Equity Analysis in Regional Transportation Planning Processes, Volume 1: Guide (2020)

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41 By identifying underserved persons in Step 1 and determining their needs in Step 2, an agency begins to know what benefits underserved persons need or which burdens they want to avoid. The next step in conducting an equity analysis is to assess the impacts of the agency’s proposed actions (such as plans, programs, or projects) on underserved persons and their respec- tive control groups (such as minority, non-minority; low-income, non- low-income; and LEP, non-LEP). This chapter guides an agency through selecting and then measuring indicators of agency outputs and forecast outcomes. The sections provide separate guidance for measuring outputs versus out- comes; measurement techniques tend to be similar within the two catego- ries, but they tend to differ from one category to another. Step 3 involves five broad tasks, and each broad task involves subsets of actions to consider or perform. The broad tasks, as discussed in this chapter, are: • Select indicators, • Differentiate project types for evaluation, • Measure outputs, • Measure outcomes, and • Document the task, outputs, and outcomes for use in subsequent steps. Step 4 (addressed in Chapter 6) will discuss how to compare the results found in Step 3 to determine whether any differences constitute disparate impacts or have DHAE. Select Indicators The first task in Step 3 is to select indicators to measure the impacts, benefits, and burdens of the agency’s actions. As with recommended practices in performance-based planning and programming, a comprehensive equity analysis considers indicators of agency outputs and indi- cators of potential outcomes: • Outputs can be described as the “quantity of activity delivered through a project or program” (FHWA 2016). A transportation agency has direct control over output indicators, based on the agency’s decisions regarding inputs and investments. Outputs can demonstrate quanti- tatively how an agency is responding to disparate impacts/DHAE identified in their needs assessment or prior analyses, as required for Title VI and EJ analyses. Examples of outputs C H A P T E R 5 Step 3: Measure Impacts of Proposed Agency Activity

42 Equity Analysis in Regional Transportation Planning Processes include the amount of money spent on transit projects, the miles of sidewalk constructed, and the number of safety countermeasures projects. • Outcomes are the “results or impacts of a particular activity that are of most interest to system users” because the outcomes measure the system users’ experience (FHWA 2016). A trans- portation agency has indirect control over outcome indicators. Examples of outcomes include the decrease in average travel times, the increase in the number of jobs accessible within the region’s average commute time, and the decrease in exposure to poor air quality. To measure congestion reduction, an agency might select indicators that measure the amount of money spent on projects to increase system efficiency (the output) and the travel-time savings resulting from those outputs (the forecast outcome). Meaningful equity analyses should examine both the benefits and the burdens of transporta- tion investments, rather than looking solely at where those investments are allocated. Federal regulations do not prescribe which burdens or benefits to assess, nor which indicators to use. Rather, MPOs can work with underserved persons and communities to determine which items to analyze. The converse of many of the benefits could be burdens, and vice versa. For example, bodily harm as measured by an increase in the number of traffic injuries and fatalities is a burden, but a decrease in injuries and fatalities is a benefit. How to Select Indicators Guidance on how to select indicators is detailed in the following actions: 1. Inventory the indicators the agency currently measures. 2. Create a list of the indicators the transportation agency already measures (such as bicycle network coverage, accessibility to jobs, or average travel time by mode). Any of these indica- tors likely can be tailored to an assessment of impacts on underserved persons without incur- ring significant additional work for the agency. Distinguish which indicators measure outputs and which measure outcomes. 3. Of the indicators the agency is already measuring, determine which indicators can be tailored to address the needs and concerns of underserved persons. 4. Review the inventory of indicators and identify those that relate to the needs and concerns of underserved persons, as identified in Step 2 of the equity analysis. For example, if under- served persons want to increase the amount of time their children spend playing outside, look for measures of the availability and quality of sidewalks, bicycle routes, and access to local parks, with an eye toward developing infrastructure and services that would comple- ment public health, community development, and law enforcement programs. If under- served persons expressed a need for reliable transit, look for indicators of transit hours of service, frequency, and coverage. 5. Determine if the agency needs to add new indicators for a meaningful equity analysis. 6. Identify whether any of the needs identified are lacking relevant indicators and determine what new indicators the agency could begin to measure, either for the current analysis effort or as part of ongoing research activities. If the new indicators would be highly meaningful but would require too high a level of effort to develop at this time, consider including their development as part of the agency’s work plan for the upcoming year. In practice these indicators translate into definitions of benefits and burdens. Table 5 lists benefits and burdens discussed in federal guidance, along with possible indicators for measur- ing them. The inventory created by any given agency might resemble this table, but it should reflect the specific needs and concerns of underserved persons in the region, as discovered through public engagement.

