How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone

Report
How Long to Wait?:
Predicting Bus Arrival Time with Mobile
Phone based Participatory Sensing
Pengfei Zhou, Yuanqing Zheng, Mo Li
-twohsien 2012.9.3
Outline
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Introduction
System design
Evaluation
Limitations
Conclusion
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
Objective
• Crowd-participated approach
– Sharing users
– Querying users
Mobile Phone
– Backend server
• Energy friendly
– Microphone, accelerometer
Main idea
• Map the bus routes to a space featured by
sequences of nearby cellular towers
Challenges
• Bus Detection
• Bus Classification
• Information Assembling
System Design
Pre-processing Celltower Data
Top-3 strongest cell towers
300 meters apart
Example
Bus Detection
• Audio detection : short beep audio response
Peak at
1 kHz and 3kHz
Bus Detection
• Sliding window, size: 32 samples
• Empirical threshold: three standard deviation
Bus Detection
• Accelerometer detection
– Bus v.s. Rapid train
Bus Detection
• Threshold
– Small: trains will be misdetected as buses
– Big: miss detection of actual buses
Bus Classification
• Cell tower sequence matching
– Smith-Waterman algorithm
• If ui = Cw ∈ Sj ,
ui and Sj are matching with each other, and
mismatching otherwise
Bus Classification
•   = 0.5−1
w: rank of signal strenth
penalty cost for mismatches : -0.5
Overlapped route
• Survey 50 bus route
Range of cell tower:
300-900 meters
threshold of celltower
sequence length : 7
Cell tower Sequence Concatenation
Arrival Time Prediction
EVALUATION
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
Bus Detection Performance
Bus vs. MRT Train
False detection: Driving along straight routes late during night time
Bus Classification Performance
Arrival Time Prediction
Arrival Time Prediction
System Overhead
• Battery lifetime
Limitation and On-going Work
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Alternative reference points
Number of passengers
First few bus stops
Overlapped routes
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.

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