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How Long to Wait?:

Predicting Bus Arrival Time with Mobile

Phone based Participatory Sensing

Pengfei Zhou, Yuanqing Zheng, Mo Li

-twohsien 2012.9.3

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Outline

Introduction

System design

Evaluation Limitations

Conclusion

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Introduction

Why travelers do not like to travel by bus?

Excessively long waiting time

Existing methods to predict arrival time

Timetable ( operating hours, time intervals, etc.)

Special location tracking devices on buses

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Objective

Crowd-participated approach

Sharing users

Querying users

Backend server

Energy friendly

Microphone, accelerometer

Mobile Phone

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Challenges

Bus Detection

Bus Classification

Information Assembling

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Pre-processing Celltower Data

300 meters apart

Top-3 strongest cell towers

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Example

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Bus Detection

Sliding window, size: 32 samples

Empirical threshold: three standard deviation

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Bus Detection

Accelerometer detection

Bus v.s. Rapid train

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Bus Detection Threshold

Small: trains will be misdetected as buses

Big: miss detection of actual buses

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Bus Classification

Cell tower sequence matching

Smith-Waterman algorithm

If ui = Cw Sj,ui and Sj are matching with each other, and

mismatching otherwise

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Bus Classification

= 0.51

w: rank of signal strenth

penalty cost for mismatches : -0.5

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Overlapped route

Survey 50 bus route

Range of cell tower:

300-900 meters

threshold of celltower

sequence length : 7

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Cell tower Sequence Concatenation

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Arrival Time Prediction

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EVALUATION

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Experimental Methodology

Mobile phones

Samsung Galaxy S2 i9100

HTC Desire

Experiment environment

4 campus shuttle bus routes

2 SBS transit bus route 179 and 241

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Bus Detection Performance

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Bus Classification Performance

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Arrival Time Prediction

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Arrival Time Prediction

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System Overhead

Battery lifetime

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Conclusion

Present a crowd-participatedbus arrival time

prediction system using commodity mobile

phones.

Evaluate the system through a prototype

system deployed on the Android platform with

two types of mobile phones.