Step 3: Measure Impacts of Proposed Agency Activity 43 Benefits Sample Output Indicators Sample Outcome Indicators Travel-time reductions Dollars invested in projects to improve system efficiency Average commute travel times Number of jobs accessible in 30-minute commute Congestion reductions Dollars invested in projects to improve system efficiency Vehicle/person-hours of delay Congested lane-miles Passenger/freight throughput Safety improvements Dollars invested in countermeasures Numbers of countermeasure projects Decreases in injuries and fatalities Travel option improvements Transit hours of service and service frequencies Sidewalk network coverage Bike lane network coverage Number of jobs accessible in 30-minute transit commute Infrastructure condition Dollars invested in maintenance Roadway and sidewalk condition Burdens Sample Output Indicators Sample Outcome Indicators A denial of, reduction in, or delay in the receipt of benefits See the indicators listed in the benefit rows See the indicators listed in the benefit rows Air or water pollution Number of CMAQ-funded projects Exposure to mobile source air emissions Displacement of persons or businesses Number of households and businesses within or adjacent to proposed roadway expansion corridors Number of households or businesses displaced or rendered less accessible (usually not known until project design but can be estimated prior to that) Loss of access to transit Number of transit stops removed Reduced frequency or coverage of transit routes Number of households with no access to transit Destruction/disruption of community resources, cohesion, or economic vitality, including access to key destinations Density of walkable intersections Pedestrian network connectivity index Ratio of high- to low-stress streets (using a pedestrian level-of-traffic- stress index tool) Increased travel times to access key destinations by mode CMAQ = The FHWA’s Congestion Mitigation and Air Quality Improvement program (https://www.fhwa.dot.gov/fastact/factsheets/cmaqfs.cfm). Table 5. Sample indicators of benefits and burdens. Example in Practice: Develop a List of Potential Measures The MORPC developed a table of potential measures, each of which described the relevant mode, the type of portrayal (population, geographic, or visual), the tool needed to measure it, and the availability of data for immediate use. The list helped the agency to quickly narrow down an initial list of indicators that could be devel- oped fairly quickly, and potential indicators to develop in the future (MORPC 2017). Document Rationale Explain the rationale behind choosing the indicators in the table, and how the agency con- siders each to be a benefit or a burden. Carefully lay out the rationale for each decision to draw

44 Equity Analysis in Regional Transportation Planning Processes on this reasoning for future analyses and when communicating the decisions to the public, stakeholders, and agency decision makers. Be aware of indicators studied and involved in devel- oping solutions to address any identified disparate impacts/DHAE and, in general, the needs and concerns of underserved persons. Differentiate Project Types for Evaluation Some projects benefit adjacent communities, whereas other projects can place adjacent com- munities at risk for adverse impacts such as increased noise, pollution, or physical isolation. To distinguish between projects that will benefit communities and projects that might burden communities, MPOs can begin by categorizing projects by their type (such as safety counter- measures, bicycle and pedestrian, highway expansion). Table 6 describes some of the options for breaking out projects and lists example MPO analyses that appear in this guide. Measure Outputs Transportation agencies typically measure output indicators by comparing the distributions of funding between underserved communities and the remainder of the region. Many indi- cators also can be expressed in terms of their occurrence or usefulness relative to the study population. This section outlines two approaches of measuring outputs: allocating investments by Reason to Break Out Examples: Projects to Examine Examples: MPO Analyses Literature Review, Pilot, and Case-Study Information Volume 1 Appendix Volume 2 App. A App. B Projects tending to have net benefit to adjacent communities • Transit, if it stops in the community • Maintenance and preservation projects • Bicycle and pedestrian facilities • Highway modernization • Safety countermeasures • DRCOG • Memphis Urban Area MPO ** • MARC • Wichita Area MPO (WAMPO) ** Projects that may generate a net burden to adjacent communities (e.g., risks of displacement or pollution) * • Highway expansions that could fragment a neighborhood or speed up traffic in a pedestrian area • Express transit lines that bifurcate the community without adding access • DRCOG • Madison Area Transportation Planning Board (MATPB) ** Modes favored by underserved persons • Transit • Bicycle and pedestrian facilities • DRCOG • MATPB ** • WAMPO ** * Supports a planning and environmental linkages (PEL) approach: identifying potential impacts during planning prepares for them to be addressed during project design, thereby streamlining the NEPA and environmental review process. ** References to the Memphis Urban Area MPO in Volume 1 appear in the Examples in Practice text box, “Educating the Community”; in chapter text under the headings, “Define Population Groups for Analysis” and “Conduct Additional Quantitative Analysis,” and in Appendix Table A-2, with additional information provided in Volume 2. References to the MATPB in Volume 1 appear in two “Example in Practice” text boxes: “Enriching Travel Survey Data” and “Using GTFS,” with case study information provided in Volume 2. The WAMPO case study information is provided in Volume 2. Table 6. Types of projects to consider breaking out for separate analyses.

