exponential Exponential
pedestrians (0.025) (0.04)
Evacuees loading parameter for Exponential (0.03) Exponential
vehicles (0.01)
Point of origin 7405 7533
Transit centers 15930 15931
Interval for a public transport to 600 seconds
load in the system
Maximum waiting time for a 3600 seconds
public transport at the transit
center
Maximum capacity for one 30
public transport
Destinations 1780, 1777, 1776, 1782
Walking speed for the 2.5 miles / hour
pedestrians
Analysis of emergency state evacuation using simulation using large scale simulation 16

17. An example of pedestrian route
Analysis of emergency state evacuation using simulation using large scale simulation 17

18. An example of public transport route
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19. An example of other vehicle route
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20. Sample simulation output
End of evacuation
In hours In minutes In seconds
3.08 184.5 11070.104
Evacuee loading time
TAZ In seconds
Last pedestrian loaded 1055 0.26
in the system 1061 11.85
Last Vehicle loaded in 1055 22.28
the system 1061 77.02
Numbers for the transit centers
In hours In Minutes In seconds
Average time for a For a whole network 0.41 24.62 1477.15
pedestrian to reach the
transit center For TAZ 1055 0.46 27.36 1641.6
For TAZ 1061 0.41 24.5 1468.8
Average waiting time For TAZ 1055 0.71 42.64 2558.33
for a pedestrian at the For TAZ 1061 0.84 50.4 3023.7
Transit center
Analysis of emergency state evacuation using simulation using large scale simulation 20

21. Sample simulation output
Numbers for vehicles
Average time to reach destination
Destination
In hours In minutes In seconds
Considering all destination 0.8 47.2 2831.3
1780 0.08 4.7 280.3
1777 0.05 2.7 162.5
TAZ 1055
1776 0.05 2.7 162.7
1782 0.1 6.02 361.3
1780 0.1 6.14 368.74
1777 1.35 81.37 4881.9
TAZ 1061
1776 1.35 81.1 4866.5
1782 0.1 6.02 361.6
Analysis of emergency state evacuation using simulation using large scale simulation 21

22. Validation of SimPy based model
Simulation time in hours
Replication
Python ARENA
number
model model
1 3.07 2.19
2 3.07 2.19
3 3.07 2.15 For SimPy For ARENA
4 3.08 2.19 model model
5 3.08 2.13
6 3.08 2.49
7 3.08 2.15
8 3.08 2.15 Average total
3.08 hours 2.17 hours
9 3.08 2.19 evacuation time
10 3.08 2.13
11 3.08 2.19
12 3.08 2.19 Variance of total 2.44 X 10-9 5.64 X 10–3
13 3.08 2.13 evacuation time hours hours
14 3.07 2.13
15 3.08 2.16
16 3.08 2.19
17 3.08 2.15
18 3.08 2.13
19 3.08 2.13
20 3.08 2.13
Analysis of emergency state evacuation using simulation using large scale simulation 22

23. Model validation
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24. Definitions
Route free flow time:
It is the summation of the free flow times of a particular route. Hence, it is the minimum
time that a vehicle should take to reach its destination
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25. Model Analysis (stage 1)
Route Mean vehicle trip time (min) Free flow time (min)
TAZ 1055 From 7405 to 1780 4.7 3.84
From 7405 to 1777 2.72 1.5
Number of From 7405 to 1776 2.72 1.5
vehicles = 715
From 7405 to 1782 6.03 3.8
TAZ 1061 From 7533 to 1780 6.13 2.0
From 7533 to 1777 81.35 3.9
Number of From 7533 to 1776 81.09 3.8
vehicles = 7785 From 7533 to 1782 6.02 1.9
Some of the routes are facing severe traffic congestions
Analysis of emergency state evacuation using simulation using large scale simulation 25

26. Demand loading functions
The following demand loading functions were used
to load the traffics into the simulation model for
analysis purpose.
• Exponential distribution
• Rayleigh function
• S-curve function
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27. Rayleigh function
1.2
1
Cumulative loading %
0.8
0.6
0.4
0.2
0
0 30 60 90 120 150 180 210 240 270 300
t (minute)
T= 900 minutes
Analysis of emergency state evacuation using simulation using large scale simulation 27

28. S-curve function
1
Cumulative loading %
0.75
0.5 alpha 0.05
alpha 0.1
0.25 alpha 0.2
0
0 30 60 90 120 150 180 210 240 270 300
time, t (minute)
Half loading time = 180 minutes
Analysis of emergency state evacuation using simulation using large scale simulation 28

