Transportation mode detection using mobile phones and GIS information Leon Stenneth, Ouri Wolfson,...
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University of Illinois, Chicago 1
Transportation mode detection using mobile phones and GIS
information
Leon Stenneth, Ouri Wolfson, Philip Yu, Bo Xu
University of Illinois, Chicago 2
Problem
• Detecting a mobile user’s current mode of transportation based on GPS and GIS.
• Possible transportation modes considered are:
University of Illinois, Chicago 3
Technique
• A supervised machine learning model
• New classification features derived by combining GPS with GIS
• Trained multiple models with these extracted features and labeled data.
University of Illinois, Chicago 4
Motivation
• Value added services to context detection systems
• More customized advertisements can be sent
• Providing more accurate travel demand surveys instead of people manually recording trips and transfers
• Determining a traveler’s carbon footprint.
University of Illinois, Chicago 5
Approach
• In addition to traditional features on speed, acceleration, and heading change. We build classification features using GPS and GIS data
Mobile Phone’s GPS sensor report
Bus stop spatial data
Rail line spatial data
Real time bus locations
Training example
University of Illinois, Chicago 6
Features
• Traditional – Speed, acceleration, and heading change
• Combining GPS and GIS– Rail line closeness– Average bus closeness– Candidate bus closeness– Bus stop closeness rate
University of Illinois, Chicago 7
Rail line closeness
• ARLC - average rail line closeness• Let {p1, p2, p3, p4…pn} be a finite the set of GPS
reports submitted within a time window. ARLC = ∑i=1 to n di
rail / n
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Average bus closeness (ABC)
• Let {p1, p2, p3, p4…pn} be a finite the set of GPS reports submitted within a time window.
ABC = (∑i=1 to n dibus) / n
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Candidate Bus closeness (CBC)
• dj.tbus 1≤j≤m - Euclidian distance to each bus busj
• Dj - total Euclidian distance to bus j over all reports submitted in the time window
Dj = ∑t=1 to n dj.tbus 1≤j≤m
• Given Dj for all the m buses, we compute CBC as follows.
CBC = min (Dj) 1≤j≤m
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Bus stop closeness rate (BSCR)
• | PS | is the number of GPS reports who's Euclidian distance to the closest bus stop is less than the threshold
BSCR = | PS | / window size
0 50 100 150 200 250 300 350 400 450 5000
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GPS sensor report number
Eucl
ilidi
an d
ista
nce
from
clo
sest
bus
stop
(m
)
University of Illinois, Chicago 11
Machine learning models
• We compared five different models then choose the most effective– Random Forest (RF)– Decision trees (DT)– Neural networks (MLP)– Naïve Bayes (NB)– Bayesian Network (BN)
• WEKA machine learning toolkit
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Results
• Random Forest was the most effective model• Precision and recall accuracy of Random
forest shown below
train bus
stationary walk car bike
avera
ge0
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Traditional features onlyTraditional and GIS fea-tures
mode
prec
ision
train bus
stationary walk car bike
avera
ge0
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Traditional features onlyTraditional and GIS fea-tures
mode
reca
ll
University of Illinois, Chicago 13
Feature Ranking
• Below we rank the features to determine the most effective.
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Results
• Using the top ranked features only• Precision and recall accuracy is shown below
train bus
stationary walk car bike
avera
ge0
10
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100
Top ranked features only
mode
prec
ision
train bus
stationary walk car bike
avera
ge0
10
20
30
40
50
60
70
80
90
100
Top ranked features only
mode
reca
ll
University of Illinois, Chicago 15
Deployed System
• We can provide further information (i.e. route, bus id) on the particular bus one is riding.
University of Illinois, Chicago 16
Related work with GPS
• Liao et. al (2004) – consider the user’s history such as where one parked.
• Zheng et. al (2008) – Robust set of features and a change point segmentation method.
• Reddy et. al (2010) – Combined accelerometer and GPS to achieve high accuracy.
University of Illinois, Chicago 17
Conclusion
• Using GIS data improves transportation mode detection accuracy.
• This improvement is more noticeable for motorized transportation modes.
• Only a subset of our initial set of features are needed.
• Random forest is the most effective model• We can provide further information about the
bus that a user is riding
University of Illinois, Chicago 18