Step 3: Measure Impacts of Proposed Agency Activity 45 geographic area and allocating investments by users. Both approaches not only fulfill federal requirements but also encourage agencies to conduct a meaningful equity analysis. MPOs can work with equity stakeholders in a region to determine which approach would be most meaningful. Allocate Investments by Geographic Area The FTA requires that project maps be overlaid on maps of underserved communities to determine where investments are being allocated. The overlaid maps can help an agency to compare the distributions of investment dollars in areas with required populations to dis- tributions in other areas. To look more closely at the comparisons, some agencies further stratify the data into modal categories and/or calculate relative per capita spending levels. For example, if an agency has learned that underserved persons are heavily reliant on transit for access to employment, then it would want to be able to evaluate its per capita investments in transit. This type of analysis can be conducted by performing the following actions: 1. Create map layers for each project category or funding source, overlaid on the maps created in Step 1 of the equity analysis. Given that the ultimate purpose of an equity analysis is to determine whether required populations are at risk of bearing higher burdens or receiving fewer benefits than other populations, it is best to create separate map layers of the project categories that were just developed. For each project category, overlay the map layer on the base maps of high-priority areas for each population being analyzed, which were created in Step 1. 2. Attribute investments to the adjacent communities. Divide the funding for each project among the communities the project touches. For example, if a highway expansion project touches six census tracts (or whatever unit of geography is being used for analysis), then assign one-sixth of the project’s funding to each tract. Sum the investments in underserved com- munities and in the control population groups for each project type and population group. Track this information in a table (comparable to Table 7) for use in making comparisons. Measure Population Group Total Investment ($million) Population Size Per Capita Allocation ($) Bicycle/Pedestrian Total Investment: $20 Million Minority Communities $12 1,000,000 $ 12.00 Non-Minority Areas $8 1,000,000 $ 8.00 LEP Communities $1 100,000 $ 10.00 Non-LEP Areas $19 1,900,000 $ 10.00 Low-Income Communities $14 600,000 $ 11.18 Non-Low-Income Areas $6 1,400,000 $ 4.29 Highway Expansion Total Investment: $1 Billion Minority Communities $450 1,000,000 $ 450.00 Non-Minority Areas $550 1,000,000 $ 468.42 LEP Communities $110 100,000 $2550.00 Non-LEP Areas $890 1,900,000 $ 272.00 Low-Income Communities $400 500,000 $ 667.00 Non-Low-Income Areas $600 1,500,000 $ 429.00 Table 7. Sample per capita spending per category of underserved persons.

46 Equity Analysis in Regional Transportation Planning Processes If additional underserved population groups are being considered in the equity analysis, be sure to include each population and its control group as a row for each measure. 3. Determine the per capita funding distribution. Add two columns to the table, labeled “Population Size” and “Per Capita Allocation.” Divide the total planned investments per project type by the actual population sizes to determine the per capita allocation, as shown in Table 7. If the analysis stops at Action 2 and only takes into account the total investment amounts, there would appear to be a lower level of investment in bicycle and pedestrian projects in LEP communities. Calculating the data on a per capita basis (Action 3) enables the agency to examine the investments based on relative population sizes, which shows a proportionate distribution between LEP and non-LEP populations. Allocate Investments by Usage by Different Population Groups A use-based analysis assigns spending amounts for a project to populations based on their use of that type of project rather than on the project’s proximity to a high-priority area for that population. This approach may more accurately allocate the spending among different popula- tion groups than does the geographic-based approach. The basic actions to be performed are: 1. Categorize investments as transit, roadway, or non-motorized (or other modal splits as appro- priate to a region and the available data). 2. For each mode, calculate the share (percentage) of vehicle-miles traveled (VMT) or trips made by each population group on that mode under current conditions. This data can come from the travel model, transit agency data, household travel survey data, or census data. For example, the share of trips taken by low-income individuals equals the number of transit trips taken by low-income individuals divided by the total number of transit trips. 3. Allocate the investments in each mode to the population groups based on the share of trips that are taken by each population group (as calculated in Action 2). For example, the share of investment in transit that benefits low-income individuals equals the total transit investment multiplied by the share (percentage) of transit trips taken by low-income individuals. 4. For each mode, compare the total investments allocated to each population group to that population group’s usage of the mode. Table 8 shows a hypothetical use-based comparison. In this example, the agency is ade- quately investing in minority communities reliant on transit (i.e., 64% of the agency’s transit investments are directed toward the 62% of the minority population that uses transit); however, the investment may inadequately meet the needs of low-income persons who depend on transit (only 51% of the transit investments are directed toward the low-income population, which makes up 58% of those who use transit). Further analysis would be needed to address whether this under-investment is a disparate impact. It is important to note that projects with Share of People Share of Trips Share of Investments Transit and Roadway Transit Roadway Transit and Roadway Transit Roadway Low-Income 30% 31% 58% 28% 40% 51% 28% Non-Low-Income 70% 69% 42% 72% 60% 49% 72% Minority 61% 54% 62% 55% 57% 64% 52% Non-Minority 39% 46% 38% 45% 43% 36% 48% Table 8. Sample investment allocation by usage.

