mining movement patterns for predicting next locations meng chen
DESCRIPTION
predict the drivers' next locations recommend more reasonable routes Route recommendation predict next location in advance push information Targeted advertising MotivationTRANSCRIPT
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
174382
274311
,,,,
,,,,
lllllT
lllllT
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
