how long to wait?: predicting bus arrival time with mobile phone based participatory sensing pengfei...
TRANSCRIPT
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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
Who will pay
for this?
$$$$$$$$$$$$
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Objective
• Crowd-participated approach– Sharing users– Querying users
– Backend server
• Energy friendly– Microphone, accelerometer
Mobile Phone
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Main idea
• Map the bus routes to a space featured by sequences of nearby cellular towers
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Challenges
• Bus Detection• Bus Classification• Information Assembling
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System Design
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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• Audio detection : short beep audio response
Peak at 1 kHz and 3kHz
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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 S∈ j , ui and Sj are matching with each other, and mismatching otherwise
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Bus Classification
• w: rank of signal strenthpenalty 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 vs. MRT Train
False detection: Driving along straight routes late during night time
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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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Limitation and On-going Work
• Alternative reference points• Number of passengers• First few bus stops• Overlapped routes
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Conclusion
• Present a crowd-participated bus 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.