Handbook of Research on Geoinformatics - Hassan A. Karimi Part 7 potx

276
Location-Based Services
Time of Arrival (TOA): The position of
a device can be determined by measuring the
transferring-time of a signal between the device
and the COO.
Time Difference of Arrival (TDOA): Deter-
mining a more precise position information of a
device by taking advantage of a cells infrastructure
and measuring the transferring time of a device
to three or more antennas.
Ubiquitous Information Management
(UIM): A communication concept, which is
free from temporal and, in general, from spatial
constraints.
Ultra Wideband (UWB): A technology
which enables very short-range positioning in-
formation.
277
Chapter XXXV
Coupling GPS and GIS
Mahbubur R. Meenar
Temple University, USA
John A. Sorrentino
Temple University, USA
Sharmin Yesmin
Temple University, USA
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Abstr Act
Since the 1990s, the integration of GPS and GIS has become more and more popular and an industry
standard in the GIS community worldwide. The increasing availability and affordability of mobile GIS

and GPS, along with greater data accuracy and interoperability, will only ensure steady growth of this
practice in the future. This chapter provides a brief background of GPS technology and its use in GIS,
and then elaborates on the integration techniques of both technologies within their limitations. It also
highlights data processing, transfer, and maintenance issues and future trends of this integration.
Introduct Ion
The use of the Global Positioning System (GPS)
as a method of collecting locational data for Geo-
graphic Information Systems (GIS) is increasing
in popularity in the GIS community. GIS data is
dynamic – it changes over time, and GPS is an
effective way to track those changes (Steede-Terry,
2000). According to Environmental Systems
Research Institute (ESRI) president Jack Dan-
germond, GPS is “uniquely suited to integration
with GIS. Whether the object of concern is moving
or not, whether concern is for a certain place at
a certain time, a series of places over time, or a
place with no regard to time, GPS can measure
it, locate it, track it.” (Steede-Terry, 2000).
278
Coupling GPS and GIS
Although GIS was available in the market in the
1970s, and GPS in the 1980s, it was only in the mid-
1990s that people started using GPS coupled to
GIS. The GPS technology and its analogs (Global
Navigation Satellite System or GLONASS in Rus-
sia and the proposed Galileo system in Europe)
have proven to be the most cost-effective, fastest,
and most accurate methods of providing location
information (Longley et. al, 2005; Trimble, 2002;

Taylor et. al, 2001). Organizations that maintain
GIS databases – be they local governments or oil
companies – can easily and accurately inventory
either stationary or moving things and add those
locations to their databases (Imran et. al, 2006;
Steede-Terry, 2000). Some common applications
of coupling GPS and GIS are surveying, crime
mapping, animal tracking, trafc management,
emergency management, road construction, and
vehicle navigation.
bAckground
need for gps data in gIs
When people try to nd out where on earth they
are located, they rely on either absolute coordi-
nates with latitude and longitude information or
relative coordinates where location information is
expressed with the help of another location (Ken-
nedy, 2002). GIS maps can be created or corrected
from the features entered in the eld using a GPS
receiver (Maantay and Ziegler, 2006). Thus people
can know their actual positions on earth and then
compare their locations in relation to other objects
represented in a GIS map (Thurston et. al, 2003;
Kennedy, 2002).
GIS uses mainly two types of datasets: (a)
primary, which is created by the user; and (b)
secondary, which is collected or purchased from
somewhere else. In GIS, primary data can be
created by drawing any feature based on given
dimensions, by digitizing ortho-photos, and by

analyzing survey, remote sensing, and GPS data.
Using GPS, primary data can be collected ac-
curately and quickly with a common reference
system without any drawing or digitizing opera-
tion. Once the primary data is created, it can be
distributed to others and be used as secondary
data. Before using GPS as a primary data collec-
tion tool for GIS, the users need to understand the
GPS technology and its limitations.
The GPS Technology
The GPS data can be collected from a constellation
of active satellites which continuously transmit
coded signals to receivers and receive correctional
data from monitoring stations. GPS receivers
process the signals to compute latitude, longitude,
and altitude of an object on earth (Giaglis, 2005;
Kennedy, 2002).
A method, known as triangulation, is used
to calculate the position of any feature with the
known distances from three xed locations (Le-
tham, 2001). However, a discrepancy between
satellite and receiver timing of just 1/100th of
a second could make for a misreading of 1,860
miles (Steede-Terry, 2000). Therefore, a signal
from a fourth satellite is needed to synchronize
the time between the satellites and the receivers
(Maantay and Ziegler, 2006; Longley et. al, 2005;
Letham, 2001). To address this fact, the satellites
have been deployed in a pattern that has each one
passing over a monitoring station every twelve

hours, with at least four visible in the sky all the
times (Steede-Terry, 2000).
The United States Navigation Satellite Timing
and Ranging GPS (NAVSTAR-GPS) constella-
tion has 24 satellites with 3 spares orbiting the
earth at an altitude of about 12,600 miles (USNO
NAVSTAR GPS, 2006; Longley et. al, 2005;
Steede-Terry, 2000). The GLONASS consists of 21
satellites in 3 orbital planes, with 3 on-orbit spares
(Space and Tech, 2005). The proposed system
GALILEO will be based on a constellation of 30
satellites and ground stations (Europa, 2005).
279
Coupling GPS and GIS
The NAVSTAR-GPS has three basic segments:
(1) the space segment, which consists of the satel-
lites; (2) the control segment, which is a network
of earth-based tracking stations; and (3) the user
segment, which represents the receivers that pick
up signals from the satellites, process the signal
data, and compute the receiver’s location, height,
and time (Maantay and Ziegler, 2006; Lange and
Gilbert, 2005).
Data Limitations and Accuracy Level

Besides the timing discrepancies between the
satellites and the receivers, some other elements
that reduce the accuracy of GPS data are orbit
errors, system errors, the earth’s atmosphere,
and receiver noise (Trimble, 2002; Ramadan,

