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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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Presentation on theme: "Real time street parking availability estimation Dr. Xu, Prof. Wolfson, Prof. Yang, Stenneth, Prof. Yu University of Illinois, Chicago."— Presentation transcript:

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

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

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

4 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

5 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

6 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

7 PSD, HAP, PAE 7

8 Parking status detection (PSD) Determine when/where a driver park/deparks Image sources: 8

9 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

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

11 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

12 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

13 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

14 HAP algorithm PP 1 PP 2 PP m 14

15 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

16 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.

17 Parking Availability Estimation (PAE) 17

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

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

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

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

22 PAE results 22

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

24 Related work ParkNet SFPark.org project Googles OpenSpot 27 Image sources: $300 per sensor + $12 per month service. Project cost $23 million Cumbersome $400 per system for each vehicle

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

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


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