There are several potential types of data that can be used for traffic forecasting, such as traffic counts, land use, demographic data, and public transportation data. It is important to identify and document the data types and determine whether the data is available and accessible for the project. Data types available for traffic forecasting include:
- Traffic count data: This data provides information about the volume of vehicles on a given road segment or at an intersection, which should be collected during typical weekdays, excluding weeks that contain holidays. Typical traffic count data collection includes turning movement counts for peak periods, approach/departure vehicle volume counts, and classification counts as needed. The duration of data collection should meet FDOT requirements depending on the project type. The FDOT Manual on Uniform Traffic Studies (MUTS) provides further guidance on field data collection.
- Land use data: This data includes information on the current and planned land uses in the area being analyzed, such as residential, commercial, and industrial developments. Land use data can be used to understand how future changes in land use may impact traffic patterns. Land use data can be obtained from FDOT and local municipalities.
- Demographic data: This data provides information on population, employment, income, education, and other demographic characteristics of the area being analyzed. Demographic data can be used to understand how population and employment changes may impact traffic patterns and to estimate future transportation demand. Demographic data can be obtained from the Bureau of Economic and Business Research (BEBR) of the University of Florida and US Census Bureau.
- Public transportation data: This data includes information on transit ridership, such as passenger counts, route schedules, and service frequencies, which can be obtained from the transit agency official website.
The availability of these data types may vary depending on the location and scope of the traffic forecasting, as well as the resources available to the project team. It is important to carefully evaluate the quality, relevance, and availability of each data type before using it for traffic forecasting. There are several data types that may be needed for traffic forecasting, depending on the specific scope of the project. Here are some of the most common data types and sources:
- Historical traffic data: This data includes information on traffic volumes, speeds, and congestion levels for a specific period in the past. Historical traffic data can be used to identify trends and patterns in traffic behavior, and evaluate forecasting. This data can be obtained from the FTO website and Regional Integrated Transportation Information System (RITIS).
- Transportation project data: This data provides information on planned and ongoing transportation projects, such as new roadways, transit expansions, bicycle/pedestrian infrastructure, interchanges, and the timing of the future transportation improvements. Transportation project data can be used to understand how changes in the transportation network may impact traffic patterns. The potential sources for this data may include the appropriate travel demand model, LRTP, and FDOT Five-Year Work Program.
- Origin-Destination (O-D) data: This data shows information for understanding vehicular movement patterns within a transportation network, including trip origins and destinations, travel times, and route choices. O-D datasets may be needed for complex traffic forecasting projects to accurately model travel behavior. When scoping for traffic forecasting that requires O-D data, it is important to define the scope and acceptable methods of data collection. This involves discussing the data collection approach with FDOT Coordinator and obtaining their approval for the methods used. Collecting O-D data can be a challenging and resourceintensive process, as it typically requires significant investment in data collection technologies and infrastructure, as well as planning and execution to ensure that data quality and reliability meet the needs of the forecasting project. As such, it is important to consider the cost-benefit tradeoffs of collecting O-D data for a given project and to prioritize its collection based on the specific needs and objectives of the project.
By using these data types, traffic forecasting can be developed that reasonably predicts future traffic patterns. It is important to understand the various types of data that are available and needed for traffic forecasting. The user should recognize the critical role that data plays in traffic forecasting and the importance of taking a thoughtful and thorough approach to identifying the necessary data types. It is worth noting that these are general guidelines, and the data needs and applicable projects may vary depending on the specific project purpose and need, as well as the context in which the project traffic data is being used.