1. Group 6: Paul Antonios, Tamara Dabbas, Justin Fung, Adib Ghawi, Nazli Guran, Donald McKinnon, Alara Tascioglu Quantitative Capacity Building for Emergency Evacuations of Manhattan, NYC: Manhattan Evacuation Simulation Application (MESA) - A time-oriented application for modeling capacity-constrained scenarios as a network model

2. “An efficient evacuation of Manhattan is impossible.” -Joel Friedman, P.E., Chief Engineer of NYC Department of Transportation

4. Our Goal 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.

5. Initial Hypothesis Initial formulation and methods of transportation are available A Linear Programming Formulation: Objective Function: Minimize maximum evacuation time of populations Subject to: Constrained Capacities, modes of transport available Roadway Vehicles (Cars, taxis, buses…) Subways & Railways Ferries Planes Ships & Boats (Both public and private Bikes & skateboards Walking Hot air balloons

6. Data Collection What data do we need? Where did we get it from? 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)

7. Data Assumptions 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 on a documentary about ferry evacuation during 9/11 Capacity beyond the geographic area: Not incorporated into the model

8. Different Models What algorithms did we take into consideration? Different Models Algorithms considered: Capacity Constrained Route Planner (CCRP) Algorithm Floyd-Warshall’s Algorithm Our Algorithm (MESA)

9. Different Models Common Points 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 with the minimal paths 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.

10. Different Models Floyd-Warshall and CCRP Floyd-Warshall’s Algorithm: Advantages: Runs in n 3 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

11. MESA Algorithm Different Models 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

12. Model Assumptions 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.

13. Application Development Three types of nodes: 1.Source nodes 2.End nodes 3.Intermediary nodes Edges represent traversable routes between nodes.

14. Explanation of the Algorithm 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.Minimize the total elapsed exit time 2.Minimize the average exit time for each starting location 3.Maximize the total elapsed exit time (worst-case) 4.Maximize the average exit time for each starting location (worst-case)

15. MESA Quick look at the application

16. Data Analysis & Sample Simulations Total Evacuation Time

17. Data Analysis & Sample Simulations Effect of Excluding the Subway Transportation System

18. Data Analysis & Sample Simulations Effect of Excluding Bridges and Tunnels South of 14 th Street

19. Data Analysis & Sample Simulations Effect of Increasing Population of Financial District by 50%

20. Data Analysis & Sample Simulations Effect of Increasing Subway System Efficiency

21. Future Research 1. Capacity Improvement 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

22. Future Research 2. Algorithmic Improvements 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

23. Questions?