ParkNet: Drive-by Sensing of Road-Side Parking Statistics Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin, South Korea Copyright © solarlits.com

Introduction Mobile system that collect parking space occupancy information. With GPS receiver and a ultrasonic rangefinder Mapping Ultrasonic sensor fitted on the side of a car detects parked cars and vacant spaces.

ROAD-SIDE PARKING CHALLENGE Spot reservation system requires exact knowledge requires all other vehicles to be notified may lead to inefficiencies Web based system or navigation system Improve traveler decisions Suggesting parking spaces adjust their prices dynamically Improve efficiency

Parking in slotted and unslotted areas Slotted: Separated by lines marked on the road Unslotted: without any marked parking spots Parking enforcement vehicle (street parking areas) a sensor and connectivity to a database

Parking in slotted and unslotted areas cont’d..

Parking in slotted and unslotted areas Unslotted: to define a space count Distances of stretches Number of spots Is fixed for one parking slot (approx. 6 m) Slotted Easy to create occupancy map Parking enforcement vehicle (street parking areas)

Design goal and requirements SFPark project, San Francisco To cover 6000 parking spaces Detects the presence of a vehicle using a magnetometer among other sensors Requires repeaters and forwarding nodes on lamp posts and traffic lights. $250-$800 per spot, 23 million dollars

Design goal and requirements cont’d.. Availability of road-side parking spaces on at least an hourly basis A map of paid-parking spaces Use already installed sensors in the vehicle ParkNet Mobile sensing approach with ultrasonic sensors and GPS

Ultrasonic sensor Low cost than laser sensor, radar, and camera Reusing already present ultrasonic sensors in future vehicles Range and sampling rate should be large Time 50 ms, frequency 42 Khz, inches every cycle 5Hz GPS receiver

Prototype deployment Deployed on three vehicles, 2 months survey 57 slotted parking spots and two unslotted areas To obtain ground truth, we integrated a Sony Eye webcam (20fps) GPS trip-boxes for limiting data collection

Detection on parking spaces Dips in the sensor reading Sensor reading, ground truth, speed, and output of the detection algorithm.

Challanges Reducing false alarms (objects other than cars) 20% data to train the model 80% to evaluate its performance

Detection algorithm Slotted model Ultrasonic sensor Threshold was 89.7 inches for depth and 2.52 m for width Overall error rate = 12.4%. GPS Compute the distance between the starting and ending sample Twice the threshold assume two cars Unslotted model Variable space Estimated against standard length (6 m) Parked cars (blue squares), objects other than cars (red stars)

Detection algorithm cont’d.. Slotted model Total parking slots N, Vacant slots n, Vacant slots by sensing vehicle n', Performance of the detection n'/n Missed detection rate p m, false positive rate p f Unslotted model Estimated space d', actual space between the cars is d, measure of accuracy d'/d

Detection algorithm cont’d.. System is 95% accurate in terms of obtaining parking counts Slotted model Unslotted model

Occupancy map creation Location coordinates provided by GPS are accurated to 3m (standard deviation) 8 objects, 29 different runs Correlation in the error of the GPS location

Occupancy map creation cont’d.. Environmental fingerprinting approach Utilize ultrasonic sensor readings to correct GPS trace Comparing the reported location of the pattern (dips) produced by a series of fixed objects Using the first object in a series of 8 objects to correct the error of the remaining 7 objects.

Occupancy map creation cont’d.. Slot matching 57 slots on a street were determined using a satellite picture from Google Earth A comparison of the error rates in assigning parked cars to the correct slots with and without error correction using fingerprinting.

Mobility study Study of the mobility patterns of 536 taxicabs in San Francisco Location trace of a single taxicab in San Francisco area over a span of 30 days. busiest areas with most street-parking Two areas of San Francisco (i) the greater San Francisco area, and (ii) the busiest portion

Mobility study Study of the mobility patterns of 536 taxicabs in San Francisco one can extrapolate to estimate the number of taxicabs to be fitted with sensor To compute the CDF, the city is divided into cell of size 175 m × 190 m cumulative distribution function

Cost analysis ParkNet $400 for one sensing vehicle $170 for PC platform: $170, $20 for sensor $100 GPS unit $100 for wiring and connecting components including labor SF park project cost: $120,000 (6000 fixed sensors)

Challenges faced DC to DC power supply Multilane detection through ultrasonic sensor and GPS Speed limitation of ultrasonic sensor 20 readings per second Speed limit in street parking areas is 35– 40 miles per hour Obtaining parking spot maps From previous data, decide unmarking and parking spots

Conclusions Based on over 500 miles of data collected over 2 Months Ultrasound sensors combined with GPS can achieve 95% accurate parking space counts It can generate over 90% accurate parking occupancy maps Cost is reduced using mobile sensors (by a factor of 10-15) Using trace-driving simulation density of taxicabs can be estimated in an urban area Taxicab coverage analysis will also benefit other mobile sensing applications