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Real time street parking availability estimation Dr. Xu, Prof. Wolfson, Prof. Yang, Stenneth, Prof. Yu University of Illinois, Chicago

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In one business district, vehicles searching for parking produces 730 tons of CO 2, 47000 gallons on gasoline, and 38 trips around the world. 2

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Problem estimating street parking availability using only mobile phones mobile phone distribution among drivers GPS errors, transportation mode detection errors, Bluetooth errors, etc. 3

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Motivations save time and gas to find parking reduce congestion and pollution mobile phone are ubiquitous affordable - SF park 8000 parking spaces cost 23M USD external sensors such as cameras not utilized 4

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Why mobile phones ? ubiquitous with several sensors (GPS, gyro, accelerometer) several people own a mobile phone other alternatives – Sensor in pavement (e.g. SF Park) $300 + $12 per month – Manual reporting (e.g. Google OpenSpot) – Ultrasonic sensors on taxi (e.g. ParkNet) $400 per sensor 5

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Contributions parking status detection (PSD) street parking estimation algorithms – historical availability profile construction (HAP) – parking availability estimation (PAE) weighted average (WA) Kalman Filter (KF) historical statistics (HS) scaled PhonePark (SPP) 6

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PSD, HAP, PAE 7

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Parking status detection (PSD) Determine when/where a driver park/deparks Image sources: http://videos.nj.com/, http://pocketnow.com/smartphone-news/http://videos.nj.com/http://pocketnow.com/smartphone-news/ http://sf.streetsblog.org 8

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Parking Status Detection (PSD) We proposed three schemes for PSD – transportation mode transition of driver – Bluetooth pairing of phone and car – Pay by phone piggyback 9

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3 Schemes for PSD Transportation mode transition (GPS/accelerometer) Bluetooth Pay-by-phone piggy back 10

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HAP construction estimates the historic mean (i.e. ) and variance (i.e. ) of parking relevant terms – prohibited period, permitted period – false positives, false negatives – b, N 11

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Why is Building Profile Non-trivial Low sample rate due to low market penetration – 1% to 5% Errors in parking status detection – False negative Missing parking activities that have occurred E.g., misclassifying parking as getting off a bus – False positive: Reporting parking activities that have not occurred E.g., misclassify getting on a bus as deparking

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Historical availability profile (HAP) Algorithm Start with a time at which the street block is fully available, e.g., end of a prohibited time interval (start permitted period) When a parking report is received, availability is reduced by: Similarly when a deparking report is received b: penetration ratio (uniform distribution) fn: false negative probability fp: false positive probability Justification: 1. Each report (statistically) corresponds to 1/b actual parking 2. 1/(1 fn) reports should have been received if there were no false negatives 3. The report is correct with 1 fp probability

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HAP algorithm PP 1 PP 2 PP m 14

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HAP uncertainty bounding Given an error tolerance, with what P the diff between q(t) and is less than x parking spaces. Lemma 1 Lemma 2 15

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More specifically: Example: – If we want error < 2 with 90% confidence, standard deviation of the estimation is 10 (i.e., the average fluctuation of estimated availability at the 8:00am is 10). – then we need 68 permitted periods. i.e. about two months of data. Estimation average Estimation variance True average Number of samples, or permitted periods Cumulative distribution function of normal distr.

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Parking Availability Estimation (PAE) 17

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Parking Availability Estimation (PAE) Combining history with real time – Weighted average 18

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Parking Availability Estimation (PAE) combining history with real time – Kalman Filter estimation (KF) 19

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Evaluation RT data from SFPark.org 04/10 to 08/11 Polk St (12 spaces )and Chestnut St (4 spaces ) 20

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HAP Results Polk St. block 12 spaces available Chestnut St. block 4 spaces available 21

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PAE results 22

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PAE results Boolean availability i.e. at least one slot available b =1 % 23

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Related work ParkNet SFPark.org project Googles OpenSpot 27 Image sources: http://www.thesavvyboomer.com/http://www.thesavvyboomer.com/ http://pocketnow.com/smartphone-news/ http://sf.streetsblog.org $300 per sensor + $12 per month service. Project cost $23 million Cumbersome $400 per system for each vehicle

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Conclusion schemes for parking status detection (PSD) – GPS, accelerometer, Bluetooth historical availability profile (HAP) algorithm real time parking availability estimation algorithms (PAE) 25

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Acknowledgements SF Park team (J. Primus etc.) Reviewers for fruitful comments NSF and NURAIL 26

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