San Quentin Prison Break

LT Matt MooshegianCapt Bryan Jadro

Background

• November 2012 SFPD arrest notorious drug kingpin Jose “El Torro” Velasquez.

• Velasquez is sent to San Quentin Prison while the U.S and Mexico begin extradition talks.

• Prison officials fear an escape attempt

Agenda• Problem Statement• Assumptions• Network Introduction• Model Introduction• Summary• Future Research• Questions

Problem Statement

• Analyze the current configuration of police roadblocks in order to determine which ones possess the greatest risk of facilitating a high profile prisoners escape.

-How will attacks on the network affect the police’s ability to meet their goal of establishing all checkpoints within 60 minutes?

-How many attacks are necessary to significantly impact response times?

Assumptions

• Police force available is proportional to size of respective police department

• 1 police unit consists of one police officer and 1 police car• Demand at checkpoints are predetermined• Once informed, all police stations respond at an equal rate• No lag time from dispatch to the deployment of the police

force• Police move at speed limit• Traffic is not a factor in time to reach checkpoint

Network Model (Nodes)

• Police Departments (7)– San Francisco PD– Oakland PD– El Cerrito PD– Richmond PD– Marin County PD– Vallejo PD– San Rafael PD

• Checkpoints (14)– 3 Bridges– 5 Roadblock Locations– 6 Checkpoint Locations

• Start Node• End Node

Network Model (Edges)

• Start to Police Departments• Police Departments to Checkpoints• Checkpoints to End

s t

SFPD

Oakland

Richmond

Marin County

Vallejo

San Rafael

El Cerrito

B1

B2

B3

R1

R2

R3

R4

R5

CP1

CP2

CP3

CP4

CP5

CP6

Min-Cost or Multi-Commodity Flow

• First tackled problem as a min-cost problem– GAMS output was in in total time– Did not provide the insight we desired

• Re-analyzed as a multi-commodity flow problem where each police officer represents a different commodity

Multi-Commodity Flow

• Purpose: Minimize response times for surrounding police departments to establish a network of checkpoints

• Each police officer is a different commodity– Want to determine which police officer

(commodity) is taking the longest to reach their checkpoint

– Subsequently determine the number and location of attacks to break network

Final Model

• Primal LP:min Individual officer travel time

s.t. Network flow constraintsCapacity constraintsLower bounds

1 Attack

2 Attacks

3 Attacks

Operator Resilience Curve

1 Attack

2 Attacks

3 Attacks

4 Attacks

5 Attacks

6 Attacks

7 Attacks

Operator Resilience Curve

4 Attacks

5 Attacks

6 Attacks

7 Attacks

Conclusions

• Network is highly susceptible to failure with a minimal number of attacks and a concentration of police units at 1 or 2 departments

• Attacks center on police departments with the most manpower– SFPD and Oakland PD

• Remaining police departments contribute an insufficient number of police officers to handle the checkpoints

Message to Stakeholders

• Law Enforcement– Ensure each police department has enough units

to satisfy most demanding checkpointPrisoner/Accomplices– Focus on attacking routes to SFO

Future Research

• Increase level of network granularity– More police stations and more checkpoints

• Better estimates of the number of police units available from each police department and requirements per checkpoint

• Consideration for prisoner movements• Mobile dispatch/command centers

Other Applications

• Expandable to multi-response scenarios– Fire Department response to multiple fires– EMT response to multiple accidents