ANALYSIS OF EMERGENCYEVACUATION USING LARGE-SCALE         SIMULATION            Presented by:         Ahsanur Rahman      ...
Presentation Outline   • Research goals   • Why Python ?   • SimPy based simulation model        – Program Logic        – ...
Research Goals• Reproduce an existing Mesoscopic transportation  evacuation model (DOE_EVAC) using a general  purpose prog...
Why Python ?   Python and SimPy:   Python is a general purpose programming language .   And SimPy is an object oriented, p...
Traffic simulation approaches       • Microscopic simulation           Simulates detailed behaviour of every individual v...
SimPy Based Simulation Model Model highlights 1. It follows the Mesoscopic simulation approach 2. It can effectively simul...
Program LogicAnalysis of emergency state evacuation using simulation using large scale simulation   7
Simulating traffic signals1) Non electronic signals   Example: STOP sign                                                  ...
Simulating traffic signals                                       Simulation example for 3 way linksAnalysis of emergency s...
Simulating traffic • Vehicles      – They take the shortest route from origin to destination to get out of        the disa...
Simulating trafficsAnalysis of emergency state evacuation using simulation using large scale simulation   11
Simulation execution   Assumptions        – The affected area is known or at least the tentative affected area is         ...
Sample geographical area     Risk area     The geographical area that has the possibility to be affected by disaster. The ...
Sample transportation network • Nodes • Links • IntersectionsAnalysis of emergency state evacuation using simulation using...
A sample networkAnalysis of emergency state evacuation using simulation using large scale simulation   15
Network parameters for simulation                        Property name                TAZ 1055             TAZ 1061       ...
An example of pedestrian routeAnalysis of emergency state evacuation using simulation using large scale simulation   17
An example of public transport routeAnalysis of emergency state evacuation using simulation using large scale simulation  ...
An example of other vehicle routeAnalysis of emergency state evacuation using simulation using large scale simulation   19
Sample simulation outputEnd of evacuation In hours        In minutes          In seconds 3.08            184.5            ...
Sample simulation output   Numbers for vehicles                                              Average time to reach destina...
Validation of SimPy based model                  Simulation time in hours    Replication                    Python       A...
Model validationAnalysis of emergency state evacuation using simulation using large scale simulation   23
DefinitionsRoute free flow time:It is the summation of the free flow times of a particular route. Hence, it is the minimum...
Model Analysis (stage 1)                        Route               Mean vehicle trip time (min)   Free flow time (min)   ...
Demand loading functions    The following demand loading functions were used    to load the traffics into the simulation m...
Rayleigh function                                     1.2                                      1              Cumulative l...
S-curve function                                                       1                              Cumulative loading %...
Model analysis (Stage 2)  Selecting comparable parameters for demand loading functions   • For Exponential Distribution : ...
Model analysis ( Stage 2) Mean trip time (in minutes) from TAZ 1055 To destination 1780                       To destinati...
Model analysis ( Stage 2)    Mean trip time (in minutes) from TAZ 1061To destination 1780                           To des...
Model analysis ( Stage 2)                                             Mean trip time for all the routes in the            ...
Analysis summary   • Among the 3 demand loading functions     Exponential distribution is the most inefficient     one as ...
Model analysis ( Stage 3)          Evaluating the effect of different alpha values for a fixed half loading               ...
Critical parameters for the sample network     using S-curve approach                                 Simulation        Fr...
Summary   • There is a critical half loading time such     that, beyond which the network’s traffic     congestion remains...
Future research   • The graphical representation such as traffic animation as an     output of the simulation will be a gr...
Thank You!                               Questions?Analysis of emergency state evacuation using simulation using large sca...
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ANALYSIS OF EMERGENCY EVACUATION USING LARGE-SCALE SIMULATION

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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.

