ParkNet Drive-by Sensing of Road-Side Parking Statistics Sutha

ParkNet Drive-by Sensing of Road-Side Parking Statistics Sutha

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

Overview 1.Introduction 2.Design Goals and Requirements 3.Prototype Development 4.Parking Space Detection 5.Occupancy Map 6.Mobility Study 7.Improvements 8.Conclusion Introduction - Problems

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

Monitoring o Uses ultrasonic sensor attached to the side of cars o 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 - 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 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 Design Goals - Parking Information Space count o Sufficient for most parking applications Occupancy Map o Useful for parking enforcemen Design Goals - Cost and

Participation Low-cost Sensors o Typical per spot parking management systems ranges from $250 to $800 per spot o Current systems are difficult to place in areas without marked parking spots Low Vehicle Participation o Be able to function without a lot of cars fitted o Keep costs down Prototype Development Hardware

Moxbotix WR1 rangefinder o Waterproof o Emits every 50ms o 12-255 inches PS3 Eye webcam o 20 fps o Used for ground truth o Not in production Garmin GPS o Readings come at

5Hz o Errors can be less than 3m On-board PC o 1GHz CPU o 512 MB Ram o 20 GB HD o PCI WiFi card o 6 USB ports

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 Prototype Development Verification PS3 Eye o Mounted just above the rangefinder o 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 Parking Space Detection Challenges Ultrasonic sensor does not have a perfectly narrowwidth GPS Errors False alarms o Other impeding objects: Trees, people, recycling bins Missed detections o Parked vehicles classified to be something other

than a parked car 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

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

o 80% of the data is used for evaluating performance Parking Space Detection - Slotted Slotted Model Accuracy Parking Space Detection Unslotted Unslotted Model Accuracy 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 Occupancy Map - Environmental Fingerprinting Fixed objects in the environment used to increase positional accuracy

Recognition Walkthrough 1.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

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. Mobility Study - Taxicab Mean Time

Greater San Francisco Downtown San Francisco Mobility Study - Cost Analysis Current Cost: o Parknet: (~$400 per sensing vehicle) x (number of vehicles needed to get desired rate of detection) o 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 o 6000 parking spots o Parknet: 300 cabs, 80% coverage every 25 minutes, $0.12 million o Fixed Sensor: $1.5 million Improvements

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

Conclusion Data collected o 500 miles over 2 months Accuracy o 95% accurate parking space counts o 90% accurate parking occupancy maps Frequency and Coverage o 536 vehicles equipped o Covers 85% every 25 minutes of a downtown area o Covers 80% every 10 minutes of a downtown area

Cost Benefits o Estimated factor of 10-15 times cheaper than current systems Questions? Links Fixed Parking System (SFpark) http://sfpark.org http://vimeo.com/13867453

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