Mining Movement Patterns For Predicting Next Locations

Meng Chen

Location data check-in data the vehicle passage records

Trajectory a sequence of locations ordered by time-stamps e.g.,

Introduction

1l5l

4l3l

2l

321 lll

predict the drivers' next locations

recommend more reasonable routes

Route recommendati

on

predict next location in advance

push information

Targeted advertising

Motivation

Training data the historical trajectories

Markov model Global Markov Model Personal Markov Model NLPMM: a combined model

Time factor cluster the time periods build a separate model for each cluster

Overview

They all choose the route, so do I.

Global Markov Model

Variable-order GMMOrder-N GMM

Order-0

Order-N

Training data

1 3 42

3 412

3 42

3 41

1 32

training training

Global Markov Model

Order-1 GMM

12

3

0.5

0.5

2

3

1

3

4

0.25

0.75

1.0

Training data

1 3 42

3 412

3 42

3 41

1 32

Global Markov Model

I am familiar with the route, repeating…

Personal Markov Model

Variable-order PMMOrder-N PMM

Training data

1 3 42

3 412training

Order-0

Order-N

training

Personal Markov Model

1 32 1.0

32 4 1.0

3 4 1.01

3 1.012

Variable-order NLPMM

Test data327 4

1

2

3

2

3

1

3

4

0.5

0.5

0.25

0.75

1.0

Order-1Order-2 Order-N

.

.

.

Order-0

2

1

3

4

0.24

0.24

0.28

0.24

Predicting next location

Time Factor

Time Factor

Training data

1 3 42

3 42

3 41

3 412

1 32

0: 00

24: 00

Time

Train m independent models, each for a different time bin, using the trajectories falling in each bin.

Bin 1

Bin 2

Bin 3

Bin m

…

Time Binning

Cluster 1 Cluster 2 Cluster 3

Bin 1 Bin 2 Bin 3 Bin 6Bin 4 Bin 5

Distribution Clustering

Training: train a separate NLPMM for each cluster with the

trajectories in it.

Testing:

determine the cluster that the trajectory belongs to. predict next location with the corresponding model.

Distribution Clustering

A Object-clustered Markov model

B Trajectory-clustered Markov model

C Object Trajectory Markov Model

computing the spatial locality matrixclustering objectsMarkov modelingnext location prediction

trajectory clusteringMarkov modelingnext location prediction

logistic regression

Overview

Computing the Spatial Locality Matrix

user A user B user C

global location probability

Computing the Spatial Locality Matrix

Clustering Objects

Cluster 1 Cluster 2 Cluster 3

1 2 3 4 5 6

Kullback-Leibler divergence Cosine similarity

Variable-order MMOrder-m MM

Order-0

Order-m

training training

Trajectories in one cluster

1 3 42

3 412

3 42

3 41

1 32

Markov Modeling

Introduction Related Work Object-MM Tra-MM Experiments Conclusion

Order-1 MM

12

3

0.5

0.5

2

3

1

3

4

0.25

0.75

1.0

Trajectories in one cluster

1 3 42

3 412

3 42

3 41

1 32

Markov Modeling

Introduction Related Work Object-MM Tra-MM Experiments Conclusion

1 32 1.0

32 4 1.0

3 4 1.01

3 1.012

Variable-order MM

Test data 327 4

1

2

3

2

3

1

3

4

0.5

0.5

0.25

0.75

1.0

Order-1Order-2 Order-m

.

.

.

Order-0

2

1

3

4

0.24

0.24

0.28

0.24

Next Location Prediction

cluster 1

Introduction Related Work Object-MM Tra-MM Experiments Conclusion

Trajectory Clustering

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Methods based on collective patterns build a Markov model using the trajectories of all objects make predictions at too coarse a granularity not considering the inherent similarity between trajectories

Distance measures Euclidean distance Dynamic Time Warping

Trajectory ClusteringTraditional clustering algorithms

developed for static and small datasets not suitable for large-scale trajectories and real-time stream data

Markov ModelingMarkov Modeling

train a variable-order Markov model for each clusterNext Location Prediction

find its closest cluster for a trajectory choose the corresponding model of the cluster predict next location

数据挖掘之我见• 道 or 术

– 一招鲜吃遍天• 第一层

– 模型了解，工具会用• 第二层

– 调参数，应用特定数据• 第三层

– 新模型

• 简约而不简单• 简约而不简单• 简约而不简单

数据挖掘之我见

推荐内容• 数学之美• 机器学习实战• python 入门• 分布式数据挖掘

WE ARE JUST ON THE WAYTHANK YOU.

Meng Chenchenmeng114@hotmail.com