29. Model analysis (Stage 2)
Selecting comparable parameters for demand loading functions
• For Exponential Distribution :
Average simulation end time = 3.08 hours
Hence, we decided parameters for other 2 loading functions will be as
following:
• For Rayleigh function:
Maximum mobilization time = 3 hours
• For S-curve function:
Half loading time = 1.5 hours
Analysis of emergency state evacuation using simulation using large scale simulation 29

30. Model analysis ( Stage 2)
Mean trip time (in minutes) from TAZ 1055
To destination 1780 To destination 1777
0 2 4 6 8 0 2 4 6 8
Legends:
To destination 1776 To destination 1782 S-curve function
Rayleigh function
Exponential
distribution
0 2 4 6 8 0 2 4 6 8
Analysis of emergency state evacuation using simulation using large scale simulation 30

31. Model analysis ( Stage 2)
Mean trip time (in minutes) from TAZ 1061
To destination 1780 To destination 1777
0 2 4 6 8 0 20 40 60 80 100
Legends:
To destination 1776 To destination 1782 S-curve function
Rayleigh function
Exponential
distribution
0 20 40 60 80 100 0 2 4 6 8
Analysis of emergency state evacuation using simulation using large scale simulation 31

32. Model analysis ( Stage 2)
Mean trip time for all the routes in the
Destinations ==> network (in minutes)
Exponential distribution 47.19
Rayleigh distribution 39.51
S-curve 15.17
S-curve
Rayleigh distribution
Exponential distribution
0.00 10.00 20.00 30.00 40.00 50.00 60.00
Time (min)
Analysis of emergency state evacuation using simulation using large scale simulation 32

33. Analysis summary
• Among the 3 demand loading functions
Exponential distribution is the most inefficient
one as it causes the most traffic congestions
• S-curve function performs the best as it
creates the least traffic congestions
Analysis of emergency state evacuation using simulation using large scale simulation 33

34. Model analysis ( Stage 3)
Evaluating the effect of different alpha values for a fixed half loading
time for S-curve function
Here,
Half loading time = 1.5 hours
Total evacuation time Mean trip time considering all
6 routes
Simulation time in hrs
40
Time in minutes
4
30
2 20
10
0 0
3.5 4 5 6 8 14 3.5 4 5 6 8 14
alpha alpha
Analysis of emergency state evacuation using simulation using large scale simulation 34

35. Critical parameters for the sample network
using S-curve approach
Simulation From TAZ 1061 to Evacuation time in hours
H
ended 1777 1776 14.00
(in hours)
(in hours) (in min) (in min) 12.00
1.50 5.07 25.97 25.78 10.00
Time in hrs
8.00
2.00 4.57 19.06 18.87
6.00
3.00 6.07 16.79 16.60
4.00
3.50 8.57 5.47 5.23 2.00
4.00 12.07 4.12 3.87 0.00
5.00 12.57 4.12 3.87 1.50 2.00 3.00 3.50 4.00 5.00 6.00
6.00 13.07 4.12 3.88 Half Time, H (hrs)
Mean trip time considering all the 8 routes Mean trip time (min) from origin 7533 to 2 different
16.00 destinations
14.00 30.00
12.00 25.00
Time in minutes
10.00 Time in minutes 20.00
8.00
15.00
6.00 1777
10.00
4.00
1776
2.00 5.00
0.00 0.00
1.50 2.00 3.00 3.50 4.00 5.00 6.00 1.50 2.00 3.00 3.50 4.00 5.00 6.00
Half Time, H (hrs) Half Time, H
Analysis of emergency state evacuation using simulation using large scale simulation 35

36. Summary
• There is a critical half loading time such
that, beyond which the network’s traffic
congestion remains the same but the
evacuation end time increases
• For the sample network this critical half
loading time is 4 hours
Analysis of emergency state evacuation using simulation using large scale simulation 36

37. Future research
• The graphical representation such as traffic animation as an
output of the simulation will be a great addition to its
features
• Implement a more dynamic approach in the automated
traffic signaling
• Creation of an algorithm which will update the vehicle’s
shortest path during evacuation
• Investigate the incidents like vehicles run out of gas through
simulation model
• Creation of an algorithm which will replace the existing
traffic loading models to the simulation system and provide
a more dynamic way to load the traffic into the network.
Analysis of emergency state evacuation using simulation using large scale simulation 37

38. Thank You!
Questions?
Analysis of emergency state evacuation using simulation using large scale simulation 38