1998). With better attention to interoperability
between the GPS units, hardware, and software,
some of these errors can be minimized before
the data are used in GIS (Thurston et. al, 2003;
Kennedy, 2002).
Using a differential correction process, the
receivers can correct such errors. The Differential
GPS (DGPS) uses two receivers, one stationary
and one roving. The stationary one, known as
the base station, is placed at a precisely known
geographic point, and the roving one is carried by
the surveyor (Maantay and Ziegler, 2006; Imran
et. al, 2006; Thurston et. al, 2003; Kennedy, 2002;
Taylor et. al, 2001; Steede-Terry, 2000). The base
station sends differential correction signals to the
moving receiver.
Prior to 2000, the GPS signal data that was
available for free did not deliver horizontal po-
sitional accuracies better than 100 meters. Data
with high degree of accuracy was only available
to U.S. government agencies and to some uni-
versities. After the U.S. Department of Defense
removed the restriction in May 2000, the positional
accuracy of free satellite signal data increased to
15 meters (Maantay and Ziegler, 2006). In Sep-
tember 2002, this accuracy was further increased
to 1 to 2 meters horizontally and 2 to 3 meters
vertically using a Federal Aviation Administration
funded system known as Wide Area Augmenta-
tion System (WAAS). WAAS is available to the

public throughout most of the continental United
States (Maantay and Ziegler, 2006).
Depending on the receiver system, the DGPS
can deliver positional accuracies of 1 meter or less
and is used where high accuracy data is required
(Maantay and Ziegler, 2006; Longley et. al, 2005;
Lange and Gilbert, 2005; Taylor et. al, 2001). For
example, the surveying professionals now use
Carrier Phase Tracking, an application of DGPS,
which returns positional accuracies down to as
little as 10 centimeters (Maantay and Ziegler,
2006; Lange and Gilbert, 2005).
Integr At Ion of gps And gIs
The coupling of GPS and GIS can be explained
by the following examples:
• A el
d crew can use a GPS receiver to enter
the location of a power line pole in need of
repair; show it as a point on a map displayed
on a personal digital assistant (PDA) using
software such as ArcPad from ESRI; enter
attributes of the pole; and nally transmit this
information to a central database (Maantay
and Ziegler, 2006).
•
A re
searcher may conduct a groundwater
contamination study by collecting the co-
ordinates and other attributes of the wells
using a GPS; converting the data to GIS;

measuring the water samples taken from
the wells; and evaluating the water quality
parameters (Nas and Berktay, 2006).
There are many ways to integrate GPS data
in GIS, ranging from creating new GIS features
in the eld, transferring data from GPS receiv-
ers to GIS, and conducting spatial analysis in the
eld (Harrington, 2000a). More specically, the
GPS-GIS integration can be done based on the
280
Coupling GPS and GIS
following three categories – data-focused integra-
tion, position-focused integration, and technol-
ogy-focused integration (Harrington, 2000a). In
data-focused integration, the GPS system collects
and stores data, and then later, transfers data to
a GIS. Again, data from GIS can be uploaded
to GPS for update and maintenance. The posi-
tion-focused integration consists of a complete
GPS receiver that supplies a control application
and a eld device application operating on the
same device or separate devices. In the technol-
ogy-focused integration, there is no need for a
separate application of a device to control the GPS
receiver; the control is archived from any third
party software (Harrington, 2000a).
Figure 1 provides an example of a schematic
workow process of the GPS-GIS integration by
using Trimble and ArcGIS software. In short, the
integration of GPS and GIS is primarily focused

on three areas - data acquisition, data processing
and transfer, and data maintenance.
Data Acquisition
Before collecting any data, the user needs to de-
termine what types of GPS techniques and tools
will be required for a particular accuracy require-
ment and budget. The user needs to develop or
collect a GIS base data layer with correct spatial
reference to which all new generated data will be
referenced (Lange and Gilbert, 2005).
The scale and datum of the base map are also
important. For example, a large-scale base map
should be used as a reference in a site specic
project in order to avoid data inaccuracy. While
collecting GPS data in an existing GIS, the datum
designation, the projection and coordinate system
designation, and the measurement units must be
identical (Kennedy, 2002; Steede-Terry, 2000). It
is recommended that all data should be collected
and displayed in the most up-to-date datum avail-
able (Lange and Gilbert, 2005).
The user may create a data dictionary with
the list of features and attributes to be recorded
before going to the eld or on-spot. If it is created
beforehand, the table is then transferred into the
GPS data collection system. Before going to the
eld, the user also needs to nd out whether the
locations that will be targeted for data collection
are free from obstructions. The receivers need
a clear view of the sky and signals from at least

four satellites in order to make reliable position
measurement (Lange and Gilbert, 2005; Giaglis,
2005). In the eld, the user will check satellite
availability and follow the manuals to congure
GPS receivers before starting data collection.
GIS uses point, line, and polygon features, and
the data collection methods for these features are
different from one another. A point feature (e.g.,
an electricity transmission pole) requires the user
Figure 1. Example workow process of GPS-GIS
integration
281
Coupling GPS and GIS
to remain stationary at the location and capture
the information using a GPS device. For a line
feature (e.g., a road), the user needs to record the
positions periodically as s/he moves along the
feature in the real world. To capture a polygon
feature (e.g., a parking lot) information, the posi-
tions of the recorder are connected in order to form
a polygon and the last position always connects
back to the rst one. The user has to decide what
types of features need to be created for a GIS map.
In a small scale map, a university campus can be
shown as a point, whereas in a detailed map, even
a drain outlet can be shown as a polygon.
GPS coordinates can be displayed in real time
in some GIS software such as ESRI ArcPad,
Intergraph Intelliwhere, and Terra Nova Map
IT. In the age of mobile GIS, users can go on

a eld trip, collect GPS data, edit, manipulate,
and visualize those data, all in the eld. While
GPS and GIS are linked, the GPS receiver can
be treated as the cursor of a digitizer. It is linked
to the GIS through a software module similar to
a digitizer controller where data are saved into a
GIS ling system (Ramadan, 1998; UN Statistics
Division, 2004). In real-time GPS/GIS integration,
data may be collected and stored immediately for
future use in a mapping application, or data may
be discarded after use in a navigation or tracking
application (Thurston et. al, 2003).
For example, Map IT is a new GIS software
designed for digital mapping and GPS data capture
with a tablet PC. The software connects a tablet pc
to a GPS antenna via a USB port. While conduct-
ing the eld work, the user may use the software
to: (a) display the current ground position on the
tablet PC’s map display in real time; (b) create new
features and add coordinates and other attributes;
(c) edit or post-process the data in real time; and
(d) automatically link all activity recorded in the
eld (including photographs, notes, spreadsheets,
and drawings) to the respective geographic posi-
tions (Donatis and Bruciatelli, 2006).
Although the integration of GIS and GPS can
in general increase accuracy and decrease project
costs and completion time, it can also create new
problems, including creation of inaccurate data
points and missing data points (Imran et. al, 2006).

