Improving Parking Garage Efficiency using Reservation Optimization Techniques

1. Improving Parking Garage Efficiency using Reservation Optimization Techniques By Arjun Rao Advisor : Dr. Ivan Marsic Committee Members : Dr. Joseph Wilder Dr. Manish Parashar

2. INTRODUCTION • Problems with Parking Garages • No reservation policy • Only display of rates and location • No reservation of parking spots • Ambiguity of information • Display of number of parking spots available creates ambiguity • Environmental concerns • 40% of total traffic (1) • 47000 gallons of gas was used up in a year in a business district of LA(1) • Lack of revenue management

3. OUTLINE • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

4. GOALS Improve parking garage operation efficiency • Track car position for real-time monitoring • Improve reservation efficiency in garages using reservation defragmentation techniques • Improving revenue for parking garages using revenue management techniques

5. OUTLINE • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

6. PROPOSED SOLUTIONS • What is Tracking of a car in a parking garage? • Knowing real-time position from entrance up to parking. • Obtaining knowledge of which parking spot has the car been actually parked in • Tracking is simulated based on real-world parameters

7. PROPOSED SOLUTIONS • What is Reservation Defragmentation? • Aim to free parking spots so • as to accommodate more • parking reservations • Re-arrangement of reservations • to increase efficiency • Similar to disk defragmentation • in principle. Reservations moved due to defragmentation Reservations not moved even after defragmentation

8. PROPOSED SOLUTIONS • What is Revenue Management? • Implemented Types: • -Booking Limits: • Classifying parking spots in garage based on fare to increase revenue • -Overbooking: • Permitting reservations beyond capacity of parking garage to account for no-shows • Spoilage Costs • Denied Parking Corporate Class Leisure Class Booking Limits Overbooking

9. OUTLINE • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

10. RESEARCH QUESTIONS • Tracking • What method can be used to track cars? • What metrics should be selected to show effectiveness of these algorithms? • Reservation Defragmentation • What methods can be used for packing more number of reservations into the garage? • What metrics should be chosen to demonstrate efficiency of such algorithms? • Revenue Management • What techniques can be used for revenue management? • Can these techniques from other industries be directly be ported over to the parking garages?

11. OUTLINE • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

12. SYSTEM ARCHITECTURE • Overall System Tracking Sub-System Reservation Defragmentation Sub-System Parking Garage Entrance Console Database Revenue Management Sub-System Remote Client

13. SYSTEM ARCHITECTURE • Tracking System Parking Lot Functions ______________ -Mark entry -Track -Park - Provide new spot -Determine accuracy Simulator ______________ -Arrival Thread -Sensor detection -Path vectors - Modified spot generation Database (MySQL)

14. SYSTEM ARCHITECTURE • Reservation Defragmentation Parking Lot Functions ______________ -Make reservation -Defragmentation -Update reservations Simulator ______________ -Reservation thread -Bitmap/Vector allocation -Defragmentation thread Database (MySQL)

15. SYSTEM ARCHITECTURE • Revenue Management -Decide Parameters -Run Booking Limit Algorithm Set Booking Limits Database -Decide Parameters -Run Overbooking Algorithm Set Overbooking capacity

16. OUTLINE • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

17. ALGORITHMS I. Tracking • Sensor details • Sensor action is simulated using real-world commercially available sensor data (cost and accuracy). • Ultrasonic sensors used for car detection (motion and occupancy) • Sensors are used for inter-floor and intra-floor motion detection • All sensors are ceiling mounted Ultrasonic Sensor Prototype(2) Wiring up ultrasonic sensors(2) Example of a parking garage with ultrasonic sensors

18. ALGORITHMS • Tracking a) Algorithm T1 • More sensors used • High Accuracy/High cost • Algorithm tracks car based on sensor crossed • Features included • Track car path • Dynamic allocation Floor Exit Sensor Floor Entry Sensor

