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Real time street parking availability estimation

Real time street parking availability estimation. Dr. Xu, Prof. Wolfson, Prof. Yang, Stenneth, Prof. Yu University of Illinois, Chicago . In one business district, vehicles searching for parking produces 730 tons of CO 2 , 47000 gallons on gasoline, and 38 trips around the world. . Problem.

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Real time street parking availability estimation

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  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 CO2, 47000 gallons on gasoline, and 38 trips around the world.

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

  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

  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

  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)

  7. PSD, HAP, PAE

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

  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

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

  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

  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 fp: false positive probability b: penetration ratio (uniform distribution) fn: false negative 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 PP1 PP2 PPm

  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

  16. More specifically: Cumulative distribution function of normal distr. Number of samples , or permitted periods • 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 True average Estimation variance

  17. Parking Availability Estimation (PAE) • Solely real time observations • scaled PhonePark (SPP) – capped • Solely historical parking data (HAP) • historical statistics (HS)

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

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

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

  21. HAP Results • RMSE between q • b = 1% , see for b = 50% in paper Chestnut St. block 4 spaces available Polk St. block 12 spaces available

  22. PAE results • RMSE between x • b =1 % , see for b = 50% in paper

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

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

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

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

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