Sometimes a handheld GPS navigator may not
be able to acquire a lock on available satellites
because of natural conditions like dense forest
canopies, or human-made structures like tall
buildings or other obstacles (Lange and Gilbert,
2005; Thurston et. al, 2003). Data collection with
GPS also might get affected by any equipment
malfunction in the eld.
data processing and t ransfer
Once the data are collected, they can be download-
ed, post-processed, and exported to GIS format
from the eld computer to the ofce computer.
Where real-time signals are needed but cannot
be received, the post-processing techniques can
be applied to re-process the GPS positions. Us-
ing this technique, the feature positions can be
differentially corrected to the highest level of
accuracy. The users who integrate GPS data into
their own applications need to consider how and
when they should apply differential corrections.
Real-time processing allows recording and
correcting a location in seconds or less, but is
usually less accurate. Post-processing allows
the surveyor recording a location as much time
as s/he likes, and then differentially corrects
each location back in the ofce. This technique
is used in mapping or surveying (Steede-Terry,
2000; Thurston et. al, 2003). Instead of relying
on real-time DGPS alone, the users should enable
their applications to record raw GPS data and al-

low post-processing techniques to be used either
solely or in conjunction with real-time DGPS
(Harrington, 2000b).
Most GPS receiver manufacturers have their
own data le format. GPS data is stored in a
receiver in its own format and later can be trans-
lated to various GIS formats (Lange and Gilbert,
282
Coupling GPS and GIS
2005; Ramadan, 1998). Data can be transferred
in a couple of ways. One simple way is collecting
coordinates and attributes in a comma delimited
le from the GPS device storage. The other more
preferable way is converting the data from GPS
storage to the user-specic database interchange
format using a data translation program (Lange
and Gilbert, 2005). Such a program allows the
user to (1) generate metadata; (2) transform the
coordinates to the projection, coordinate system,
and datum of the user’s choice; and (3) translate
GPS data into customized formats that the GPS
manufacturers could never have anticipated
(Lange and Gilbert, 2005).
A number of le interchange protocols are
available to exchange data between different
brands and types of receivers. One widely used
interchange protocol is the Receiver Independent
Exchange Format (RINEX), which is supported
by most satellite data processing software (Yan,
2006). Another commonly used interface standard

is a standard released by the National Marine
Electronics Association (NMEA). Most GPS
receivers support this protocol and can output
NMEA messages, which are available in ASCII
format (Yan, 2006).
data Maintenance
For data revisions or data maintenance, GIS data
is transferred back to the eld computer and can
be veried or updated in the eld. The user can
relocate features via navigation, verify the position
and attribute features, and navigate to locations
to collect new attribute data. The user may select
features and examine them in the eld, modify
attributes, and even collect new features if desired.
Using receivers such as Trimble, any feature that
has been added or updated is automatically marked
to determine which data needs to go back to GIS
(Trimble, 2002).
future trends
The future trends of GIS-GPS integration will
be focused on data accuracy, interoperability,
and affordability. In order to make the WAAS
level of precision available to users worldwide,
the Unites States is working on international
agreements to share similar technologies avail-
able in other parts of the world, namely Japan’s
Multi-Functional Satellite Augmentation System
(MSAS) and Europe’s Euro Geostationary Navi-
gation Overlay Service (EGNOS) (Maantay and
Ziegler, 2006). In addition, the European satellite

positioning system, Galileo, will be dedicated to
civilian activities which will further increase the
availability of accurate data to general users.
New applications of GIS-GPS integration are
constantly becoming popular and widespread. The
latest developments in GPS technology should
encourage more use of such integration in the
future. Reduction in cost and personnel training
time of using GPS technology with high data
accuracy will eventually provide a cost-effective
means of verifying and updating real time GIS
mapping in the eld (Maantay and Ziegler, 2006;
UN Statistics Division, 2004).
conc Lus Ion
In today’s market, the mobile GIS and GPS devices
are available with greater accuracy at a reduced
cost. The data transfer process from GPS to GIS
has become faster and easier. GIS software is get-
ting more powerful and user friendly, and GPS
devices are increasingly getting more accurate
and affordable. The integration of GIS and GPS
has been already proven to be very inuential in
spatial data management, and it will have steady
growth in the future.
283
Coupling GPS and GIS
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Giaglis, G. (2005). Mobile Location Services. In
M. Khosrow-Pour (Ed.), Encyclopedia of Infor-
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gies that Work Well Together. Proceedings in the
ESRI User Conference, San Diego, California.
Harrington, A. (2000b). GPS/GIS Integration:
What Can You Do When Real-Time DGPS Doesn’t
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accessed on August 25, 2006.
Imran, M., Hassan, Y., & Patterson, D. (2006).
GPS-GIS-Based Procedure for Tracking Vehicle
Path on Horizontal Alignments. Computer-Aided
Civil and Infrastructure Engineering, 21(5),
383-394.
Kennedy, M. (2002). The Global Positioning
System and GIS: An Introduction. New York:
Taylor and Francis.
Maantay, J. & Ziegler, J. (2006). GIS for the Urban
Environment. California: ESRI Press, 306-307.
Nas, B. & Berktay, A. (2006). Groundwater
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(Turkey): A GIS Perspective. Journal of Environ-
mental Management. 79(1), 30-37.
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Letham, L. (2001). GPS Made Easy. Washington:
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Longley, P., Goodchild, M., Maguire, D., & Rhind,
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Steede-Terry, K. (2000). Integrating GIS and
the Global Positioning System. California: ESRI
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Corbett, S. (2001). Road Reduction Filtering for
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Thurston, J., Poiker, T., & Moore, J. (2003). In-
tegrated Geospatial Technologies – A Guide to
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Trimble Navigation Limited. (2002). TerraSync
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and Maintenance Tool for Quality GIS Data.
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284
Coupling GPS and GIS
key t er Ms
Coordinate System: A reference framework
used to dene the positions of points in space in
either two or three dimensions.
Datum: The reference specications of a
measurement system, usually a system of coor-
dinate positions on a surface or heights above or
below a surface.
DGPS: The Differential GPS (DGPS) is used
to correct GPS signal data errors, using two receiv-
ers, one stationary (placed at a precisely known
geographic point) and one roving (carried by the
surveyor). The stationary receiver sends differ-
ential correction signals to the roving one.
GPS Segment: GPS consists of three seg-
ments: (i) space segment – the GPS satellites, (ii)

