Computerized Trip Classification of GPS Data Framework

1. Computerized Trip Classificationof GPS Data:A Proposed FrameworkTerry Griffin - Yan Huang – Ranette HalversonMidwestern State University, Wichita FallsUniversity of North Texas, Denton Midwestern State University, Wichita Falls TX

2. 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. Midwestern State University, Wichita Falls TX

3. Overview of the Presentation • Some Background • Trip PurposeClassification • Data Collection • Data Preparation • Data Aggregation • Clustering • Generating Random Data • Results • Conclusions Midwestern State University, Wichita Falls TX

4. 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 Midwestern State University, Wichita Falls TX

5. Background GPS Streams • 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) Midwestern State University, Wichita Falls TX

6. 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) Midwestern State University, Wichita Falls TX

7. Background Clustering • Dbscan – Density Based Clustering • Eps • MinPts • Density Reachability • Density Connectivity Midwestern State University, Wichita Falls TX

8. Background Clustering • Dbscan – Density Based Clustering Midwestern State University, Wichita Falls TX

9. Background Decision Trees • What is a decision tree? • Used as a tool for classification and prediction • Tree like structure that represents rules • leaf node - indicates the value of the target attribute (class) of examples, or • 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. Midwestern State University, Wichita Falls TX

10. Background ATTRIBUTE | POSSIBLE VALUES ============+======================= outlook | sunny, overcast, rain temperature | continuous humidity | continuous windy | true, false 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 …. Example Decision Tree Decision Trees Given and You get Midwestern State University, Wichita Falls TX

11. Background Example Decision Tree (Golf) Decision Trees Midwestern State University, Wichita Falls TX

12. Background Decision Trees • Entropy – measures the purity of an arbitrary collection of examples (the homogeneity ) • Information gain - measures how well a given attribute separates the training examples according to their target classification Midwestern State University, Wichita Falls TX

13. 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 Midwestern State University, Wichita Falls TX

14. 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 Midwestern State University, Wichita Falls TX

15. Trip Purpose Detection Data Preparation • Data cleansing • Compute trip stop lengths from given raw GPS data. • Continuous • Movement only Midwestern State University, Wichita Falls TX

16. 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… Midwestern State University, Wichita Falls TX

17. Trip Purpose Detection Data Aggregation • It’s from these aggregate values that we can build / train our decision tree. Midwestern State University, Wichita Falls TX

18. Trip Purpose Detection Classifying Points of Interest Identified Clusters: Midwestern State University, Wichita Falls TX

19. Trip Purpose Detection Classifying Points of Interest • Example Tree • created by c4.5: Midwestern State University, Wichita Falls TX

20. Trip Purpose Detection Classifying Points of Interest Identified Clusters: Midwestern State University, Wichita Falls TX

21. Random Data x - current time of dayµ - 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)} Midwestern State University, Wichita Falls TX

22. 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. Midwestern State University, Wichita Falls TX

23. Results Midwestern State University, Wichita Falls TX

24. Results Midwestern State University, Wichita Falls TX

25. Future Work Midwestern State University, Wichita Falls TX

26. Future Plans • Create a GPS database • $5000 grant for GPS devices (fall 2006) • Additional University funds • Fill a needed gap in GPS research Midwestern State University, Wichita Falls TX

27. 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?? Midwestern State University, Wichita Falls TX