19. ALGORITHMS • Tracking b) Algorithm T2 • Fewer sensors used • Low Accuracy/Low cost • Algorithm tracks car based on sensor crossed • Features included • Track car path • Dynamic allocation Floor Exit Sensor Floor Entry Sensor

20. ALGORITHMS • Tracking • c) Performance metrics • Inaccurate Tracking • - Fail to detect occupancy sensor • OR • - Fail to achieve the tolerance limit (10%,50%,75% ) • Number of sensor points Example of 75% tolerance Example of 50% tolerance

21. ALGORITHMS II. Reservation Defragmentation • Usage of bitmap Parking Spot Index • Bitmap indicates if reservation is made for that spot and time. • It is a matrix of 1’s and 0’s having ‘m’ rows each indicating ‘30 minutes’ of time and ‘n’ columns indicating parking spot index. • ‘1’ indicates ‘Reservation made’ and ‘0’ indicates ‘Free space’. Time (hours) Bitmap matrix

22. ALGORITHMS II. Reservation Defragmentation • Bitmap Terminology Current Time 0 1 2 3 4 5 6 7 8 9 Contiguous Free Time Slot for Spot 4 Reservation Made Slot Index Free Space Contiguous Free Time Slot for Spot 4 0 1 2 3 4 Parking Spot Index

23. ALGORITHMS II. Reservation Defragmentation • Types of reservations used • Next Day Reservations • Current Day Reservations

24. ALGORITHMS II. Reservation Defragmentation • Basic Components

25. ALGORITHMS II. Reservation Defragmentation a) First Fit Algorithm(3) • Attempts to place the reservation in the first parking spot that can accommodate the reservation. • Easy to implement. • Fast allocation • Inefficient allocation Parking Spot Index 0 1 2 3 4 5 6 7 6 8 1 11 0 1 2 3 4 5 6 Time slot Index 2 5 7 10 12 4 3 9

26. ALGORITHMS II. Reservation Defragmentation • Implemented Algorithms

27. ALGORITHMS • Reservation Defragmentation b) Algorithm R2: Recursive First Fit Decreasing(5)

28. 28 ALGORITHMS Sort II. Reservation Defragmentation • Algorithm R2: Example 8 Reservations sorted according to durations 7 12 1 4 11 2 10 9 5 3

29. ALGORITHMS II. Reservation Defragmentation • Algorithm R2: Example->Current Day 6 Parking Spot Index Parking Spot Index Time slot 8 0 1 2 3 4 5 6 7 11 2 5 1 0 1 2 3 4 5 6 7 Current Time 0 1 2 3 4 5 6 0 1 2 3 4 5 6 6 2 5 8 11 1 7 10 12 Post-Defrag 4 3 7 10 12 Time Slot Index Time Slot Index 4 3 9 9

30. ALGORITHMS • Reservation Defragmentation • Algorithm R3: Example-> Current Day Parking Spot Index 6 Parking Spot Index 0 1 2 3 4 5 6 7 Current Time Time slot 0 1 2 3 4 5 6 8 11 2 5 1 0 1 2 3 4 5 6 6 Time slot 8 11 2 5 1 Post-Defrag 7 10 12 Time Slot Index Time Slot Index 12 7 10 4 3 4 3 9 9

31. ALGORITHMS II. Reservation Defragmentation • Algorithm R1: Example->Next Day 6 8 11 1 Parking Spot Index Parking Spot Index Time slot 0 1 2 3 4 5 6 7 2 5 0 1 2 3 4 5 6 7 Current Time 0 1 2 3 4 5 6 11 6 8 1 5 2 7 10 12 Post-Defrag Time Slot Index 4 3 7 10 12 Time Slot Index 4 3 9 9

32. II. Reservation Defragmentation ALGORITHMS • Performance Metrics • Percentage Reduction in Free time slots = Number of empty time slots (Pre-defrag)-Number of empty time slots(Post-defrag) Number of empty time slots (Pre-Defrag) • Percentage Decrease in Occupied Parking spots = Number of empty parking spots (Pre-defrag)-Number of empty parking spots(Post-defrag) Number of empty time slots (Pre-Defrag) 100 100