user segment – the GPS handheld navigator, and
(iii) ground control segment – the GPS monitor-
ing stations.
Projection: A method requiring a system-
atic mathematical transformation by which the
curved surface of the earth is portrayed on a at
surface.
Scale: The ratio between a distance or area
on a map and the corresponding distance or area
on the ground, commonly expressed as a frac-
tion or ratio.
WAAS: The Wide Area Augmentation System
(WAAS) is a system that can increase the GPS
signal data accuracy to 1 to 2 meters horizontally
and 2 to 3 meters vertically.
285
Chapter XXXVI
Modern Navigation Systems
and Related Spatial Query
Wei-Shinn Ku
Auburn University, USA
Haojun Wang
University of Southern California, USA
Roger Zimmermann
National University of Singapore, Singapore
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Abstr Act
With the availability and accuracy of satellite-based positioning systems and the growing computational
power of mobile devices, recent research and commercial products of navigation systems are focusing
on incorporating real-time information for supporting various applications. In addition, for routing

purposes, navigation systems implement many algorithms related to path nding (e.g., shortest path
search algorithms). This chapter presents the foundation and state-of-the-art development of navigation
systems and reviews several spatial query related algorithms.
Introduct Ion
Navigation systems have been of growing interest
in both industry and academia in recent years. The
foundation of navigation systems is based on the
concept of utilizing radio time signals sent from
some wide-range transmitters to enable mobile
receivers to determine their exact geographic
286
Modern Navigation Systems and Related Spatial Query
the server via wired or wireless networking in-
frastructures.
positioning signal t ransmission
Systems
Positioning signal transmitters, such as satellites
and base stations, broadcast precise timing signals
by radio to receivers, allowing them to determine
exact geographic locations and then dynamically
display and update their current position on digital
maps. As of 2006, the Global Positioning System
(GPS) is the only fully functional satellite-based
positioning signal transmission system.
Global Positioning System
The invention of GPS has had a huge inuence
on modern navigation systems. GPS was devel-
oped by the U.S. Department of Defense in the
mid-1980s. Since it became fully functional in
1994, GPS has acted as the backbone of modern

navigation systems around the world.
The GPS consists of a constellation of 24 sat-
ellites in circular orbits at an altitude of 20,200
kilometers (Leick, 1995). Each satellite circles
the Earth twice a day. Furthermore, there are six
orbital planes with four satellites in each plane. The
orbits were designed so that at least four satellites
are always within line-of-sight from most places
on the earth (Langley, 1991). The trajectory of the
satellites is measured by ve monitoring stations
around the world (Ascension Island, Colorado
Springs, Diego Garcia, Hawaii, and Kwajalein).
The master control station, at Schriever Air
Force Base, processes the monitoring informa-
tion and updates the onboard atomic clocks and
the ephemeris of satellites through monitoring
stations (El-Rabbany, 2002).
Each GPS satellite repeatedly broadcasts radio
signals traveling by line-of-sight, meaning that
they will pass through air but will not penetrate
most solid objects. GPS signals contain three
location. Based on this precise location, mobile
receivers are able to perform location-based
services (Shekhar, et al 2004). With the avail-
ability and accuracy of satellite-based positioning
systems and the growing computational power of
mobile devices, recent research, and commercial
products of navigation systems are focusing on
incorporating real-time information for support-
ing various applications. In addition, for routing

purposes navigation systems implement many
algorithms related to path nding (e.g., shortest
path search algorithms). An increasing number
of useful applications are implemented based on
these fundamental algorithms.
Modern nAvIgAt Ion syste Ms
A navigation system is an integration of position
and orientation devices, computation devices,
communication hardware and software for guid-
ing the movement of objects (e.g., people, vehicles,
etc.) from one location to another. In general,
the infrastructure of navigation systems can be
classied into two subsystems: positioning sig-
nal transmission systems and positioning signal
receivers. The positioning signal transmission
system allows the signal receiver to determine its
location (longitude, latitude, and altitude) using
timing signals. Positioning signal receivers range
from hand-held devices, cellular phones, to car-
based devices. These devices typically include
some storage of map data and the computing
capabilities of spatial operations, such as calculat-
ing directions. Additionally, in some novel geo-
informatics applications, the receiver also relies
on some server components for various services,
such as real-time trafc information. In such a
scenario, a server infrastructure is introduced
which includes a Web server, a spatial database
server, and an application server to provide these
services. The signal receiver communicates with

287
Modern Navigation Systems and Related Spatial Query
pieces of information (Hofmann-Wellenhof et
al, 1994): a pseudo random sequence, ephemeris
data, and almanac data. The pseudo random
sequence identies which satellite is transmit-
ting the signal. Ephemeris data allows the GPS
receiver to determine the location of GPS satellites
at any time throughout the day. Almanac data
consists of information about the satellite status
and current time from the onboard atomic clock
of the satellite.
The GPS receiver calculates its location based
on GPS signals using the principle of trilateration
(Kennedy, 2002). First, the GPS receiver calcu-
lates its distance to a GPS satellite based on the
timing signal transmission delay from the satel-
lite to the receiver multiplied by the speed of
radio signals. After measuring its distance to at
least four satellites, the GPS receiver calculates
its current position at the intersection of four
abstract spheres, one around each satellite, with
a radius of the distance from the satellite to the
GPS receiver.
GPS Accuracy
As a positioning signal transmission system,
the accuracy of GPS is a very important issue.
However, GPS was initially introduced with a
feature called Selective Availability (or SA) that
intentionally degraded the accuracy by introduc-

ing an error of up to 100 meters into the civil tim-
ing signals. Improved accuracy was available to
the United States military and a few other users
who were given access to the undegraded timing
signal. On May 1, 2000, SA was nally turned
off, resulting in a substantial improvement of the
GPS accuracy (Conley, 2000).
Additionally, the accuracy of GPS can be af-
fected by the atmospheric conditions (e.g., Iono-
sphere, Troposphere) as well as reections of the
radio signal off the ground and the surrounding
structures close to a GPS receiver. The normal
GPS accuracy is about 30 meters horizontally and
52 meters vertically at the 95% probability level
when the SA option is turned off (Kennedy, 2002).
There are several approaches that have been used
to improve the accuracy of GPS.
Differential GPS (DGPS) (Kennedy, 2002)
uses a network of stationary GPS receivers on the
ground acting as static reference points to calculate
and transmit correction messages via FM signals
to surrounding GPS receivers in a local area. The
improved accuracy provided by DGPS is equal to
0.5 m to 1 m near the reference point at the 95%
probability level (Monteiro et al. 2005). Before
the SA option was turned off by the Department
of Defense, DGPS was used by many civilian
GPS devices to improve the accuracy.
The Wide Area Augmentation System (WAAS)
(Loh, Wullschleger et al. 1995) has been widely

