This document proposes a method to identify potential traffic congestion based on transportation mode using spatio-temporal mining of GPS data. It divides an area into grids, clusters GPS trajectory points from different time intervals to determine grid density levels, and overlays the clusters to identify areas with consistently high densities that indicate potential for congestion. The authors develop an architecture based on previous research that adds spatial approaches like gridding and clustering overlays to accommodate identifying congested areas. The goal is to analyze how transportation modes fill space over time to determine where congestion regularly occurs.

1. Spatio-Temporal Mining to Identify Potential
Traffic Congestion Based on Transportation
Mode
Authors:
Irrevaldy
Gusti Ayu Putri Saptawati
School of Electrical Engineering and
Informatics,
Bandung Institute of Technology (ITB)
Palembang, Indonesia
1-2 November 2017

2. Introduction
 Increasing Development of a city -> Increasing use of different kind of
transportation mode -> increasing density level of space -> potential risk of
traffic congestion
 Increasing use of mobile devices with GPS Feature
 High loads of GPS data could be potential to use as an alternative solution
to the problem, if we handle it in such particular way.
 Challenge to develop related works to make a solution to the problem.

3. Problem Definition
 What is suitable criteria to identify potential congestion in a particular area?
 How could we develop previous works as a solution to identify potential
traffic congestion based on transportation mode?

4. Motivation
 Identify suitable criteria to detect potential traffic congestion using data
mining and spatio-temporal approaches
 Analyze Spatio-Temporal Mining method to identify potential traffic
congestion
 Develop architecture from previous research based on the needs of point
(1).

5. Related Works
 Y. Zheng, L. Liu, L. Wang, X. Xie “Learning transportation mode from raw
GPS data for geographic applications on the web”, 2008
 Y. Zheng, Q. Li, Y. Chen, X. Xie., W. Ma, “Understanding mobility based on
GPS data”, ACM, 2008.
 Other research…

6. GPS Data
Latitude Longitude Time
P1 Lat1 Long1 T1
P2 Lat2 Long2 T2
… … .. ..
Pn Latn Longn Tn
WalkCar
Zheng, Yu., Li, Quannan., Chen, Yukun., Xie, Xing., Ma, Wei-Ying., (2008) Understanding Mobility
Based on GPS Data, ACM.

7. Transportation Mode Detection
Architecture
Test Data
Segmentation
Extracting
Feature
Inference
Model
Post-Processing
Transportation
Mode
Online Inference Offline Learning
Traning Data
Segmentation
Extracting
Feature
Model Training
Change Point
Clustering
Graph Building
Knowledge
Extraction
Spatial
Knowledge
Spatial Indexing
Spatially
Indexed
Knowledge
Zheng, Yu., Li, Quannan., Chen, Yukun., Xie, Xing., Ma, Wei-Ying., (2008) Understanding Mobility
Based on GPS Data, ACM.

8. Tober’s First Law of Geography
 Everything is related to everything els, but near things are more related
than distant things.

9. Traffic Congestion Criteria Analysis
 An area is considered to have traffic congestion potential if in that
area there is increase in density level.
 In traffic congestion context, an area that has traffic congestion
potential is an area where there is many transportation user which
fill certain limit of space in a long time.

10. Solution Approach
 Based on those criteria, the steps to find area that has
traffic congestion potential is listed as below:
1) Divide area into several sub-areas
2) Find area which has quite high density
3) Measure the density based on how many transportation modes fill
a certain area
4) Measure the density of those area in several time intervals to know
which area that has high density every time.

11. Solution Approach
 In this research, GPS trajectory data is used to show transportation mode that pass a
certain area. GPS data has some trajectory which is consist of GPS points recorded every
seconds.
 If we relate traffic congestion criteria with a condition where a transportation is in a certain
area for a long time, then this condition can be described by how many points in a
trajectory of a transportation recorded in GPS log in a certain areas.
 If there is lots of points close to each other and overlapping each other then those points
shows that there are no movement or only few movements from the vehicle that sends the
GPS data.
 The condition of a vehicle moves too slow or not moving can also be known by looking at
the speed from one GPS point to another in a trajectory which is very low. This approach
can be used to detect an area which has density and potential traffic congestion.

12. Rectangular Grid

13. Point Per Time Interval Bar Chart

14. Density Shown Using DBSCAN

15. Density Illustration

16. Cluster Overlay

17. Architecture
Development
GPS
Trajectory
Raw Dataset
Extraction of
Derivative
Attribute
GPS Dataset with
Derivative
Attribute
Test Data
Segmentation
Extracting
Feature
Inference
Model
Post-
Processing
Transportation
Mode
Online
Inference
Offline
Learning
Traning
Data
Segmentation
Extracting
Feature
Model
Training
Change
Point
Clustering
Graph
Building
Knowledge
Extraction
Spatial
Knowledge
Spatial
Indexing
Spatially
Indexed
Knowledge
Labeled GPS
Trajectory Dataset
Extracting
Geospatial Data
GPS Trajectory
and Geospatial
Location
Database
Roadway
Geospatial
Data
City Border
Geospatial
Data
Roadway
and City
Border Data
Dividing Area into
Grid and Labeling
Several Grids with Label
Dividing Labeled GPS Dataset
into several time interval
group for each Grid
Labeled Time
Interval Group
GPS and
Geospatial
Location
Database
GPS Trajectory Data with grid label
and time group information
Spatio-Temporal
Clustering
Cluster Labeling
for GPS Trajectory
Data
GPS and
Geospatial
Location
Database
Visualization
Extract Potential
Traffic Congestion
and Transportation
Mode Data
Traffic Congestion
and
Transportation
Mode Data
Yu Zheng Architecture

18. Architecture Development
Labeled GPS
Trajectory Dataset
Extracting
Geospatial Data
GPS Trajectory
and Geospatial
Location
Database
Roadway
Geospatial
Data
City Border
Geospatial
Data
Roadway
and City
Border Data
Dividing Area into
Grid and Labeling
Several Grids with Label
Dividing Labeled GPS Dataset
into several time interval
group for each Grid
Labeled Time
Interval Group
GPS and
Geospatial
Location
Database
GPS Trajectory Data with grid label
and time group information
Spatio-Temporal
Clustering
Cluster Labeling
for GPS Trajectory
Data
GPS and
Geospatial
Location
Database
Visualization
Extract Potential
Traffic Congestion
and Transportation
Mode Data
Traffic Congestion
and
Transportation
Mode Data

19. Conclusion
 The previous research still could not yet accommodate the needs to
identify potential traffic congestion, although it provides data mining
method to infer transportation mode.
 We add some spatio-temporal approach to the previous works to
accommodate the process of identifying potential traffic congestion.
 We define clustering overlay to identify which part of an area having
potential traffic congestion

20. Thank You
Contact Email:
Irrevaldy@gmail.com
putri@informatika.org

21. Why this is a spatio-temporal mining
 Trajectories is a form of temporal snapshot model which is part of spatio-
temporal data mining.