Improving Parking Garage Efficiency using Reservation Optimization Techniques

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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

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OUTLINE

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

4
GOALS
Improve parking garage operation efficiency

1. Track car position for real-time monitoring
2. Improve reservation efficiency in garages using
   reservation defragmentation techniques
3. 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

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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
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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
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OUTLINE

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

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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?

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OUTLINE

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

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SYSTEM ARCHITECTURE

• Overall System

Tracking Sub-System
Reservation Defragmentation Sub-System
Parking Garage Entrance Console
Database
Revenue Management Sub-System
Remote Client
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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)
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SYSTEM ARCHITECTURE

• Reservation Defragmentation

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

• Revenue Management

-Decide Parameters -Run Booking Limit Algorithm
Set Booking Limits
Database
-Decide Parameters -Run Overbooking Algorithm
Set Overbooking capacity
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OUTLINE

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

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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
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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
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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
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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 50 tolerance

• Example of 75 tolerance

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ALGORITHMS

• II. Reservation Defragmentation
• Usage of bitmap

Parking Spot Index
1 2 3 4 5 . . . . . 500
0000 1 1 1 0 0 0 0
0030 0 0 1 1 1 1 1
0100 0 0 0 0 0 1 0

2330 0 0 0 0 1 1 1

• Bitmap indicates if reservation is made for that
  spot and time.
• It is a matrix of 1s and 0s 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
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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
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ALGORITHMS

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

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ALGORITHMS

• II. Reservation Defragmentation
• Basic Components

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ALGORITHMS
II. Reservation Defragmentation
Parking Spot Index

• 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

0 1 2 3 4 5 6 7
1
6
8
11
0 1 2 3 4 5 6
Time slot Index
2
5
7
10
12
4
3
9
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ALGORITHMS

• II. Reservation Defragmentation
• Implemented Algorithms

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ALGORITHMS

• Reservation Defragmentation
• b) Algorithm R2 Recursive First Fit
  Decreasing(5)

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ALGORITHMS
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• II. Reservation Defragmentation
• Algorithm R2 Example

8
Reservations sorted according to durations
7
12
1
4
11
2
10
9
5
3
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ALGORITHMS

• II. Reservation Defragmentation
• Algorithm R2 Example-gtCurrent Day

Parking Spot Index
Parking Spot Index
0 1 2 3 4 5 6 7
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
11
8
1
Post-Defrag
7
10
12
Time Slot Index
Time Slot Index
4
3
9
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ALGORITHMS

• Reservation Defragmentation
• Algorithm R3 Example-gt Current Day

Parking Spot Index
Parking Spot Index
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
Time slot
8
11
1
2
5
Post-Defrag
Time Slot Index
Time Slot Index
7
10
12
4
3
9
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ALGORITHMS

• II. Reservation Defragmentation
• Algorithm R1 Example-gtNext Day

Parking Spot Index
Parking Spot Index
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
Current Time
0 1 2 3 4 5 6
6
11
1
8
2
5
Post-Defrag
Time Slot Index
7
10
12
Time Slot Index
4
3
9
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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
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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
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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

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ALGORITHMS

• Mathematical decision
• If we protect Q1 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) lt Rl F(Q) Cumulative
Probability Rh Corporate class fare Rl
Leisure class fare

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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
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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 1.645STD)
Where, AU Total Overbooked Capacity (in 30
minute slots) CAP Garage Capacity (in
hours) NSR No-show rate STD Std. Deviation of
NSR
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Outline

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

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RESULTS

• I. Tracking
• Simulation Parameters

Sr. No. Parameter Value Garage Operator Parameters
1. Sensor failure 2,5,10,20,50 Yes
2. Speed Limit Max. limit of 30.23mph Yes
3. Arrival Distribution Poisson Distribution Yes
4. Customer Arrival Rate 100 cars per hour Yes
5. Garage Capacity 500 parking spots Yes
6. Performance Metric 10, 50 and 75 tolerance Yes
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RESULTS

• I. Tracking

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RESULTS
I. Tracking
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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
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RESULTS

• I. Tracking
• Conclusions
• Information Provided
• Implementation Costs

3
Algorithm T2
Algorithm T1
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Algorithm T2
Algorithm T1
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RESULTS

• II. Reservation Defragmentation
• Simulation Parameters

Sr. No. Parameter Value
1. Garage Capacity 500 parking spots
2. Period of observation 24 hours
3. Duration of reservations 30 minutes to 22 hours
4. No-show rate 15
5. Type of reservations Next-day reservations
6. Performance Metric 1 Time slots freed
Performance Metric 2 Parking spots freed
Performance Metric 3 Length of contiguous free time slots
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RESULTS

• II. Reservation Defragmentation

1750 Reservations 95 of maximum capacity
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RESULTS

• II. Reservation Defragmentation

1750 Reservations 95 of maximum capacity
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RESULTS

• II. Reservation Defragmentation

48 time slots (max. number of free time slots per
parking spot)
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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
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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.