embedded in GPS devices recently. WAAS uses
25 ground reference stations across the United
States to receive GPS signals and calculate cor-
rection messages. The correction messages are
uploaded to a geosynchronous satellite and then
broadcast from the satellite on the same frequency
as GPS to the receivers. Currently WAAS only
works for North America as of 2006. However,
the European Geostationary Navigation Overlay
Service (EGNOS) and the Multi-Functional Sat-
ellite Augmentation System (MSAS) are being
developed in Europe and Japan, respectively. They
can be regarded as variants of WAAS.
The Local Area Augmentation System (LAAS)
(United States Department of Transportation,
FAA, 2002) uses a similar approach where cor-
rection messages are calculated, transmitted,
and broadcast via VHF data link within a local
area where accurate positioning is needed. The
transmission range of these correction messages
is typically about a 30 to 50 kilometer radius
around the transmitter.
gLon Ass and galileo positioning
System
The GLObal NAvigation Satellite System
(Global’naya Navigatsionnaya Sputnikovaya
288
Modern Navigation Systems and Related Spatial Query
Sistema, GLONASS) is a satellite-based position-
ing signal transmission system developed by the

Russian government as a counterpart to GPS in
the 1980’s. The complete GLONASS consists
of 24 satellites in circular orbits at an altitude
of 19,100 kilometers. Each satellite circles the
Earth in approximately 11 hours, 15 minutes.
The orbits were designed such that at least ve
satellites are always within line-of-sight at any
given time. Based on measurements from the
timing signal of four satellites simultaneously,
the system is able to offer location information
with an accuracy of 70 meters.
There were 17 satellites in operation by
December 2005 offering limited usage. With
the participation of the Indian government, it is
expected that the system will be fully operational
with all 24 satellites by 2010.
GALILEO (Issle et al. 2003) is being developed
by the European Union as an alternative to GPS
and GLONASS. GALILEO is intended to provide
positioning signals with a precision higher than
GPS to both civil and military users. Moreover, it
improves the coverage of satellite signals at high
latitude areas. The constellation of GALILEO con-
sists of 30 satellites in circular orbits at an altitude
of 23,222 kilometers. The GALILEO system is
expected to be fully operational by 2010.
positioning signal r eceivers
Most positioning signal receiving devices are
designed for the use with the GPS system. These
devices have been manufactured in a wide variety

for different purposes, from devices integrated
into cars, personal digital assistants, and phones,
to dedicated devices such as hand-held GPS
receivers. The most popular variants are used in
car-based navigation systems that visualize the
position information calculated from GPS signals
to locate an automobile on a road retrieved from
a map database.
In these car-based systems, the map database
usually consists of vector information of some
area of interest. Streets and points of interest are
encoded and stored as geographic coordinates.
The client is able to nd some desired places
through searching by address, name, or geographic
coordinates. The map database is usually stored
on some removable media, such as a CD or ash
memory. A common approach is to have a base
map permanently stored in the ROM of GPS de-
vices. Additional detailed information of areas of
interest can be downloaded from a CD or online
by the user in the future.
Integrating the positioning data from a GPS
receiver with the Geographic Information Sys-
tem (GIS) involves data retrieval, data format
transformation, multi-layer data display, and
data processing. With GPS, it is possible to col-
lect the positioning data in either the real-time or
post-processed mode. The digital format of GPS
data is then converted into a compatible format
used in the GIS applications (Steede-Terry 2000;

Kennedy, 2002). Together with other spatially
referenced data (e.g., the digital road map data),
the GIS application consists of a collection of
layers that can be analyzed for a wide variety of
purposes, such as calculating the route from the
current position to a destination.
nAvIgAt Ion r eLAted spAt IAL
Quer y ALgor Ith Ms
As mentioned earlier, many location-based
end-user applications can be provided after a
positioning signal receiver calculates the posi-
tion of a user. There are several spatial query
algorithms which are commonly utilized by
modern positioning signal receivers (e.g., GPS
devices) for supporting location-based services
and shortest path routing. We broadly categorize
them into location-based query algorithms and
shortest path search algorithms in this section.
289
Modern Navigation Systems and Related Spatial Query
Table 1 summarizes the symbolic notations used
throughout this section.
Location-Based Query Algorithms
Point Query The term point query (PQ) can
be dened as: given a query point q, nd all the
spatial objects O which contain q.
PQ(q ) {O| o O,q o }
i i
The query processing efciency of a point query
can be improved by utilizing spatial indices, e.g.,

the R-tree (Guttman, 1984) or the Quadtree (Samet,
1984). With a spatial index, all the spatial objects
are represented by geometric approximations such
as an MBR (Minimum Bounding Rectangle).
Consequently, determining whether the query
point is in an MBR is less expensive than check-
ing if the query point is in an irregular polygon.
After retrieving all the MBRs which overlap with
the query point as candidates, the exact geometry
of each element in the candidate set is examined.
Point queries can be applied to determine the
overlapping regions (e.g., administrative divisions)
of navigation system users.
Nearest Neighbor Query The term nearest
neighbor query (NNQ) can be dened as: given
a query point q and a set of spatial objects O, nd
the spatial object o
i

∈
O which has the shortest
distance to q.
NNQ(q ) {o | o O,dist( q,o ) dist( q,o )}
i j i j
R-trees and their derivatives (Sellis et al. 1987;
Beckmann et al. 1990) have been a prevalent
method to index spatial data and increase query
performance. To nd nearest neighbors, branch-
and-bound algorithms have been designed that
search an R-tree in either a depth-rst (Rousso-

poulos et al. 1995) or best-rst manner (Hjaltason
& Samet, 1999) to detect and lter out unqualied
branches. Both types of algorithms were designed
for stationary objects and query points. They may
be used when moving objects infrequently pose
nearest neighbor queries.
Range Query The term range query (RQ) can
be dened as: given a query polygon q and a set
of spatial objects O, nd all the spatial objects in
O which intersect with q.
RQ(q ) {O| o O,o q }
i i
j
Range queries can be solved in a top-down recur-
sive procedure utilizing spatial index structures
(e.g., the R-tree). The query region is examined
rst against each branch (MBR) from the root.
If the query polygon overlaps with any branch,
the search algorithm is employed recursively
on sub-entries. This process terminates after it
reaches the leaf nodes of the index structure. The
selected entries in the leaves are used to retrieve
the records associated with the selected spatial
keys (Shekhar et al. 2004).
shortest path search Algorithms
Dijkstra’s Algorithm One important function
of navigation systems is to nd the shortest route
to a user specied destination. The well-known
Dijkstra’s algorithm (Dijkstra, 1959) provides an
ideal solution for nding single-source shortest

