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Computerized Trip Classificationof GPS Data:

A Proposed Framework

Terry Griffin - Yan Huang – Ranette HalversonMidwestern State University, Wichita Falls

University of North Texas, Denton

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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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•Conclusions

•Results

•Generating Random Data

•Trip Purpose Classification•Data Collection•Data Preparation•Data Aggregation•Clustering

•Some Background

Overview of the Presentation

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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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GPS StreamsBackground

What is a GPS stream?

•The logged GPS data can be described as a collection of points

•Each point is defined by a Latitude (Lat) and Longitude (Lon) pair, accompanied by the Time of Day (ToD).

•The entire set becomes:

(P1, P2...Pn)

(P[Lat,Lon,ToD]1,P[Lat,Lon,ToD]2,...,P[Lat,Lon,ToD]n)

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GPS StreamsBackground

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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ClusteringBackground

Dbscan – Density Based Clustering

•Eps

•MinPts

•Density Reachability

•Density Connectivity

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ClusteringBackground

Dbscan – Density Based Clustering

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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.

Background

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Example Decision Tree

ATTRIBUTE | POSSIBLE VALUES============+=======================outlook | sunny, overcast, raintemperature | continuoushumidity | continuouswindy | true, false

OUTLOOK | TEMPERATURE | HUMIDITY | WINDY | PLAY=====================================================sunny | 85 | 85 | false | Don't Playsunny | 80 | 90 | true | Don't Playovercast| 83 | 78 | false | Playrain | 70 | 96 | false | Playrain | 68 | 80 | false | Playrain | 65 | 70 | true | Don't Playovercast| 64 | 65 | true | Play….

Given and

You get

Decision TreesBackground

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Decision Trees

Example Decision Tree (Golf)

Background

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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

Background

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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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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

Trip Purpose Detection

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Data Preparation

• Data cleansing

• Compute trip stop lengths from given raw GPS data.

• Continuous

• Movement only

Trip Purpose Detection

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Data AggregationTrip Purpose Detection

•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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Data AggregationTrip Purpose Detection

•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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Classifying Points of Interest

Trip Purpose Detection

•Example Tree

created by c4.5:

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Classifying Points of Interest

Trip Purpose Detection

Identified Clusters:

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Random Data

d = (d1,d2)|d {(0,1),(-1,0),(-1,1)}

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

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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??