Written by Val Noronha, Digital Geographic Research Corporation, Mississauga Ontario, Canada (noronha@dgrc.ca)


Transportation data are used in a widening variety of applications, e.g. to “spot” customers in marketing studies, ttrack emergency vehicles and to optimize delivery routes.  Recently it has become possible to monitor traffic volumes and speeds on highways, and to pass advisory messages to dashboard computers in vehicles.  This emerging technology is Intelligent Transportation Systems (ITS), formerly Intelligent Vehicle/Highway Systems (IVHS).

It is useful to think of five components of transportation data:

  1. the coordinates of the street centerline (a polyline);
  2. the name or label of the centerline, e.g. “Erin Mills Parkway
  3. address ranges on either side of the road;
  4. topological connectivities — whether adjacent links are accessible
  5. attributes (e.g. pavement quality, speed limit, traffic volume;

data on landmarks and facilities such as restaurants are increasingly stored as attributes in conjunction with street network data). With these data, one can:

  1. construct a map on screen or hardcopy from a digital database;
  2. using the street name, orient oneself on the map;
  3. geocode an address, i.e. determine the coordinates of “421 Erin Mills Parkway;
  4. determine an optimal route — shortest, quickest, scenic, easiest
  5. perform advanced analyses, e.g. schedule road maintenance, optimize the use of a vehicle fleet to pick up and deliver goods, model the consequences of closing a major traffic artery at rush hour, etc.

The availability and quality of data are critical to the success of an application.  When the required data are not available, surrogate data are often used, e.g. one might use drive distance rather than drive time to compute the quickest route between points.  It is important to understand the limitations of the analyses when data are substituted in this way.

To acquire data appropriate to a transportation application, the student must first understand the different types of data used, then survey the market critically to find the product most appropriate to his needs.

Example Application

A medical emergency is called in to a 911 center.  Emergency personnel need ... There are two components to the problem:
  1. to geocode the incident location
  2. to compute shortest routes
Accordingly, two kinds of data are required: (1) street names, address ranges and landmarks, and (2) drive-distance or drive time attributes,and topology.  The example is analogous to home delivery for a department store chain or pizza outlet, or a car pooling problem.

The GIS technician will identify and evaluate possible sources of data, taking into consideration origin and quality of the data, and impact on results.  He will perform the address matching and route optimization operations as well, but the technical details of these are beyond the scope of this unit.

Learning Outcomes

The following list describes the expected skills which students should master for each level of training, i.e. Awareness/Competency/Mastery.


To learn about potential applications of transportation data, and the appropriate data types and typical sources for each class of application.


To learn about acquisition and processing of data, by means of a practical exercise.


To assess data quality, and to anticipate limitations on accuracy of output; to appreciate cost and benefit issues.

Preparatory Units

  1. Unit 7 - Using and interpreting metadata
  2. Unit 8 - Error checking
  3. Unit 9 - Converting digital spatial data between formats, systems and software
  1. Unit 46 - Address matching


Learning Objectives:
  1. Student understands essential concepts and vocabulary of street networks.
  2. Student broadly understands how basic network operations (map construction, address matching, shortest path calculation) are performed by commercial GIS, what data are used and how they are processed.
  3. Student can differentiate between data types required to perform the various operations in (2).
  4. Student can list most common sources of data, and characteristics of each source, with particular emphasis on method of data gathering, and quality.
Vocabulary Concepts Data Sources

Street network data are available in varying degrees of quality and completeness.

The following table is necessarily skeletal and incomplete.  The inclusion of a vendor name does not constitute endorsement of data quality or the firm's reputation; similarly, omission of a name should not be construed as disapproval of a firm or its product.  Resellers of unmodified government data are not cited, neither are those who exclusively offer data packaged with software for the mass market (e.g. Rand McNally's Street Atlas USA).
Government (centerline)
Government (Multipurpose Street Network)
  • State mapping agencies
  • Municipalities
  • Victoria: Land Victoria
  • P-Data? (Sydney)
  • UBD (Sydney)
  • Melway (Melbourne)
  • Canada
  • Natural Resources Canada
  • Provincial mapping agencies
  • Municipalities
  • Street Network File (Statistics Canada)
  • Compusearch (Toronto)
  • Desktop Mapping Technologies (Markham ON)
  • USA
  • DLG (USGS)
  • National Highway Planning Network
  • State mapping agencies
  • Municipalities
  • TIGER (US Census)
  • Etak (Menlo Park CA)
  • GDT (Lebanon NH)
  • Navigation Technologies (Sunnyvale CA)
  • Thomas Brothers (Irvine CA)

    Example Implementation

    Figure 1 shows a typical centerline street network for a portion of Santa Barbara, California, USA, as represented by two commercial data vendors.  There are obvious visual differences.  Clearly Vendor 2 has taken far greater trouble to represent the dual-carriageway freeway and the exact geometry of the exit ramps; and each vendor has included roads that the other has not (usually driveways in semi-private buildings).  However — and this is not evident from the illustration — database 2 is not topologically structured, and street names are stored in a haphazard format, e.g. the main east-west artery is labelled “Hollister Av” in some places, and simply “holister” in others.  Such data, althhough positionally accurate, cannot be used for geocoding or routing.

    Two vendors' version of the same area
    Also not apparent from the illustration is the fact that Vendor 1 shows intersections between the freeway and the arterial road, although in reality the freeway overpasses the artery.  This is because the vendor's data structure cannot distinguish between planar (at-grade) and non-planar intersections.  Some vendors may use turn tables to store such data.

    In sum, although Vendor 1 leaves much to be desired in positional terms, the data can be used for topological applications such as address matching and routing.  Vendor 2 is good only for graphic applications such as map display.


    Learning Objectives:
    1. Student compiles detailed list of data requirements, including quality requirements, for a given application.
    2. Student surveys market for data sources, and selects a vendor.
    3. Student prepares data in format required for the application.
    Vocabulary Concepts Tasks

    For a given application, student compiles detailed list of data requirements, particularly attribute requirements.

    Example implementation

    To calculate drive time database: develop travel time attributes based on (a) zonal assignment of impedance values, (b) field notes and local knowledge, (c) vendor data if available, (d) TMC data if available.  Feed the data into a system to perform optimal routing, and for one or two origin-destination pairs, compare the system recommendations against real drive time measured in a vehicle.


    Learning Objectives:
    1. Quality issues
    2. Cost and benefit issues
    Vocabulary Concepts
    Trace the history of street network data available for a selected area, examining the parties that created it, and their needs, compared with current and emerging needs.

    Follow-up Units

    1. Unit 30 - Validating databases
    2. Unit 31 - Managing database files


    Data, documentation, and commentary.

    TIGER — U.S. Censu Bureau
    ITS Online — ITS new

    Examples of network-based applications.

    Mapmaker for U.S. street addresses

    Real time freeway data

    Los Angeles, California
    Toronto, Ontario, Canada
    Paris, France

    Back To Core Curriculum for Technical Programs Welcome Page

    Currently maintained by Steve Palladino
    Created: May 14, 1997. Last updated: October 5, 1998.
    Content comments to Val Noronha
    Formatting comments to Steve Palladino