paths in a graph of vertices connected through
edges. We present the algorithm, assuming that
there is a path from the vertex of interest to each
of the other vertices. It is a simple modica-
tion to handle the case where this is not so. We
initialize a set of vertices D to contain only the
Table 1. Symbolic notation
Symbol Meaning
q
O
dist(a,b)
D
E
The location of a query point
A set of spatial objects
The Euclidean distance between two objects a and b
A set of nodes
A set of edges
290
Modern Navigation Systems and Related Spatial Query
node whose shortest paths are to be determined
and assume the vertex of interest is v
1
. We also
initialize a set E of edges to being empty. First
we choose a vertex v
i
that is closest to v
1
and add

it to D. In addition, we also add the edge < v
1
, v
i

> to E. That edge is clearly a shortest path from
v
1
to v
i
. Then we check the paths from v
1
to the
remaining vertices that allow only vertices in D
as intermediate vertices. A shortest of these paths
is a shortest path. The vertex at the end of such a
path is added to D and the edge that touches that
vertex is added to E. This procedure is continued
until D covers all the vertices. At this point, E
contains the edges for the shortest paths (Nea-
politan & Naimipour, 1998).
Adaptive Shortest Path Search Algorithm
Most existing shortest path searching algorithms
are executed based on static distance informa-
tion: pre-dened road segments with xed road
conditions are used in the computation. However
any real-time events (e.g., detours, trafc conges-
tions, etc.) affecting the spatial network cannot
be reected in the query result. For example, a
trafc jam occurring on the route to the computed

destination most likely elongates the total driving
time. More drastically, the closure of a restaurant
which was found as the destination according to
its network distance might even invalidate a query
result. In other words, nding the shortest path in
terms of travel time is more important than the
actual distance. Therefore, we need adaptive short-
est path search algorithms which can integrate
real-time events into the search/routing procedure.
Ku et al. (Ku et al. 2005) proposed a novel travel
time network that integrates both road network
and real-time trafc event information. Based on
this foundation of the travel time network, they
developed an adaptive shortest path search algo-
rithm that utilizes real-time trafc information
to provide adaptive shortest path search results.
This novel technique could be implemented in
future navigation systems.
conc Lus Ion
We have presented the foundation and state of
the art development of navigation systems and
reviewed several spatial query related algorithms.
GPS has been increasingly used in both military
and civilian applications. It can be forecast that
GPS will be extensively used and its applicability
expanded into new areas of applications in the
future. Meanwhile, additional civilian frequencies
will be developed and allocated to ease the conges-
tion of civil usage. GPS developers are anticipating
the advent of the European GALILEO system that

will introduce the birth of the Global Navigation
Satellite System (GNSS) infrastructure, which
combines the functionality of GPS and GALILEO
together (Gibbons 2004). The interoperation of
GPS and GALILEO will benet the users with
more signal availability, more signal power, and
improved signal redundancy around the world.
In addition, several websites (e.g., MapQuest,
Yahoo! Maps, etc.) have integrated shortest path
search algorithms into on-line services. Users
can conveniently search the shortest path to their
destinations by utilizing these services.
Acknow Ledg Ment
This article was made possible by the NSF grants
ERC Cooperative Agreement No. EEC-9529152,
CMS-0219463 (ITR), and IIS-0534761. Any
opinions, ndings and conclusions or recom-
mendations expressed in this material are those
of the authors and do not necessarily reect those
of the National Science Foundation.
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key t er Ms
Ephemeris: Refers to the relative positions
of the planets, or satellites in the sky at a given
moment.
292
Modern Navigation Systems and Related Spatial Query
Geosynchronous Satellite: A satellite whose
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293
Chapter XXXVII

Location Privacy in Automotive
Telematics
Muhammad Usman Iqbal
University of New South Wales, Australia
Samsung Lim
University of New South Wales, Australia
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Abstr Act
Over the past few decades, the technologies of mobile communication, positioning, and computing have
gradually converged. The automobile has been a natural platform for this convergence where satellite-
based positioning, wireless communication and on-board computing work in tandem offering various
services to motorists. While there are many opportunities with these novel services, signi.cant risks to
the location privacy of motorists also exist as a result of the fast-paced technological evolution. These
risks must be confronted if trust and condence are to prevail between motorists and service providers.
This chapter provides an overview of the current situation of location privacy in automotive telematics
by exploring possible abuses and existing approaches to curb these abuses followed by a discussion of
possible privacy-strengthening measures.
294
Location Privacy in Automotive Telematics
Introduct Ion
The proliferation of location-aware computing
devices promises an array of “quality-of-life
enhancing” applications. These services include
in-car navigation, roadside assistance, infotain-
ment, emergency response services, vehicle
diagnostics and prognostics. The key idea is to
provide services using “location” as a geographic
lter. These services can be triggered by an event,
for example, the location of the vehicle can be
transmitted to an emergency response center on

deployment of air bags. Some services can be
explicitly requested by the driver, for example,
in-car navigation or road side assistance. While
other applications can be quietly running at all
times, passing on real-time information of the
vehicle’s movements such as Global Positioning
System (GPS) enabled Pay-As-You-Drive (PAYD)
insurance (Grush, 2005).
Although location data is critical to the opera-
tion of such applications, there is a precarious
balance between the necessary dissemination of
location information and the potential for abuse
of this private information. Spatio-temporal (loca-
tion in time) information continuously monitored
(and logged) about the places a person visits can
reveal a lot about one’s persona. Given the current
capabilities of inference by combining disparate
sources of information, a lot can be inferred about
an individual. These derived proles can then
be used to make judgments about a person or
used for unsolicited marketing by location-based
marketers. Orwell (1949), in his criticism against
totalitarianism, would have most likely referred
to these “Small Brothers” (location-based retail
marketers) had he known about these inference
attacks.
In the next few sections a background on loca-
tion privacy is presented, some possible privacy
abuses of telematics services are discussed, and
existing approaches to curb these abuses are

