Download Office of Emergency Management Mr. Andrew Mark

Quantitative Capacity Building
Evacuations of Manhattan, NYC:
for
Emergency
Manhattan Evacuation Simulation Application (MESA) A time-oriented application for modeling capacity-constrained
scenarios as a network model
Group 6: Paul Antonios, Tamara Dabbas, Justin Fung, Adib Ghawi, Nazli Guran,
Donald McKinnon, Alara Tascioglu
“An efficient evacuation of Manhattan
is impossible.”
-Joel Friedman, P.E., Chief Engineer of NYC Department of Transportation
OEM challenge: the ability to evacuate all
people from necessary areas in time, using
the limited resources and methods of
transportation available.
Thus, our goal is to supply our client with an
application
that
will
simulate
the
evacuation of Manhattan, New York City.
Our Goal
A Linear Programming Formulation:
Objective Function: Minimize maximum evacuation time of populations
Subject to: Constrained Capacities, modes of transport available
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•
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Roadway Vehicles (Cars, taxis, buses…)
Subways & Railways
Ferries
Planes
Ships & Boats (Both public and private
Bikes & skateboards
Walking
Hot air balloons
Initial Hypothesis
Initial formulation and methods of transportation are available
Data Sources:
• Office of Emergency Management:
• Tunnel & bridge capacities
• Roadway capacities
• Department of Transport:
• Tunnel & bridge capacities
• Charles Komanoff, consultant to NYC transit:
• Subway capacities
• US Census Tract:
• Population data (day/night)
Data Collection
What data do we need? Where did we get it from?
• Population data: Population is dynamic
• Roadway data: Data collected was for cars specifically, not
accounting for buses, motorbikes,…
• Subway data: Built in assumption of 3 square feet per person
on a subway train
• Maritime data: Ferry capacity estimated based
documentary about ferry evacuation during 9/11
on
a
• Capacity beyond the geographic area: Not incorporated into
the model
Data Assumptions
Algorithms considered:
• Capacity Constrained Route Planner (CCRP)
Algorithm
• Floyd-Warshall’s Algorithm
• Our Algorithm (MESA)
Different Models
What algorithms did we take into consideration?
Common Points:
• Neither of them gives the optimal solution as an output
• Finds the shortest path based on the path of all previous
census tracts chosen
• Finds the minimal paths on the graph for all source-end
node combinations
• Source nodes increase the flow over the path that is
chosen to be optimal by the initial population of the node.
Different Models
Common Points
Floyd-Warshall’s Algorithm:
Advantages:
•
Runs in n3 time
•
For each source node, first it calculates the shortest distance between all node pairs
Disadvantages:
•
Assumes a static weight over each edge
•
Does not update the flow over edges throughout the execution of the algorithm
CCRP
Advantages:
•
Has a relatively better run time due to its super source node
Disadvantages:
•
Super source node cannot hold specific data about each source node
•
It takes one order
•
Requires maximum capacity for all nodes and edges
•
Demands constant weight inflow characteristics for all nodes and edges
Different Models
Floyd-Warshall and CCRP
MESA algorithm:
Advantages:
• Considers multiple orders and chooses the best one
• Calculates the weight over each edge
• Updates the flow over as it iterates through the current optimal path
of each source node
Disadvantages:
• Difficult to implement compared to CCRP
Basic Formula:
• Time = function (people that want to use the route, people that are
already using the route, the capacity of the route, adjustment factor
for assumed congestion)
• Dynamically optimizes the evacuation route for every tract
Different Models
MESA Algorithm
• Each entity emanating from a source node (a person)
is assumed to be equal.
• Each person(s) will evacuate and comply with all evacuation
instructions
• The structure of Manhattan – the speed over the available roads,
bridges, tunnels, and subways, does not change throughout the
course of the evacuation
• After reaching an exit point, people automatically and ‘perfectly’
dissipate.
• All Edges can be traversed in both directions.
Model Assumptions
Three types of nodes:
1.
2.
3.
Source nodes
End nodes
Intermediary nodes
Edges represent traversable
routes between nodes.
Application Development
GOAL: Map source nodes to exit nodes; effectively moving a
body of persons within a community district to optimal exit
points via the most efficient route.
Four slightly different algorithms for four different objectives:
1.
2.
3.
4.
Minimize the total elapsed exit time
Minimize the average exit time for each starting location
Maximize the total elapsed exit time (worst-case)
Maximize the average exit time for each starting location
(worst-case)
Explanation of the Algorithm
MESA
Quick look at the application
Data Analysis & Sample Simulations
Total Evacuation Time
Data Analysis & Sample Simulations
Effect of Increasing Population of Financial District by 50%
Data Analysis & Sample Simulations
Effect of Excluding the Subway Transportation System
Roadway Network
Implement Evacuation Circles:
Continually moving routes with outbound
pickups in Manhattan and drop-offs outside Manhattan.
Subway Network
•Decrease stops through Manhattan and increase
continuous travelling
•Have subway act as a shuttle on/off island
Ferries & other waterborne vehicles network
All private boats should be utilized in conjunction with military and public
vehicles
Future Research
1. Capacity Improvement
• Model NYC instead of just Manhattan
• Determine more accurate capacities for all
transportation routes
• Simulate stochastic events
• Implement directed edges and time-delayed
events
• Addressing MESA assumptions:
• Uniform evacuees
• Refugee-effect on neighboring geographies
Future Research
2. Algorithmic Improvements
Questions?