Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A personal route prediction system base on trajectory data mining Presenter : Keng-Yu Lin Author : Ling Chen, Mingqi Lv, Qian Ye, Gencai Chen, John Woodward IS.2011

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation  Route prediction allows certain services to improve the quality.  Most existing work has focused on predicting the routes of vehicles. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  To develop a new method for extracting route patterns from personal trajectory data, which can tolerate different kind of disturbance in trajectory data to predict personal route.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Data preparation  Data collection 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Data preparation  Data filtering Duplication filter Speed filter Acceleration filter Total distance filter Angle filter  The criterion for splitting GPS data into trip is the time gap. 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Data filter algorithm Input : GPS data of user Output :STS( Spatial-Temporal Sequences ) 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  CRPM Algorithm 8 Region of Interest

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  CRPM algorithm  extend_projection 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Probabilistic model 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Example There are three candidate route Pattern: P1=, P2=, P3= 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Example of prediction algorithm The online observation show a user has just passed three ROIs: r4,r3,r5 r8,r9 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Algorithm of route prediction 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  This paper collects feedback from participants allowed to three questions.  Is this typical route that you might take?  If you do take the route, how often do follow it?  What proportion of the complete journey does the route pattern represent? 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 16

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 17

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  The prediction algorithm in this paper outperform the tradition method in route prediction.  The client-server architecture can reduce computational load on the client device. 18

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments  Advantage  The personal prediction system has good correct rate of prediction.  Applications  Prediction route  Advertisement 19