investigated. The chapter then suggests possible
measures to strengthen location privacy.
bAckground
Before delving into the core issue of location
privacy, it is important to agree on a denition of
privacy itself. Much of the literature pertaining
to privacy refers to Westin’s precise denition.
In the context of telematics, location privacy is a
special case of privacy, relating to the privacy of
location information of the vehicle, and ultimately
the user of the vehicle.
Privacy is the claim of individuals, groups and
institutions to determine for themselves, when,
how and to what extent information about them
is communicated to others. (Westin, 1967)
How Positioning Systems can be
Privacy Invasive?
Positioning systems can be categorized into either
being ‘Self-positioning’ or ‘Remote-positioning’.
In Self-positioning systems, the vehicle is either
tted with a GPS receiver or Dead-Reckoning
system (based on one or more gyroscopes, a
compass and odometer) to locate where it is on
the road. Remote-positioning systems require
a central site to determine the location of the
vehicle (Drane and Rizos, 1997). The result is
a set of coordinates (or position) of the vehicle
expressed in relation to a reference frame or da-
tum. Self-positioning systems inherently protect
location privacy because they do not report the

location of the vehicle to any other system. On
the other hand, remote-positioning systems track,
compute and retain the location information at
the central monitoring site and creates a risk to
the individual’s privacy. Self-positioning systems
also pose a privacy risk if they report the vehicle’s
295
Location Privacy in Automotive Telematics
GPS-derived location to a server through the
communications infrastructure.
pr IvAcy Att Acks
ACME Rent a Car Company
Most readers would be familiar with the highly
publicized abuse of GPS technology where
ACME charged its customers $150 for speeding
occurrences of more than 80mph. A customer
took ACME to court and won on grounds that
the company failed to clearly explain how the
location tracking system would be used (Ayres
and Nalebuff, 2001). This is an obvious scenario
of how personal information can be exploited. It
is not unreasonable to imagine that an ordinary
car trip can become an Orwellian ordeal when
one’s location information can be used in ways
not imagined.
Location-based spam
Figure 1 illustrates a possible threat scenario
where a vehicle is equipped with an on-board GPS
receiver and the vehicle periodically transmits its
location data to a tracking server. The tracking

server is connected to various service providers
which have been authorized by the driver to ac-
cess location data in order to provide telematics
services. The service providers are not necessarily
trusted and it is not unreasonable to expect loca-
tion information of individuals being sold on the
market (much like email address lists).
Proling Driving Behavior
Greaves and De Gruyter (2002) discuss how a
driving prole of a person can be derived from
GPS track data. They sought an understanding
of driving behaviors in real-world scenarios by
tting low-cost GPS receivers to vehicles, and
logging the vehicle movements. Consequently,
Figure 1. A typical privacy threat scenario

G PS S atellite
C om munications T ower
Adversa ry
Vehic le
T rackin g S erver
TS P
R eq ue s t
Location
P erio dically
rep ort loc ation
B uy or access vehic le
pos ition data
296
Location Privacy in Automotive Telematics

they were able to identify driving styles from
this data. Imagine a PAYD insurance provider
accessing this information, in order to identify an
individual with an ‘aggressive’ driving style.
electronic t oll collection
Electronic toll collection seeks to alleviate trafc
congestion at toll gates, and provides a convenient
method for drivers to pay tolls. Such schemes
typically require the car to have an electronic tag
attached to the front windscreen. Tolls are de-
ducted from the vehicle’s account when the scan-
ner senses the toll tag. Electronic toll can become
privacy invasive, for example, if the toll system
passes the entry and exit times of the vehicle to
law enforcement agencies giving them the ability
to issue speeding tickets if the distance is traveled
in too short a time (Langheinrich, 2005).
pr IvAcy defen ses
In the previous section location privacy threats
provoking some serious ambivalence about the
social and ethical telematics issues were discussed.
There are some countermeasures that can be
taken. The rst and most simple one would be an
opt-out approach. This would result in a denial
of service for the vehicle driver. The more chal-
lenging issue is how to preserve location privacy
while at the same time maximizing the benets
of telematics services.
Legislation and Regulation
Location privacy can be considered to be a special

case of information privacy. However, because
this area of the law is in its embryonic stages,
one can consider ‘location’ and ‘information’ as
being synonymous.
In the United States, legislation to protect
location information arises primarily from the
Telecommunications Act of 1996 and the 1998
E911 amendments. As a result, there is ambiguity
about the so-called “opt-in” or “opt-out” approach
for customer consent. However, a bill speci-
cally addressing location privacy, the Wireless
Location Privacy Protection Act of 2005, which
required location-based services (LBSs)to give
their informed consent for disclosure of location
information, was referred to the U.S. Senate
(Ackerman et al, 2003).
In Australia, the Privacy Act of 1988 (“Privacy
Act 1988 (Commonwealth)”, 2005) deals with con-
sumers’ privacy. Besides legislation, Standards
Australia has published a guideline suggesting
procedures for toll operators and electronic park-
ing operators to protect the personal privacy of
their customers (Standards-Australia, 2000) .
Japan and the European Union have well es-
tablished laws for protecting consumers’ location
privacy (Ackerman et al, 2003) .One issue that
should be emphasized is that legislation is not the
only defense against (location) privacy attacks.
The corporate world is very good at obscuring
questionable practices with ne print in a ser-

vice agreement or contract (Schilit et al, 2003).
Therefore there has to be enforcement of laws as
well as open audit of privacy practices.
Policy-Based and Rule-Based
protection
Privacy protection regulation concludes that “user
consent” is an essential requirement. If the growth
in telematics services proceeds as predicted, then
it would be difcult for a member of the public to
keep track of all details. Secondly, constant explicit
consent requirements can become a source of
driver distraction. Hence an analogy can be drawn
from the internet, where the Platform for Privacy
Preferences (P3P) is used to manage web server
privacy policies in Extensible Markup Language
(XML) machine readable format. Typically these
297
Location Privacy in Automotive Telematics
operate by comparing user prole rules of a web
client with the rules on a particular web server.
Such is the importance of location privacy that
there are already efforts to extend the P3P for
location rules (Morris, 2002). This means that
rules like “Allow Alice to access my location
on a weekday” can be created. Duri et al (2002)
proposed an end-to-end framework based on
a similar principle that provides both security
and privacy within the telematics environment.
There is one problem with these implementations,
since the policies serve as a mutual contract; the

