ParkNet

1. ParkNet Drive-by Sensing of Road-Side Parking Statistics Sutha Mathur, Tong Jin, Nikhil Kasturirangan, Janani Chandrashekharan, Wenzhi Xue, Marco Gruteser, Wade Trappe Rutgers University Michael Betancourt UCF - EEL 6788 Dr. Turgut

2. Overview • Introduction • Design Goals and Requirements • Prototype Development • Parking Space Detection • Occupancy Map • Mobility Study • Improvements • Conclusion

3. Introduction - Problems • Traffic congestion costs tons of money • 4.2 billion lost hours • 2.9 billion gallons of gasoline wasted • Looking for parking contributes to these numbers • Lack of information • Hard to determine best prices for meters and where they should be placed • Current parking detection systems are costly

4. Drive-by Parking Monitoring  Uses ultrasonic sensor attached to the side of cars Detects parked cars and vacant spaces Attaches to vehicles that comb through a city (taxi, police, etc.) Location accuracy based on GPS and environmental fingerprinting Introduction - ParkNet

5. Introduction - Objectives • Demonstrating  the feasibility of the mobile sensing approach including the design, implementation and evaluation of the system • Proposing and evaluating a method of environmental fingerprinting to increase location accuracies • Showing that if the mobility system were currently attached to operating taxis, it would operate with enough samples to determine parking availability

6. Design Goals - Real-time Information • Improve traveler decisions with respect to mode of transportation • Suggesting parking spaces to users driving on the road • Allow parking garages to adjust their prices dynamically according to demmand • Improve efficiency of parking enforcement in systems that utilize single pay stations for multiple parking spots

7. Design Goals - Parking Information • Space count • Sufficient for most parking applications • Occupancy Map • Useful for parking enforcemen

8. Design Goals - Cost and Participation • Low-cost Sensors • Typical per spot parking management systems ranges from $250 to $800 per spot • Current systems are difficult to place in areas without marked parking spots •  Low Vehicle Participation • Be able to function without a lot of cars fitted • Keep costs down

9. Moxbotix WR1 rangefinder Waterproof Emits every 50ms 12-255 inches  PS3 Eye webcam 20 fps Used for ground truth Not in production Garmin GPS Readings come at 5Hz Errors can be less than 3m On-board PC 1GHz CPU 512 MB Ram 20 GB HD PCI WiFi card 6 USB ports Prototype Development - Hardware

10. Prototype Development - Deployment • System was placed on 3 vehicles • 3 specific areas were marked off to be analyzed • Data was collected over a 2 month period • Drivers were oblivious to the data collection • All range sensor data is tagged with:Kernel-time, range, latitude, longitude, speed

11. Prototype Development - Verification • PS3 Eye • Mounted just above the rangefinder • Took pictures at 20fps that were time tagged • Each picture was manually checked to see if there was a car parked • This was used to verify the data collected from the system

12. Parking Space Detection - Challenges • Ultrasonic sensor does not have a perfectly narrow-width • GPS Errors • False alarms • Other impeding objects: Trees, people, recycling bins • Missed detections • Parked vehicles classified to be something other than a parked car

13. Parking Space Detection - Dips • A "dip" is a change in the rangefinder readings which usually occurs when there is an object in view Two Cars Parked Together Far Close

14. Parking Space Detection - Algorithms • Slotted Model • Determines which dips are classified as cars • Subtracts the total number of cars found with the total number of spaces available in the area • Unslotted Model • Determines which dips are classified as cars • Measures the distance between dips to see if it is large enough to fit a car • Training • 20% of the data is used for training • 80% of the data is used for evaluating performance

15. Slotted Model Accuracy Parking Space Detection - Slotted

16. Unslotted Model Accuracy Parking Space Detection - Unslotted

17. Occupancy Map - GPS Error • Selected 8 objects and determined their absolute GPS position using Google Maps • Corresponded the GPS reading gathered from the trials to the objects • Used the reading from one object to correct the others

18. Occupancy Map - Environmental Fingerprinting • Fixed objects in the environment used to increase positional accuracy • Recognition Walkthrough • GPS coordinates indicate system is near known object • Parses rangefinder readings • Determines what is not a parked car • Tries match the pattern with the known object • If object found, correct position if within 100m

19. Mobility Study - Taxicab Routes • Public dataset of 536 taxicabs GPS position every 60 seconds • Routes were approximated by linear interpolation • Found that taxicabs spend the most time in downtown areas where parking is scarce • Determined the mean time between cabs visiting a particular street.

20. Mobility Study - Taxicab Mean Time Greater San Francisco Downtown San Francisco

21. Mobility Study - Cost Analysis • Current Cost: • Parknet: (~$400 per sensing vehicle) x (number of vehicles needed to get desired rate of detection) • Fixed Sensor: ($250-800 per space) x (number of spaces) • Uses opportunistic WiFi connections to transfer data • Easily managed due to the much smaller number of fixed sensors • Example • 6000 parking spots • Parknet: 300 cabs, 80% coverage every 25 minutes, $0.12 million • Fixed Sensor: $1.5 million

22. Improvements • Multilane Roads • Moving cars could be determined by long dips • Rangefinder would need to be longer • Speed Limitations • Sensors currently work best at speeds below 40mph • Obtaining Parking Spot Maps • Difficult for large areas • Algorithms could determine location surroundings after data collection has been started • Using vehicles current proximity sensors

23. Conclusion • Data collected • 500 miles over 2 months • Accuracy • 95% accurate parking space counts  • 90% accurate parking occupancy maps • Frequency and Coverage • 536 vehicles equipped • Covers 85% every 25 minutes of a downtown area • Covers 80% every 10 minutes of a downtown area • Cost Benefits • Estimated factor of 10-15 times cheaper than current systems • Questions?

24. Links Fixed Parking System (SFpark) http://sfpark.org http://vimeo.com/13867453