Jean-Paul Rodrigue (2017), New York:
Routledge, 440 pages.
Geographic Information Systems for Transportation (GIS-T)
Authors: Dr. Shih-Lung Shaw and Dr. Jean-Paul Rodrigue
In a broad sense a geographic information system (GIS) is an information
system specializing in the input, management, analysis and reporting
of geographical (spatially related) information. Among the wide range
of potential applications GIS can be used for, transportation issues
have received a lot of attention. A specific branch of
GIS applied to transportation issues, commonly
labeled as GIS-T, is one of the pioneer GIS application areas.
Geographic Information Systems for Transportation (GIS-T)
refers to the principles and applications of applying geographic
information technologies to transportation problems.
GIS-T research can be approached from two different, but complementary,
directions. While some GIS-T research focuses on issues of how GIS can
be further developed and enhanced in order to meet the needs of transportation
applications, other GIS-T research investigates the questions of how
GIS can be used to facilitate and improve transportation studies. In general, topics related to GIS-T studies can be grouped into
2. GIS-T Data Representations
Data representation is a core research topic of GIS. Before a GIS
can be used to tackle real world problems, data must be properly
represented in a digital computing environment. One unique characteristic
of GIS is the capability of integrating spatial and non-spatial data
in order to support both display and analysis needs. There have been
various data models developed for GIS. The
two basic approaches are object-based
data models and field-based data models:
- Data representations. How can various components of transport
systems be represented in a GIS-T?
- Analysis and modeling. How can transport methodologies
be used in a GIS-T?
- Applications. What types of applications are particularly
suitable for GIS-T?
GIS-T studies have employed both object-based and field-based data
models to represent the relevant geographic data. Some transportation
problems tend to fit better with one type of GIS data model than the
other. For example, network analysis based on the graph theory
typically represents a network as a set of nodes interconnected with
a set of links. The object-based GIS data model therefore is a better
candidate for such transportation applications. Other types of transportation
data exist which require extensions to the general GIS data models.
One well-known example is linear referencing data (e.g. highway
mileposts). Transportation agencies often measure locations of features
or events along transportation network links (e.g. a traffic accident
occurred at the 52.3 milepost on a specific highway). Such a one-dimensional
linear referencing system (i.e. linear measurements along a highway
segment with respect to a pre-specified starting point of the highway
segment) cannot be properly handled by the two-dimensional Cartesian
coordinate system used in most GIS data models. Consequently, the dynamic
segmentation data model was developed to address the specific need of
the GIS-T community.
Origin-destination (O-D) flow data are another
type of data that are frequently used in transportation studies. Such
data have been traditionally represented in matrix forms, a
two-dimensional array, for analysis. Unfortunately,
the relational data model widely adopted in most commercial GIS software
does not provide adequate support for handling matrix data. Some GIS-T
software have developed additional file formats and
functions for users to work with matrix data in a GIS environment. Conventional GIS approaches can
further extended and enhanced to meet the needs of transportation applications.
The creation and expansion of add-ons for GIS software represents a way
that specific methods and models can be implemented in existing
Developments of enterprise and multidimensional GIS-T data models
also have received significant attention. Successful GIS
deployments at the enterprise level (e.g., within a state department
of transportation) demand additional considerations to embrace the diversity
of application and data requirements. An enterprise GIS-T data model
is designed to allow "each application group to meet the established
needs while enabling the enterprise to integrate and share data". The
needs of integrating 1-D, 2-D, 3-D, and temporal data in support of
various transportation applications also have called for the implementation
of multidimensional (including spatio-temporal) data representations.
Modern information and communication technologies (ICT) such as
the Internet and cellular phones have changed the ways that people
and businesses conduct their activities. These changing activity and
interaction patterns in turn lead to changing spatio-temporal
traffic patterns. The world has become more mobile and dynamic due to modern ICT. With the advancements of
location-aware technologies (e.g., Global Positioning System,
cellular phone tracking system, and Wi-Fi positioning system), it is
now feasible and affordable to collect large volumes of tracking
data at the individual level. Consequently, how to best represent
and manage dynamic data of moving objects (people, vehicles, or
shipments) in a GIS environment presents new research challenges to
GIS-T, especially when we have to deal with the Big Data issues.
In short, one critical component of GIS-T is how transportation-related
data in a GIS environment can be best represented in order to facilitate
and integrate the needs of various transportation applications.
Existing GIS data models provide a good foundation of supporting many
GIS-T applications. However, due to some unique characteristics of transportation
data and application needs, many challenges still exist to develop better
GIS data models that will improve rather than limit what we can do with
different types of transportation studies.