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RESULTS

• II. Reservation Defragmentation
• Simulation Parameters

Sr. No. Parameter Value
1. Garage Capacity 500 parking spots
2. Period of observation 24 hours
3. Duration of reservations 30 min to 22 hrs (exp. dist.)
4. No-show rate 15
5. Type of reservations Current-day reservations
6. Performance Metric Increase in garage capacity
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RESULTS

• II. Reservation Defragmentation

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RESULTS

• III. Revenue Management
• Simulation Parameters

Sr. No. Parameter Value
1. Garage Capacity 500 parking spots
2. Fare classes Leisure Class and Corporate class
3. Leisure Fare/Corporate Fare 0.166-0.75 (based on airlines)
4. Corporate Customer Arrival Distribution Poisson Distribution
5. Corporate Customer Arrival Rate 5 cars/hour to 500 cars/hour
6. Performance Metric Protection level and Booking Limit
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RESULTS

• III. Revenue Management

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RESULTS

• III. Revenue Management
• Simulation Parameters

Sr. No. Parameter Value
1. Garage Capacity 500 parking spots
2. Fare classes Leisure Class and Corporate class
3. Leisure Fare/Corporate Fare 0.166-0.75 (based on airlines)
4. Corporate customer arrival distribution Binomial Distribution (used for heavy traffic with uniform distribution)
5. Corporate customer arrival Probability 10 - 90
6. Performance Metric Protection level and Booking Limit
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RESULTS

• III. Revenue Management

Rl/Rh
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RESULTS

• Revenue Management
• Conclusions
• With Poisson arrival distribution, increase in
  corporate customer arrival rate and rate ratio
  leads to an exponential increase in protection
  level
• For Poisson distribution, increase in arrival
  rate of corporate customers causes more
  proportional increase in protection level as
  compared with increase in ratio of leisure class
  to corporate class fare.
• With Binomial distribution, as the probability of
  an entering customer to be a corporate customer
  increases, with increasing rate ratio, protection
  level increases almost linearly.
• Hence, for optimum protection level, irrespective
  of arrival distribution, the parking garage
  should have a high corporate customer arrival and
  high Rl/Rh ratio.

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RESULTS

• III. Revenue Management
• Simulation Parameters

Sr. No. Parameter Value
1. Garage Capacity 500 parking spots
2. No-show distribution Gaussian
3. No-show rate 10 - 50
4. Std. Dev. Of No-show rate 0.01 - 0.5
4. Customer Arrival Distribution Poisson
5. Performance Metric Overbooking Capacity
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RESULTS

• III. Revenue Management

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RESULTS

• Revenue Management
• Conclusions
• For low values of standard deviation of no-show
  rate, overbooking is useful since we can afford
  to book more reservations than the maximum
  capacity of parking garage.
• For high values of standard deviation of no-show
  rate, overbooking is futile since we are not even
  able to reach capacity booking
• For high values of no-show rate, we see higher
  values of overbooking as compared with low values
  of no-show rate.
• .

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Outline

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

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CONCLUSIONS

• I. Discussion of Research Questions
• A suitable tracking metric was
• developed

• A suitable reservation
• defragmentation
• metric was
• developed

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CONCLUSIONS

• I. Discussion of Research Questions
• Suitable modifications were
• made to obtain booking limits
• for parking garages.

• Modification of overbooking
• for parking garage led to a
• suitable metric

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FUTURE WORK

• Usage of a prediction model which would give some
  prior knowledge about the future usage of system
  which would enable allocating spots to users in a
  more efficient manner.
• Explore feasibility of n-fare booking limits for
  parking garage
• Perform overbooking only on certain booking
  classes
• Look into implementation of overbooking with
  reservation defragmentation

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Thank You!