San Q uentin Prison Break

1. San Quentin Prison Break LT Matt Mooshegian Capt Bryan Jadro

2. 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

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

4. 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?

5. 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

6. 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

7. Network Model (Edges) • Start to Police Departments • Police Departments to Checkpoints • Checkpoints to End

16. B1 Vallejo B2 B3 El Cerrito R1 R2 San Rafael R3 R4 s Marin County t R5 CP1 Richmond CP2 CP3 Oakland CP4 CP5 SFPD CP6

17. 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

18. 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

19. Final Model • Primal LP: min Individual officer travel time s.t. Network flow constraints Capacity constraints Lower bounds

21. 1 Attack

22. 2 Attacks

23. 3 Attacks

24. Operator Resilience Curve

26. 1 Attack

27. 2 Attacks

28. 3 Attacks

29. 4 Attacks

30. 5 Attacks

31. 6 Attacks

32. 7 Attacks

33. Operator Resilience Curve

34. 4 Attacks

35. 5 Attacks

36. 6 Attacks

37. 7 Attacks

38. 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

39. Message to Stakeholders • Law Enforcement • Ensure each police department has enough units to satisfy most demanding checkpoint Prisoner/Accomplices • Focus on attacking routes to SFO

40. 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

41. Other Applications • Expandable to multi-response scenarios • Fire Department response to multiple fires • EMT response to multiple accidents