Mining Movement Patterns For Predicting Next Locations Meng Chen.
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![Page 1: Mining Movement Patterns For Predicting Next Locations Meng Chen.](https://reader033.fdocuments.in/reader033/viewer/2022051521/5a4d1ad07f8b9ab059971242/html5/thumbnails/1.jpg)
Mining Movement Patterns For Predicting Next Locations
Meng Chen
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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
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predict the drivers' next locations
recommend more reasonable routes
Route recommendati
on
predict next location in advance
push information
Targeted advertising
Motivation
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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
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They all choose the route, so do I.
Global Markov Model
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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
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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
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I am familiar with the route, repeating…
Personal Markov Model
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Variable-order PMMOrder-N PMM
Training data
1 3 42
3 412training
Order-0
Order-N
training
Personal Markov Model
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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
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Time Factor
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Time Factor
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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
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Cluster 1 Cluster 2 Cluster 3
Bin 1 Bin 2 Bin 3 Bin 6Bin 4 Bin 5
Distribution Clustering
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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
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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
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Computing the Spatial Locality Matrix
user A user B user C
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global location probability
Computing the Spatial Locality Matrix
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Clustering Objects
Cluster 1 Cluster 2 Cluster 3
1 2 3 4 5 6
Kullback-Leibler divergence Cosine similarity
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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
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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
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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
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Trajectory Clustering
174382
274311
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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
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Trajectory ClusteringTraditional clustering algorithms
developed for static and small datasets not suitable for large-scale trajectories and real-time stream data
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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
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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
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• 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
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推荐内容• 数学之美• 机器学习实战• python 入门• 分布式数据挖掘