Economic Impact Case Study Tool for Transit (2016)

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17 C H A P T E R 3 As with the case selection and development process, the web tool through which the seven transit cases studies can be accessed is based on the TPICS for Highways web tool that was developed for SHRP 2. The effort to develop a parallel web tool for transit cases allowed exploration of interface changes necessary to appropriately present transit cases. Dur- ing development of the web tool for transit cases, the project team was also able to identify a number of improvements that could also benefit the previously completed highway projects. It should also be noted that the original highway projects are now also presented in AASHTO’s EconWorks format, which houses the same data as the original TPICS database, but uses a different format and visual presentation style. 3.1 Case Search Screening and Selection The original TPICS for Highways introduced the concept of classifying projects in terms of five major dimensions: (1) project type, (2) region of the United States, (3) motiva- tion, (4) urban/class level, and (5) economic setting. Users check boxes to narrow their case study search to only those that fall into certain categories. The original TPICS also included a set of “other criteria” to further restrict options. These search criteria were designed to allow users to review a large database to focus on projects that are of interest, whether due to similarity to a project they are planning themselves, or in response to a research question, and so forth. These search criteria emphasize some of the most important categorical variables for grouping projects and impact regions. The new TPICS for Transit follows this same general frame- work, but adjusts the dimensions to maintain greater relevance for transit projects. The basic search criteria available in TPICS for Transit can be seen in Figure 2. One major difference between highway and transit interfaces for TPICS was the need to add a transit mode search criteria and to provide different options for project type, only maintaining the passenger inter- modal option in the pilot project interface. The current seven cases, without the intermodal passenger cases completed for the original TPICS work, only include the first three transit project types and light rail, heavy rail, and BRT modes. Because of the limited number of cases in the Transit data- base to date (only the seven pilot cases) the search function is not yet of much use. With sufficient expansion of the data- base, however, a planner in the Midwest who is considering a new BRT line to the suburbs will be able to read narratives and access project data tables for several similar projects in their region. The other similarities and differences are shown in Table 9. The primary differences in search categories for transit (as com- pared with the categories for highways) are 1. Motivation—for transit, identify areas motivated by air quality attainment status (rather than delivery markets, international boarder access, and marine port access for highways); 2. Urban/Class Level—for transit, focus on urban core versus suburban (rather than metro versus non-metro counties); and 3. Economic Distress—for transit, keep the same distressed/ not distressed split but base it on urban area (rather than county-level data). By default, all characteristics will be selected across the categories. To narrow the search, deselect irrelevant char- acteristics or hit “Deselect All,” and then select relevant characteristics. Figure 3 shows the Instructions box, which appears to the left of the search categories in the web interface and also shows the current number of matches based on selected characteris- tics. This counter helps a user determine whether their crite- ria are too narrow or too expansive to provide an adequate, Web Tool Development

18 Clicking on the blue hyperlinked title opens an additional window with each case study’s details (see Figure 4). The case study information is organized in six tabs for TPICS for Tran- sit. From left to right, they are characteristics, setting, narrative, impacts, pre/post conditions, and images. These six sections contain the information that was collected for each case, as described in Section 2.4, and is organized into data fields, tables, and the narrative. 3.2 Case Study Results Reporting This section discusses the general contents of the case study reports and where information can be found. Specific differ- ences between the reports in TPICS for Transit compared with TPICS for Highways as well as the additions needed for rep- resenting transit cases are again highlighted. Many of these changes were first mentioned in Chapter 2. In this section, we can see how they were implemented in the pilot web tool. The images in this section contain data from the NoMa–Gallaudet Red Line Station case study. The first tab is Characteristics, shown in Figure 5. The data presented provides characteristics of the project such as planned and actual cost, construction period, project type and length, but manageable, list of projects. Clicking the “View Results” button will bring up a list of projects as seen in Figure 4. To support quick comparisons among search results, each entry in this list provides a brief description of the project studied, the project type, the state and region, the project cost in constant dollars, and the date at which project construction ended. Figure 2. TPICS for Transit search criteria. Setting Indicator Existing Highway TPICS New Transit TPICS Region 5 Regions plus International <same> Motivation 9 Categories: Air Access, Rail Access, Labor Market, Delivery Market, International Border Access, Marine Port Access, Site Development, Congestion Mitigation, and Tourism 7 Categories: Adding Air Quality Attainment Area and removing Delivery Market, International Border Access, and Marine Port Access. Urban/Class Level 3 Categories: Metro, Rural (Non-Metro), Mixed 2 Categories: Core City, Suburban (Rural may be added in the future) Economic Setting Economic Distressed Area* (based on County) Economic Distress Area* (based on Zipcodes/Municipality) * Economic distress is defined as having an unemployment ratio more than 1.2 times the national rate. Table 9. Location categories for case study selection. Figure 3. Instructions and match view selection.

Figure 4. List of selected cases.