33. ALGORITHMS II. Reservation Defragmentation • Performance Metrics • Reduction in mean length of contiguous free time slot (say Mx) : • Calculate total number of free time slots per parking spot (say FTM) • Calculate number of sets of contiguous time slots per parking spot (say S) Mx = FTM / S • Percentage Increase in garage capacity = Total number of cars in garage( Post-defrag) - Total number of cars in garage (pre-defrag) Total number of cars in garage (Pre-defrag) 100

34. ALGORITHMS III. Revenue Management a) Booking Limits Algorithm(7) • Two fare class model (Leisure class and • Corporate class • Booking Limit = C – Q* • Where • C = Capacity of garage • Q* = Optimal Protection level • Calculate F(Q) where • F(Q) is the cumulative probability of • demand for the spot at the corporate • class cost given that Q is the • protection level. Traditionally, derived • from historical data but in our case • derived from simulation based on • real- world values

35. ALGORITHMS • Mathematical decision: • If we protect Q+1 spots for the corporate class, then we should lower the protection to ‘Q’ as long as: III. Revenue Management a) Booking Limits Algorithm (1 – F(Q)) (Rh) <= Rl ; F(Q) = Cumulative Probability Rh = Corporate class fare Rl = Leisure class fare

36. ALGORITHMS • Revenue Management b)Overbooking Algorithm: Probabilistic/Risk Model(8) Basic Equation AU × (1-NSR)* = CAP • Probability equation decides amount of overbooking to be done. • Overbooking (AU) on a garage [capacity (CAP)] such that we have a minimum number of customers denied parking. • Gaussian no-show rate (NSR) for reservations. Overbooking

37. ALGORITHMS • Revenue Management c) Overbooking Algorithm: Probabilistic/Risk Model Formula • Difference between airline and garage overbooking • Overbooking amount is calculated prior to reservations being made. • Overbooking done on entire garage capacity. AU = _______CAP_______ (1-NSR + 1.645*STD) Where, AU = Total Overbooked Capacity (in 30 minute slots) CAP = Garage Capacity (in hours) NSR = No-show rate STD = Std. Deviation of NSR

38. Outline • Goals • Proposed Solutions • Research Questions • System Architecture • Algorithms • Results • Conclusions and Future Work

39. RESULTS I. Tracking • Simulation Parameters

40. RESULTS I. Tracking

41. RESULTS I. Tracking

42. RESULTS I. Tracking • Conclusions • With increase in sensor failure rate, increase in inaccurate tracking is exponential • Algorithm T2 is more inaccurate than Algorithm T1 due to usage of fewer sensors • Higher the failure tolerance, lesser are the inaccurate readings Sensor Failure Rate Inaccurate tracking Inaccuracies observed Failure Tolerance

43. RESULTS I. Tracking • Conclusions • Information Provided • Implementation Costs 3 Algorithm T2 Algorithm T1 11 Algorithm T2 Algorithm T1

44. RESULTS II. Reservation Defragmentation • Simulation Parameters

45. RESULTS II. Reservation Defragmentation 1750 Reservations 95% of maximum capacity

46. RESULTS II. Reservation Defragmentation 1750 Reservations 95% of maximum capacity

47. RESULTS II. Reservation Defragmentation 48 time slots (max. number of free time slots per parking spot)

48. RESULTS II. Reservation Defragmentation 48 time slots (max. number of free time slots per parking spot) Mean Length of number of free time slots For 1750 reservations

49. RESULTS II. Reservation Defragmentation Conclusions (Next day) • Increase in number of reservations causes increase in percentage defragmentation • Algorithm R2 provides best defragmentation in terms of metrics when random cancellation is carried out. • Algorithm R3 provides improved parking garage spot occupancy when block cancellation is carried out. • Std. deviation for R2 is lesser than R1 and R3 indicating more predictability of algorithm R2.

50. RESULTS II. Reservation Defragmentation • Simulation Parameters