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ANALYSIS OF EMERGENCY EVACUATION USING LARGE-SCALE SIMULATION

  1. 1. ANALYSIS OF EMERGENCYEVACUATION USING LARGE-SCALE SIMULATION Presented by: Ahsanur Rahman Thesis supervisor: Dr. Suleyman Karabuk
  2. 2. Presentation Outline • Research goals • Why Python ? • SimPy based simulation model – Program Logic – Simulation Execution – A sample network • Model analysis • Future researchAnalysis of emergency state evacuation using simulation using large scale simulation 2
  3. 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 modelAnalysis of emergency state evacuation using simulation using large scale simulation 3
  4. 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 ARENAAnalysis of emergency state evacuation using simulation using large scale simulation 4
  5. 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 simulationAnalysis of emergency state evacuation using simulation using large scale simulation 5
  6. 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. 7. Program LogicAnalysis of emergency state evacuation using simulation using large scale simulation 7
  8. 8. Simulating traffic signals1) Non electronic signals Example: STOP sign RED 2) Electronic signals Yellow GreenAnalysis of emergency state evacuation using simulation using large scale simulation 8
  9. 9. Simulating traffic signals Simulation example for 3 way linksAnalysis of emergency state evacuation using simulation using large scale simulation 9
  10. 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 areaAnalysis of emergency state evacuation using simulation using large scale simulation 10
  11. 11. Simulating trafficsAnalysis of emergency state evacuation using simulation using large scale simulation 11
  12. 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 accountAnalysis of emergency state evacuation using simulation using large scale simulation 12
  13. 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) codesAnalysis of emergency state evacuation using simulation using large scale simulation 13
  14. 14. Sample transportation network • Nodes • Links • IntersectionsAnalysis of emergency state evacuation using simulation using large scale simulation 14
  15. 15. A sample networkAnalysis of emergency state evacuation using simulation using large scale simulation 15
  16. 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 pedestriansAnalysis of emergency state evacuation using simulation using large scale simulation 16
  17. 17. An example of pedestrian routeAnalysis of emergency state evacuation using simulation using large scale simulation 17
  18. 18. An example of public transport routeAnalysis of emergency state evacuation using simulation using large scale simulation 18
  19. 19. An example of other vehicle routeAnalysis of emergency state evacuation using simulation using large scale simulation 19
  20. 20. Sample simulation outputEnd of evacuation In hours In minutes In seconds 3.08 184.5 11070.104Evacuee 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 77.02Numbers 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 centerAnalysis of emergency state evacuation using simulation using large scale simulation 20
  21. 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 6.02 361.6Analysis of emergency state evacuation using simulation using large scale simulation 21
  22. 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 2.13Analysis of emergency state evacuation using simulation using large scale simulation 22
  23. 23. Model validationAnalysis of emergency state evacuation using simulation using large scale simulation 23
  24. 24. DefinitionsRoute free flow time:It is the summation of the free flow times of a particular route. Hence, it is the minimumtime that a vehicle should take to reach its destinationAnalysis of emergency state evacuation using simulation using large scale simulation 24
  25. 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 congestionsAnalysis of emergency state evacuation using simulation using large scale simulation 25
  26. 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 functionAnalysis of emergency state evacuation using simulation using large scale simulation 26
  27. 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 minutesAnalysis of emergency state evacuation using simulation using large scale simulation 27
  28. 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 minutesAnalysis of emergency state evacuation using simulation using large scale simulation 28
  29. 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 hoursAnalysis of emergency state evacuation using simulation using large scale simulation 29
  30. 30. Model analysis ( Stage 2) Mean trip time (in minutes) from TAZ 1055 To destination 1780 To destination 17770 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 8Analysis of emergency state evacuation using simulation using large scale simulation 30
  31. 31. Model analysis ( Stage 2) Mean trip time (in minutes) from TAZ 1061To destination 1780 To destination 17770 2 4 6 8 0 20 40 60 80 100 Legends:To destination 1776 To destination 1782 S-curve function Rayleigh function Exponential distribution0 20 40 60 80 100 0 2 4 6 8Analysis of emergency state evacuation using simulation using large scale simulation 31
  32. 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. 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 congestionsAnalysis of emergency state evacuation using simulation using large scale simulation 33
  34. 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 alphaAnalysis of emergency state evacuation using simulation using large scale simulation 34
  35. 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 25.00Time 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, HAnalysis of emergency state evacuation using simulation using large scale simulation 35
  36. 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 hoursAnalysis of emergency state evacuation using simulation using large scale simulation 36
  37. 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. 38. Thank You! Questions?Analysis of emergency state evacuation using simulation using large scale simulation 38

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