Real time street parking availability estimation

Dr. Xu, Prof. Wolfson, Prof. Yang, Stenneth, Prof. YuUniversity of Illinois, Chicago

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• In one business district, vehicles searching for parking produces 730 tons of CO2, 47000 gallons on gasoline, and 38 trips around the world.

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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.

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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

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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

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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)

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PSD, HAP, PAE

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

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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

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3 Schemes for PSDTransportation mode transition (GPS/accelerometer)

Bluetooth

Pay-by-phone piggy back

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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

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

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

)1(

1

fnb

fp

b: penetration ratio(uniform distribution)

fn: false negative probability

fp: false positive probability

Justification:1. Each report (statistically) corresponds to 1/b actual parking2. 1/(1fn) reports should have been received if there were no false negatives3. The report is correct with 1fp probability

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HAP algorithm

PP1

PP2

PPm

m

tatq

m

ii

1

)(ˆ

)(ˆm

tqtatQ

m

ii

1

2))(ˆ)(ˆ()(ˆ

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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

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.

1))(ˆ

(2}|)()(ˆ{|Prob tQ

mtqtq

Estimation average Estimation varianceTrue average

Number of samples , or permitted periods

Cumulative distribution function of normal distr.

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Parking Availability Estimation (PAE)

• Solely real time observations– scaled PhonePark (SPP) – capped

• Solely historical parking data (HAP)– historical statistics (HS)

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Parking Availability Estimation (PAE)

• Combining history with real time– Weighted average

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

RM

SE o

f es

tim

ated

mea

n

wHS

b=1%, fn=fp=0,Chestnut

b=1%, fn=fp=0.1,Chestnut

b=50%, fn=fp=0, Polk

b=50%, fn=fp=0.1, Polk

b=50%, fn=fp=0.25,Polk

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Parking Availability Estimation (PAE)

• combining history with real time– Kalman Filter estimation (KF)

.

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Evaluation

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

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HAP Results

• RMSE between q • b = 1% , see for b = 50% in paper

Polk St. block12 spaces available

Chestnut St. block4 spaces available

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PAE results

• RMSE between x • b =1 % , see for b = 50% in paper

0

0.5

1

1.5

2

2.5

fn=fp=0.05 fn=fp=0.15 fn=fp=0.25

RMSE

of es

timate

d ava

ilabil

ity

WA

KF

SPP

HS

0.44

0.45

0.46

0.47

0.48

0.49

0.5

0.51

0.52

0.53

0.54

fn=fp=0.05 fn=fp=0.15 fn=fp=0.25

RMSE

of es

timate

d ava

ilabil

ity WA

KF

SPP

HS

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PAE results

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

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

fn=fp=0.05 fn=fp=0.15 fn=fp=0.25

boole

an av

ailabil

ity ac

curacy

WA

KF

SPP

HS

0.5

0.55

0.6

0.65

0.7

0.75

0.8

fn=fp=0.05 fn=fp=0.15 fn=fp=0.25

boole

an av

ailab

ility a

ccurac

y

WA

KF

SPP

HS

Related work

• ParkNet

• SFPark.org project

• Google’s OpenSpot

27Image sources: 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)

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Acknowledgements

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