3. GIS-T Analysis and Modeling
GIS-T applications have benefited from many of the standard GIS functions
(query, geocoding, buffer, overlay, etc.) to support data management,
analysis, and visualization needs. Like many other fields, transportation
has developed its own unique analysis methods and models. Examples
include shortest path and routing algorithms (e.g. traveling salesman
problems, vehicle routing problem), spatial interaction models (e.g.
gravity model), network flow problems (e.g. minimum cost flow problem,
maximum flow problem, network flow equilibrium models), facility location
problems (e.g. p-median problem, set covering problem, maximal covering
problem, p-centers problem), travel demand models (e.g. the four-step
trip generation, trip distribution, modal split, traffic assignment
models, and more recent activity-based travel demand models), and land use-transportation interaction models.
While the basic transportation analysis procedures (e.g. shortest
path finding) can be found in most commercial GIS software, other transportation
analysis procedures and models (e.g. travel demand models) are
available only selectively in some commercial software packages. Fortunately,
the component GIS design approach adopted by GIS software companies
provides a better environment for experienced GIS-T users to develop
their own custom analysis procedures and models.
It is essential for both GIS-T practitioners and researchers to have
a thorough understanding of transportation analysis methods and models.
For GIS-T practitioners, such knowledge can help them evaluate different
GIS software products and choose the one that best meets their needs.
It also can help them select appropriate analysis functions available
in a GIS package and properly interpret the analysis results. GIS-T
researchers, on the other hand, can apply their knowledge to help improve
the design and analysis capabilities of GIS-T. Due to the increasing
availability of tracking data that include both spatial and temporal
elements, development of spatio-temporal GIS analysis functions to
help better understand the dynamic movement patterns has attracted significant research attention in recent
GIS-T is one of the leading GIS application fields. Many GIS-T applications
have been implemented at various transportation agencies and private
firms. They cover much of the broad scope of transportation and logistics,
such as infrastructure planning and management, transportation safety
analysis, travel demand analysis, traffic monitoring and control, public
transit planning and operations, environmental impacts assessment, intelligent
transportation systems (ITS), routing and scheduling, vehicle tracking
and dispatching, fleet management, site selection and service area analysis,
and supply chain management. Each of these applications tends to have
its specific data and analysis requirements. For example, representing
a street network as centerlines may be sufficient for transportation
planning and vehicle routing applications. A traffic engineering application,
on the other hand, may require a detailed representation of individual
traffic lanes. Turn movements at intersections also could be critical
to a traffic engineering study, but not to a regional travel demand
These different application needs are directly relevant to the
GIS-T data representation and the GIS-T analysis and modeling issues. When a need arises to represent transportation networks
of a study area at different scales, what would be an appropriate GIS-T
design that could support the analysis and modeling needs of various
applications? In this case, it is desirable to have a GIS-T data
model that allows multiple geometric representations of the same transportation
network. Research on enterprise and
multidimensional GIS-T data models aims at addressing
these important issues of better data representations in support of
various transportation applications.
With the rapid growth of the Internet and wireless communications
in recent years, a growing number of Internet-based and wireless GIS-T
applications can be found, particularly for driving directions which
is the most common commercial use. Global positioning system (GPS)
navigation systems are available as built-in devices in vehicles, as portable devices,
and dominantly as built in applications in smartphones. Coupled with wireless communications, these
devices can offer real-time traffic information and provide helpful
location-based services (LBS). Another trend observed in recent
years is the growing number of GIS-T applications in the private sector,
particularly for logistics applications.
Since many businesses involve operations at geographically dispersed
locations (e.g., supplier sites, distribution centers, retail
stores, and customer location), GIS-T can be a useful tool for a variety
of logistics applications. Many of these logistics applications
are based on the GIS-T analysis and modeling procedures such as the
routing and facility location problems.
GIS-T is interdisciplinary in nature and has many possible applications.
Transportation geographers, who have appropriate backgrounds in both
geography and transportation, are well positioned to pursue GIS-T studies
and assist in GIS-T implementations addressing real-world problems.
- An object-based data model treats geographic space as populated
by discrete and identifiable objects. Features are often
represented as points, lines, and/or polygons.
- On the other hand, a field-based data model treats geographic
space as populated by real-world features that vary continuously
over space. Features can be represented as regular tessellations
(e.g., a raster grid) or irregular tessellations (e.g., triangulated
irregular network - TIN).