Urban Integrated National Transit Database
Introduction
The National Transit Database (NTD) is important source of data for transit agencies, especially for benchmarking performance and planning service. While most of these data are publicly available, they are cumbersome to access, especially when comparing multiple agencies or years. The Florida Transit Information System (FTIS) makes it easy to examine NTD information with a free web-based tool called the Urban Integrated National Transit Database (Urban iNTD). Developed by Florida International University’s Lehman Center for Transportation Research for the Florida Department of Transportation, this tool gives transit planners an increased ability to both retrieve and analyze NTD data.
How it Works
Transit agencies that receive funds from the Federal Transit Administration (FTA) are required to report operating data to the NTD. About 850 agencies are included in the database.[1] This information is technically public, but is presented as a series of excel files which require knowledge of the data schema to use and experience with Microsoft Excel in order to analyze. iNTD brings all of this information into a central system. The data can be sorted by agency, year, and individual NTD forms.
Peer Comparisons
One of the most useful features of iNTD is the ability to easily compare an agency to its peers. Peer comparison is useful for benchmarking performance against comparable agencies and for understanding how agencies with different funding structures, riderships, or other factors operate.
The first step in the comparison process is to establish an agency's peer group. After inputting an agency, iNTD will automatically identify potential matches and sort them by how comparable they are. A user can then manually trim down this list to the desired peer agencies. The comparison can be filtered to look at entire agencies or specific modes (bus, rail, etc.) and can distinguish between directly operated and purchased service. This step also allows the user to select what years to use in the analysis. NTD data is published in the fall for the preceding year, so the most recent years may not be available.
Once the peer group is established, the next step is to select performance measures. NTD measures can be selected individually, but iNTD also allows for the selection of predefined variable groups. Once the desires variables have been selected, they can be saved as a new group for future use.
After the peer group and performance measures have been specified, iNTD will retrieve the data. There are multiple ways that the data can be presented. It is possible to look at the actual NTD forms, though this is the least clear way of exploring the data. To get a quick snapshot of the information, the tool can create summary reports looking at mode data for either a single agency across all selected years or all selected agencies for a single year.
For more in-depth analysis, iNTD can create a table containing all the selected data. iNTD provides tools within the app that lets users sort the data, adjust monetary values for inflation, make charts, and perform summations and regressions. The table can also be exported as an Excel file for further work.
Case Studies
San Jose, CA
When looking to assess the efficacy of its light rail maintenance program, the Santa Clara Valley Transportation Authority (VTA) turned in part to iNTD. The tool helped the agency establish a peer group, which it then edited to account for VTA’s specific vehicle type. VTA also used iNTD to retrieve data for variables such as fleet age, miles of track, and maintenance costs. The analysis shows that, even after accounting for high labor costs in the San Francisco Bay Area, VTA has very high maintenance costs compared to its peers. The data also showed that VTA has an unusually high spare ratio, which could drive some of these costs.
Knoxville, TN
Most of the funding for Knoxville Area Transit comes from the city’s general fund. The agency was curious to see how its performance compared to agencies that are similar, but have a dedicated funding source. It selected a peer group and used NTD data to eliminate agencies without dedicated funding. It then found data for variables like operating expense per capita, boardings per revenue hour, and percentage of operating costs that are subsidized. It discovered while the region’s investment in transit is relatively low, the agency’s service compares well to its peers.
Urban Integrated National Transit Database
References
Additional Reading
- This report contains both illustrated instructions for using iNTD and a more in-depth framework for measuring performance and comparing it to peer agencies. Note: iNTD was previously called INTDAS, which is what the report refers to it as. The instructions are still relevant.