20 After providing basic project information and settings, the TPICS system provides a Narrative—a discussion of the project and its impacts. The narrative allows case studies to capture a variety of factors that are important to each project’s success or that posed challenges. Transportation projects occur in so many different contexts and forms that it is not possible to represent all of the information in data tables that can be captured in a well-constructed narrative based on multiple interviewees with local contacts and a synthesis of project data. The format for the case study narratives in TPICS for Tran- sit follows that of the original TPICS for Highways, with the following sections: 1. Synopsis 2. Background 2.1 Location and Transportation Connections 2.2 Community Character and Project Context average ridership, and multiple location details. It is also shown that the Impact area is not a county designation. Following the characteristics report, the Settings tab (see Figure 6) provides additional detail on the socio-economic and market context of the project. We can see that in the initial year of construction, unemployment in Washington, D.C., was 1.42 times the national average. The NoMa–Gallau- det project is located in the urban core of the D.C. metro area, where population density is quite high and the total labor market population is nearly 5 million. The “Urban Core” designation differs from the highway class levels, under which the NoMa project would have simply been classified as “Metro.” Transit cases are predominantly in metro areas, so an urban core/suburban designation is much more useful. In the 5 years preceding the start of construction in 2002, the settings report shows that D.C. was losing population, but maintained employment growth. * values correspond to the Post-Construction Study Date Figure 5. The project characteristics report. * values >1.2 pronounced local unemployment compared to U.S. average ** compound annual growth rate for the pre-project interval Figure 6. The setting report.

21 affected local transportation and how those changes led to gross and net changes in area population, economic activ- ity, and land-use development. Section 4.2 provides detailed information on the transportation impacts identified and any development associated with the project. Section 5 iden- tifies other (non-transportation) factors that served either to enhance or to reduce the amount of economic and land development occurring around the project. This information also serves to refine the attribution of credit that affects the net impact estimate. The next tab, Impacts (see Figure 8), is a table showing the estimated net direct impact of the project on various measures of economic activity and land development occurring in the study area—information that is discussed more fully in Sec- tions 4 and 5 of the Narrative tab. The Impacts report only shows the net effects that are attributable to the transportation improvement; further information on gross changes in devel- opment around the project is usually provided in the narrative. It is important to note that the impact study area selected for these seven pilot case studies was the neighborhood level—an 3. Project Description and Motives 4. Project Impacts 4.1 Transportation Impacts 4.2 Demographic, Economic, and Land Use Impacts 5. Non-Transportation Factors 6. Resources 6.1 Citations 6.2 Interviews An example of a typical narrative (as an excerpt, including the first part only) is shown in Figure 7. The Synopsis provides a more robust summary than is pro- vided at the outset (in the Case Search) on the search results listing. In Section 2 introduces a reader to project location background if they are unfamiliar with the area’s transpor- tation services or general socio-economic characteristics. Section 3 corresponds with the project motivation search criteria that were used to screen cases and provides much more specific detail on why local actors advocated for the improvement. Section 4 explains how the improvement Figure 7. The case narrative.

22 are as close as possible to the pre-project and post-project study years, although they are not always exactly the same as those years. For the seven case studies, we used consistent data sources when available to provide comparable data across projects. This information can be used to portray trends at different spatial levels, which provide a context for viewing changes occurring at the project location relative to its surrounding region. However, at the most local levels (e.g., zipcode or city), economic data is sometimes more limited than at county levels. The Pre/Post Conditions tab also contains a new report on transportation trends, shown in Figure 10. This report con- tains information on both the project and the transit system to which it belongs. Ridership numbers are annualized and provide the ability to compare the station or routes associated with the project with the overall system. The broader system is broken into categories such local bus and streetcar; rapid transit like light rail, heavy rail, and BRT; and commuter rail and bus services. From this information, we can see that the new riders at the NoMa–Gallaudet Station represent 1/12th of the total growth in WMATA’s rapid transit system. This trans- portation information is very important in understanding a project and in interpreting the impacts from the previous tab. The final tab, seen in Figure 11, provides an aerial view of the project—in this case, located directly over the NoMa– Gallaudet Station—although for longer, the full project extent maybe less apparent. This interface allows the user to pan and zoom, as well as enter Google’s street view. This tab could also house additional visual documentation of the project, if desired. area within walking distance (roughly ¼ mile) from applicable transit stations. Thus, any new jobs and development activity that was not relocated from within the local neighborhood was counted as a net impact. When more cases are added in the future, it is likely that some will have impacts at a broader city- wide or, perhaps, even at a county-wide level; in those cases, it will be important to redefine the study area for calculating net impacts accordingly. Of course, more effort will be required to identify the extent to which some of the area jobs, income, and land development were merely shifted from other parts of the larger study area. All of these issues should be addressed in the narrative discussion. Following the Impacts report, TPICS for Transit compiles a significant amount of background pre/post data on socio- economic conditions for three geographic boundaries for the project. This includes information on background economic trends at a local, county, and state level, as well as selected transit system data. The latter is a new feature of the transit case study database. Figure 9 presents an example of the Pre/Post Conditions tab, showing economic data at a county level. It is important to note that this pre/post data is not intended to represent eco- nomic impacts of the project but, rather, supporting infor- mation that can help in the determination of net impacts. For instance, the new jobs and building investment occurring in the vicinity of a new transit station may be even more notable if the economy of the broader zipcode or city was declining during that period. The pre/post economic data (nominal values) come from national databases and local sources representing years that Impact Year, 2014 NA designates Not Available Figure 8. The impacts report.

23 * all dollar values are nominal (actual for the year in which they occur) ** An increase over time reflects more economic distress Figure 9. The pre/post conditions report (county). * NA designates Not Available * all dollar values are nominal (actual for the year in which they occur) ** An increase over time reflects more economic distress Figure 10. The transportation pre/post conditions report.

24 Figure 11. Case map/images.