Compression and Mining of GPS Trace Data: New Techniques and Applications for
http://rip.trb.org/browse/dproject.asp?n=24443
Research in Progress
Compression and Mining of GPS Trace Data: New Techniques and
Applications for Transportation
http://www.utrc2.org/research/projects.php?viewid=202
Record Type: UTC
Research Statement
The widespread availability of inexpensive GPS devices has made it
feasible to passively collect a large volume of GPS trace data by
placing such devices in vehicles and by letting individuals carry them.
Each trace represents the trajectory of a vehicle or a person over time.
Using more sophisticated sensing devices, additional information (e.g.
the speed/acceleration of a vehicle, temperature and humidity values
inside a vehicle) can also be collected. A primary goal of this research
is to develop techniques that can provide additional insights to
transportation planners through the analysis and mining of trace data. A
long term goal of this research is to develop a prototype system that
can continuously process trace data and proactively notify
transportation planners regarding potential bottlenecks and accidents.
Benefits of the proposed research include the following:
(a) By a careful analysis of the trace data generated by GPS devices
carried by people, one can obtain a model of transportation-related
activities for people. For example, by overlapping the trace data on a
map and suitably partitioning the data into segments, trip purposes can
be identified more precisely. Models obtained on the basis of this
approach would be more accurate than those obtained using survey data.
(b) Analyzing the GPS trace data can also provide additional insights
regarding the modes of transportation used by people during different
parts of the day.
(c) By mining the trace data from vehicles, one can obtain predictive
models to estimate performance measures of traffic flow and to identify
potential bottlenecks and accidents.
Some challenges that arise in analyzing the trace data are the
following:
(a) The volume of data to be stored and analyzed is extremely large.
Thus, algorithmic techniques that can efficiently compress, analyze and
mine the data are needed.
(b) There may be errors in the trace data (because of a GPS device’s
limited accuracy) and some of the data may be missing (because the
quality of the signal from the satellite may be poor in certain
regions). Thus, the data needs to be carefully cleansed before analyzed.
(c) In certain applications (e.g. monitoring traffic flow), trace data
appears as a collection of real-time data streams. Special
stream-oriented operations must be carried out on the data to obtain
useful information.
The research team includes a faculty member from the Department of
Geography & Planning with expertise in various aspects of transportation
and three faculty members from the Department of Computer Science with
expertise in data mining, machine learning, processing stream data and
algorithm design. In addition, some researchers from General Electric
Global Research Center (Niskayuna, NY) have expressed willingness to
participate in this research. Thus, the research team is well qualified
to undertake the proposed research.
Start date: 2010/1/1
End date: 2010/12/31
Status: Active
Contract/Grant Number: RF 49111-18-21
Total Dollars: 102219
Source Organization: City College of City University of New York
Date Added: 12/07/2009
