A.2 – Geographic Information Systems for Transportation (GIS-T)

Authors: Dr. Shih-Lung Shaw and Dr. Jean-Paul Rodrigue

Geographic Information Systems for Transportation (GIS-T) refer to the principles and applications of geographic information technologies to transportation problems.

1. GIS in Transportation

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. They have transformed and expanded geography through their ability to store large amounts of data, analyze it, and particularly by depicting customized cartographic outputs. Among the wide range of potential applications GIS can be used for, transportation issues have received much attention since they are simultaneously highly dependent on visualization and analytical methods. 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 and Transportation
GIS Data Models

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 three categories:

  • Data representations. How various components of transport systems are represented as a database, which involves the network as well as technical and operational characteristics (capacity, speed).
  • Analysis and modeling. How transport methodologies can be used to represent real-world transportation activities.
  • Applications. What types of applications are particularly suitable for the data and analytical capabilities of GIS-T.

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:

  • 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).

GIS-T studies have employed both object-based and field-based data models to represent 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. Therefore, the object-based GIS data model is a better candidate for such transportation applications. Other transportation data types 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 with 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 concerning 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 is another type of data that is 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 has developed additional file formats and functions for users to work with matrix data in a GIS environment. Conventional GIS approaches can thus be further extended and enhanced to meet the needs of transportation applications. The creation and expansion of add-ons for GIS software represent how specific methods and models can be implemented in existing packages.

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 or a large consulting firm) 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 need to integrate 1-D, 2-D, 3-D, and temporal data in support of various transportation applications also called for implementing multidimensional (including spatiotemporal) data representations. The development of these systems has also been facilitated by cloud computing applications allowing for the storage and sharing of large databases among a large number of users at different locations.

Modern information and communication technologies (ICT) have changed how people and businesses conduct their activities. These changing activity and interaction patterns, in turn, lead to changing traffic patterns. The world has become more mobile and dynamic due to modern ICT. With the advancements in location-aware technologies (e.g., Global Positioning System, mobile phone tracking system, RFID, and Wi-Fi positioning system), collecting large volumes of tracking data at the individual level is feasible and affordable. Consequently, how to best represent and manage dynamic data of moving objects (passengers, vehicles, or shipments) in a GIS environment presents new research challenges to GIS-T. Big Data allows new opportunities for automatically collecting large amounts of data by various sensors.

In short, one critical component of GIS-T is how transportation-related data in a GIS environment can be best represented to facilitate and integrate the needs of various transportation applications. Existing GIS data models provide a good foundation for 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 can be done with different types of transportation studies.

3. GIS-T Analysis and Modeling

GIS-T applications have benefited from many 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 salesperson problems, vehicle routing problems).
  • 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 activity-based travel demand models).
  • 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 includes both spatial and temporal elements, the development of spatiotemporal GIS analysis functions to help better understand the dynamic movement and routing patterns has attracted significant research attention.

4. GIS-T applications

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:

  • Infrastructure planning and management
  • Transportation safety analysis.
  • Travel demand analysis.
  • Traffic monitoring and control.
  • Public transit planning and operations.
  • Economic and environmental impacts assessment.
  • Routing and scheduling.
  • Vehicle tracking and dispatching.
  • Fleet management.
  • Site selection and service area analysis.
  • Supply chain management.

Each of these applications tends to have specific data and analysis requirements. For example, representing a street network as centerlines may be sufficient for transportation planning and vehicle routing applications. On the other hand, a traffic engineering application may require a detailed representation of individual traffic lanes, sidewalks, and even the curvature of routes. Turn movements at intersections also could be critical to a traffic engineering study, but not to a regional travel demand study.

These different application needs are directly relevant to the GIS-T data representation, 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, having a GIS-T data model that allows multiple geometric representations of the same transportation network is desirable. Research on enterprise and multidimensional GIS-T data models aims to address these important issues of better data representations supporting various transportation applications.

With the rapid growth of the Internet and wireless communications, a 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, like portable devices, and dominantly as built-in smartphone applications. 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 locations), 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 that are widely used in e-commerce.

GIS-T is interdisciplinary in nature and has many possible applications addressing real-world problems.

Related Topics

Bibliography

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