COOPERATIVE TRANSIT TRACKING USING SMART-

PHONES

ESOE R98525087 李孟翰ESOE R99525045 郭羿呈

Arvind Thiagarajan MIT CSAILJames Biagioni Tomas Gerlich Jakob Eriksson University of Illinois at

ChicagoSenSys’10, November 3–5, 2010, Zurich,

Switzerland.Copyright 2010 ACM 978-1-4503-0344-6/10/11 ...

$10.00

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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車快到了

~^^

&$*@#$*%)

Polly’s story

Xx

通訊

月底省點錢

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Why ?

Xx

通訊

發送訊息

等待公車更新資料

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Why ?

Figure 2. GPS trace and an actual trajectory of a bus ride downtown Chicago

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Why ?

Figure 3. CDF of GPS localization errors for downtown and suburban environments

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Why ?

Figure 1. Measured difference between scheduled and actual arrival times of buses in Chicago

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Motivation

Provide more precise way for tracking services.

Other issues need to be considered.

energy efficiency

Activity Classification

Tracking Underground

Arvind Thiagarajan MIT CSAILJames Biagioni Tomas Gerlich Jakob Eriksson University of Illinois at Chicago

Cooperative Transit Tracking using Smart-phones

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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System Overview

Figure 4. Cooperative transit tracking system

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Activity Classification by Accelerometer

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Filtering out spurious stops

Performance Evaluation Conclusion

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Spatio-temporal Trajectory Matching

∃𝑘 ,𝑑𝑖𝑠𝑡 (𝐿𝑘 ,𝑆 (𝐶 ,𝑘 ) )>𝜆Sequential GPS Location

𝐸𝑆 (𝐶 ,𝑖 )=∑𝑘=1

𝑖

𝑑𝑖𝑠𝑡 (𝐿𝐾 ,𝑆(𝐶 ,𝑘))

√ 𝐸𝑆(𝐶 ,𝑖)𝑛

>𝜏Check slide

windows and return only

one bus number

𝐶𝐵𝑒𝑠𝑡

𝑟=𝐼𝑎𝑐𝑡𝑢𝑎𝑙𝐼 h𝑠𝑐 𝑒𝑑𝑢𝑙𝑒𝑑

Car Bus Unknown

INPUT

OUTPUT

Outlier Removal

Least Squares Minimization

Post Processing

Schedule Deviation

Overlapping Routes

YES

YES

YES

NO

NO

NO

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Spatio-temporal Trajectory Matching

Other Exception Check Stopping Check

Need more than 3 stops, and each stop remains exceeds 15 sec.

Inter-Stop Distance CDF

Overlapping Routes Check RMES > τ and Slide Windows

more than 1 possible buses confidence cutoff(CC)

Low: quick real-time tracking High: more precise route

map

Figure 8. Inter-Stop Distance CDF for buses

and cars.

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Tracking Underground Transit

Schedule-based hidden Markov mode(HMM) Set of emission score (ES) INPUT

Check States

Check the condition (con) of

“stopped in tunnel”

State is moving ?

Is con satisfied?

Moving in tunnel Stopped in tunnel Stopped at station

OUTPUT

States Transitions

Emission detector

YES

NO

YES

NO

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Tracking Underground Transit

States Transitions

Emission detector

Figure 10. HMM for accelerometer and schedule-based subway tracking

{ 𝐸 ( 𝑡 h𝑠𝑐 𝑒𝑑− 𝑡 ,𝜆 h𝑎 𝑒𝑎𝑑 ) ,𝑡<𝑡 h𝑠𝑐 𝑒𝑑

𝜅 𝐸 (𝑡−𝑡 h𝑠𝑐 𝑒𝑑 ,𝜆 h𝑏𝑒 𝑖𝑛𝑑 ) ,𝑡 ≥𝑡 h𝑠𝑐 𝑒𝑑

S topped∈tunnel⟺ { 𝑡>𝑡 h𝑠𝑐 𝑒𝑑⇒ 𝐿𝑜𝑤𝑛𝑜𝑛− 𝑧𝑒𝑟𝑜𝑡<𝑡 h𝑠𝑐 𝑒𝑑⇒𝑃 𝑠𝑡𝑜𝑝 (1−𝐸 (𝑡 ,𝜆 h𝑎 𝑒𝑎𝑑 )) 𝑖𝑠 h h𝑖𝑔

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Tracking Underground Transit

Other issues need to be considered : Filtering out spurious stops in tunnel.

Figure 9. Detecting bus mobility by accelerometer.

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Activity Classifier Accuracy

Table 3. Walking detection accuracy on a variety of labeled test traces.

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Trajectory Matching Accuracy

Figure 12. Prec./recall vs Confidence Cutoff, CTA Data.

Figure 13. CDF of Decision Time, CTA Data.

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Table 4. Transit matching on car traces along or near known bus routes.

Trajectory Matching Accuracy

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Tracking Accuracy in Subway

Figure 15. Comparison of estimated, scheduled and actual arrival time at

each station.

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Utility of Cooperative Transit Tracking

Figure 17. Wait time vs. Penetration level using Cooperative Transit

Tracking Only.

Figure 16. Wait time vs. Penetration level using Cooperative Transit

Tracking with fallback on schedule.

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Utility of Cooperative Transit Tracking

Figure 18. Requests served vs. Penetration level.

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Outline

Introduction Motivation Proposed Method

System Overview Activity Classification by Accelerometer Spatio-temporal Trajectory Matching Tracking Underground Transit

Performance Evaluation Conclusion

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Conclusion

Cooperative transit tracking that combines power-efficient activity detection using

accelerometer data memory-efficient spatio-temporal bus

trajectory matching using least squares minimization

accelerometer in conjunction with a Hidden Markov model to track underground trains when other localization schemes do not work.

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Q&A