Computerized Trip Classification
of GPS Data:
A Proposed Framework
Terry Griffin - Yan Huang – Ranette Halverson
Midwestern State University, Wichita Falls
University of North Texas, Denton
Midwestern State University, Wichita Falls TX
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Introduction and
Motivation
• Why Derive Trip Purpose??
• Many Transportation Departments are doing
studies that require Travel Diaries (TD) or
Origin Destination (OD) matrices.
• TD’s and OD matrices require user interaction
(lots of it).
• In this paper we propose a framework to
possibly eliminate the human factor from the
creation of TD’s and OD matrices.
• This is done by passively collecting GPS data.
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Overview of the Presentation
•Some Background
•Trip Purpose Classification
•Data Collection
•Data Preparation
•Data Aggregation
•Clustering
•Generating Random Data
•Results
•Conclusions
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Background
To create a trip classification model,
we first need to know:
•What is a trip?
•GPS streams
•How do we classify that trip?
•Clustering
•Decision Trees
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Background
GPS Streams
What is a GPS stream?
•The logged GPS data can be described as a collection of points
(P1, P2...Pn)
•Each point is defined by a Latitude (Lat) and Longitude (Lon)
pair, accompanied by the Time of Day (ToD).
•The entire set becomes:
(P[Lat,Lon,ToD]1,P[Lat,Lon,ToD]2,...,P[Lat,Lon,ToD]n)
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Background
GPS Streams
What is a GPS stream?
Each stream is typically recorded:
• continuously with a user defined
interval
• or by movement only
Each stream creates Points Of Interest (POI)
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Background
Clustering
Dbscan – Density Based Clustering
•Eps
•MinPts
•Density Reachability
•Density Connectivity
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Background
Clustering
Dbscan – Density Based Clustering
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Background
Decision Trees
• What is a decision tree?
1. Used as a tool for classification and prediction
2. Tree like structure that represents rules
3. leaf node - indicates the value of the target
attribute (class) of examples, or
4. decision node - specifies some test to be
carried out on a single attribute-value, with
one branch and sub-tree for each possible
outcome of the test.
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Background
Decision Trees
Example Decision Tree
Given
ATTRIBUTE
|
POSSIBLE VALUES
============+=======================
outlook
| sunny, overcast, rain
temperature | continuous
humidity
| continuous
windy
| true, false
and
OUTLOOK | TEMPERATURE | HUMIDITY | WINDY | PLAY
=====================================================
sunny
|
85
|
85
| false | Don't Play
sunny
|
80
|
90
| true | Don't Play
overcast|
83
|
78
| false | Play
rain
|
70
|
96
| false | Play
rain
|
68
|
80
| false | Play
rain
|
65
|
70
| true | Don't Play
overcast|
64
|
65
| true | Play
….
You get
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Background
Decision Trees
Example Decision Tree (Golf)
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Background
Decision Trees
1.Entropy – measures the purity of an
arbitrary collection of examples (the
homogeneity )
2.Information gain - measures how well a
given attribute separates the training
examples according to their target
classification
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Trip Purpose Classification
•To find and classify trip purposes for a given
GPS stream, we follow a series of steps
•Data Collection
•Data Preparation
•Data Aggregation
•Actual Classification
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Trip Purpose Detection
Data Collection
•Tools
•Used a Palm m515 (hardware)
•Magellan GPS companion (hardware)
•Cetus GPS 1.1 (software)
•Method
•Continuous
•Movement Only (caused problems)
•Collected
•6 weeks of continuous data for 1 individual
•Randomly generated a data set
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Trip Purpose Detection
Data Preparation
• Data cleansing
• Compute trip stop lengths from given raw
GPS data.
• Continuous
• Movement only
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Trip Purpose Detection
Data Aggregation
•Single points are not meaningful
•Only after many points are “clustered” together can we
really gain information.
•Each balloon is a “POI” (cluster)
•Each balloon gives us:
•Average time of day
•Average length of stay
•Longest length of stay
•Earliest arrival time
•Etc…
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Trip Purpose Detection
Data Aggregation
•It’s from these aggregate values that we
can build / train our decision tree.
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Classifying Points of Interest
Trip Purpose Detection
Identified Clusters:
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Trip Purpose Detection
Classifying Points of Interest
•Example Tree
created by c4.5:
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Trip Purpose Detection
Classifying Points of Interest
Identified Clusters:
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Random Data
x - current time of day
µ - specified time for
location in which the
probability of going there
should be high
σ - time window (standard
deviation) around µ
d – control parameter
d = (d1,d2)|
d {(0,1),(-1,0),(-1,1)}
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Results
Random Data
•50 generations
•For each generation we modified Eps and MinPts
•15x15 feet - 200x200 feet (5 distinct sizes)
•MinPts of 2 – 10 were used
•As each cluster was found, it was classified using a
classification tree based on the data generated for that
test.
•Each cluster was assigned a level of correctness
(all points in the cluster correctly identified = 1)
• We used 20 % of the generated data to train the tree.
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Results
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Results
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Future Work
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Future Plans
• Create a GPS database
– $5000 grant for GPS devices (fall 2006)
– Additional University funds
• Fill a needed gap in GPS research
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Conclusions
•This classification tool has potential, but
needs real validation
•Be nice to obtain a large data set
•Future…
•possibly predict the next trip stop
based on Markhov chains
•Questions??
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