real time street parking availability estimation dr. xu, prof. wolfson, prof. yang, stenneth, prof....

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