driver has to trust the organization to abide by
the policies.
The Internet Engineering Task Force (IETF),
the standards body responsible for developing in-
ternet standards, has also realized the importance
of location privacy. It has proposed the Geographi-
cal Location/Privacy Charter, referred to simply as
geopriv. This standard seeks to preserve location
privacy of mobile internet hosts (IETF, 2006).
Synnes et al (2003) have implemented secure
location privacy, using a similar approach of using
rules to implement policies. In the near future, it is
not hard to imagine automobiles having Internet
Protocol (IP) addresses and ultimately using the
geopriv solution to implement privacy policies.
Identity and Location Decoupling
One conclusion that can be drawn is that the
vehicle can be uniquely identied when it com-
municates with a particular Telematics Service
Provider (TSP). Therefore, decoupling of identity
and vehicle location is essential at retention of
data. This can be regulated through policy, and
laws such as discussed above. Herborn et al (2005)
have studied this concept in pervasive comput-
ing networks. They argue that decoupling these
data from each other would have more benets.
Name ‘hijacking’ would simply not be possible.
The issue here is that for decoupling the identity
and other data to work, a robust scheme to resolve
naming would be required. This, however, is still

an open research issue.
Anonymous Access
Researchers in the eld of LBSs have looked at
anonymous solutions to location privacy. The
basic idea here is to access the services anony-
mously. Unfortunately, this cannot be regarded
as a complete solution given the inference ca-
pabilities of Geographical Information Systems
(GIS) and advanced surveillance techniques, as
discussed already (Gruteser and Hoh, 2005). An
adversary can apply data-matching techniques
to independent samples of anonymous data col-
lected, and map them on predictable paths such
as roads, and infer the identity of an individual
based on where one is.
Drawing from techniques used by census
boards and electoral commissions to obscure
data so that individuals are not identied, another
methodology similar to anonymous access has
been proposed. It is called “k-anonymous access”.
This means that when the location information of
a subject is requested, it will only be responded to
if there are k other nodes present in the vicinity
(Gruteser and Grunwald, 2003). This approach
can give good protection against privacy attacks if
the value of k is set to a high number, however this
would affect the quality of LBSs. In this approach,
k is a variable that could only be altered globally.
A second approach deals with k on a per node
basis. This means that each user can specify his

or her privacy variable (Gedik & Liu, 2005). This
approach appears to more realistically simulate
user privacy preferences in the real world.
Apart from being identied through map-
matching techniques, there is one additional
problem that can affect the correct operation of
telematics services using anonymous techniques.
Existing approaches discussed here are aimed
at solving the location privacy problem in the
298
Location Privacy in Automotive Telematics
context of LBSs. Telematics can be considered to
be a special case of LBSs, the authors, however,
argue that it needs a totally different mindset
for addressing privacy problems of the mobile
public mainly because of differences such as
higher average speeds, predictable paths, and the
magnanimity of the number of users.
Obfuscation
The term “obfuscation” means the process of
confusing or obscuring. It has been identied as
one possible approach to protect location privacy
in location-aware computing (Duckham and
Kulik, 2005). This deliberate degradation of loca-
tion information is performed by the individual,
through deciding which service would require
what ‘granularity’ of information, often referred
to as the “need to know principle”. Snekkenes
(2001) constructed rules for implementing privacy
policies using this principle. He emphasized

that different services require different resolu-
tions, or accuracy, of location information. The
advantage of obfuscation over anonymity is that
it allows authentication and customization of the
services. However, it still is not the ideal remedy
when high accuracy of reported location, instead
of deliberate degradation, is required.
Privacy Aware Designs
While defenses discussed above propose measures
for limiting disclosure of location information,
others have sought to understand privacy aware
designs. The success of future LBSs depends on
designing systems with privacy in mind, not just
it being an “afterthought”. Langheinrich (2005)
discuss the need for anonymous location infra-
structures and transparency protocols allowing
customers to understand and track how their data
is collected and used. Kobsa and Telztrow (2006)
argue that clearly explaining privacy policies at
subscription would encourage users to disclose
information and create a sense of trust. They
conducted experiments to prove this comparing
their privacy friendly systems to the traditional
data collection systems.
Other examples of privacy aware designs
include work by Coroama and Langheinrich
(2006) where they implemented a GPS based
PAYD insurance system depicting real-time
risk assessment of actual road conditions. Their
system calculates premiums on board the vehicle

guaranteeing privacy of owners. There is periodic
transmission of aggregated information to the in-
surance provider for bill generation. Iqbal and Lim
(2006) extended this idea further and proposed a
GPS-based insurance product that preserves loca-
tion privacy by computing distances traveled on
the onboard unit. They additionally safeguarded
“spend privacy” by proposing smart card based
anonymous payment systems. Their approach was
to redesign a closed system curtailing redundant
exchange of location data.
conc Lus Ion
Location privacy protection in telematics is in-
deed a social issue. The authors have reviewed
in this short article location privacy threats and
possible countermeasures. Each countermeasure
to protect privacy has its own implications, and
it is clear that no general panacea exists. This
suggests that a combination of several different
approaches may be the best solution.
The reader might feel that the authors have
taken a pessimistic view of privacy issues. It is
acknowledged that location disclosure would be
necessary in life threatening scenarios, or where
law enforcement ofcials need access to this in-
formation. This critical information, however, like
other worthwhile liberties needs to be protected by
law. Under normal circumstances, only the loca-
tion information subject has the right to exercise
control of one’s personal information.

299
Location Privacy in Automotive Telematics
Development in telematics is through a coop-
eration of companies that are involved in transport
management, vehicle manufacture or information
technology services. The current approach recog-
nizes privacy to be a “non-technical barrier” to
the implementation of ITS (US-Department-of-
Transportation, 1997). Since research in transport
telematics is in its nascent stages, it is important
to understand that these issues are not merely
social hindrances. Once such scenarios become
commonplace, the general user may be reluctant
to use these telematics services at all. Therefore,
it is important to dispel these privacy concerns
right from the beginning, and focus on “building
in” privacy protection within such systems so that
as new applications become available, appropriate
privacy measures are integral to them.
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key ter Ms
Context Aware Computing: The process of
customization of software and services to user
preferences. The computing mechanism changes
based on the context, in telematics perspective,
location is a context for customization.
Electronic Tolls: Electronic payment systems
designed to identify an electronic tag mounted on
a vehicle to deduct the toll charges electronically
from the vehicle owner’s account.
In-Car Navigation: Usually a voice-activated
system with a liquid crystal display (LCD) screen
displaying maps and a combination of on-board
GPS receivers, accelerometers, compass and gy-
roscopes for positioning the vehicle on the map.
Intelligent Transportation Systems: Tools,
software, hardware and services designed for the
efcient movement of road transportation and

provision of travel information to the vehicles.
Location Privacy: Location privacy is the
ability of an individual to control access to his/her
current and past location information.