Step 3: Measure Impacts of Proposed Agency Activity 47 lower levels of usage may reflect lower investment, and that low usage does not preclude the potential use of a future investment. This is a common occurrence with bicycle and pedestrian projects, for which agencies must first invest in a safe and connected network of facilities before cyclists and pedestrians feel comfortable using those facilities. Example in Practice: Use-Based Approach The San Francisco MTC used the use-based approach to allocate spending by project use or mode, looking at indicators such as number of trips on a transit route or VMT on a roadway. The MTC broke the usage down by different popula- tions to determine if investments were proportional to the travel decisions made by residents (MTC 2015). Measure Outcomes MPOs use regional travel-demand models to forecast the impacts of an investment or policy on transportation system performance. These analyses may occur at the corridor level or at the regional scale and typically employ TAZs as the geographic unit of comparison. The primary model-based outcomes are accessibility measures, which may include measures of average highway or transit travel time to job centers or other major destinations. Many MPOs already compare the forecast outcomes of their plans and programs to current conditions or to alter- native future scenarios using the results of travel-demand models and off-model estimations. For some measures, the model results need to be postprocessed using custom programming scripts to obtain the results at a required format or level of aggregation. This section of the guide describes a population-weighted approach to using those same fore- cast measures in an equity analysis by weighting each TAZ’s results according to the percentage of the region’s population that resides within that TAZ. (Some agencies that use this approach may call it a population-based approach.) The population-weighted approach contrasts with the geographic-based approach, in which only the outcomes forecast for high-priority TAZs are considered. An analysis based on the geographic approach will likely omit underserved persons who do not live in those high-priority TAZs, whereas an analysis based on the population- weighted approach is likely to include them. Determine the Demographic Makeup for Each TAZ See “Step 1: Identify Populations for Analysis” (Chapter 3) for instructions. For each TAZ, also determine what percentage that TAZ contains of each of the regional populations for each population group being analyzed. For example, if TAZ A has one minority individual and the region has 100 minority individuals, then TAZ A has 1% of the region’s minority population. When calculating the regional outcomes for minority persons, TAZ A will be weighted at 1% as the TAZs are summed to develop the full regional outcome. Develop the Scripts for the Selected Outcomes Most indicators for outcomes are based on the model’s estimation of auto and transit travel times from each TAZ to every other TAZ, which is known as the travel time skim. Model scripts

48 Equity Analysis in Regional Transportation Planning Processes can build off the trip characteristics (such as travel times) to assess accessibility. A relatively simple script can calculate the number of trips originating in each TAZ. Then, for each TAZ, the script can divide the trips among that TAZ’s identified demographic groups. For purposes of equity analysis, demographic groups to parse out for each TAZ must encompass the popula- tions required under Title VI, E.O. 12898, and E.O. 13166: • Minority persons and non-minority persons, • LEP persons and non-LEP persons, and • Low-income households and non-low-income households. By attributing a share of each TAZ’s trips to the six population groups listed above, the scripts can calculate trip-based indicators for individual population groups throughout the region by summing the outcomes for each TAZ. The population-weighted approach can be applied to four-step models, trip-based models, tour-based models, or activity-based models. The same approach of weighting the TAZ results by their relative share of the regional population can be applied to any measure the agency forecasts using the model’s travel-time skims. The following sections describe the process for three common types of outcomes. Average Travel Time for a Given Trip Purpose Travel models produce travel times for a variety of trip purposes. Although models differ in their defined trip types, a model might include mandatory trips (i.e., trips to work or school), trips for shopping, trips for other purposes, and trips for all purposes. The actions listed in this section of the guide demonstrate how to calculate the average travel time for work trips, but the same method would be used for other trip purposes: 1. Match the travel-time skim with the regional work-trip table. 2. Determine the number of work trips from each TAZ, and apportion them among the various populations for analysis based on each TAZ’s demographic makeup. 3. When summing the TAZ results into the regional totals for each population group, use the regional percentage of each population group within each TAZ to calculate the weighted average work travel times for that population group. Average Number of Destinations Accessible Within a Given Travel Time Travel models also are used to measure the number of destinations that can be reached within a given travel time by transit or by driving. Destination types could include job opportunities, shopping opportunities, or other opportunities (depending on what is available in the model). Agencies can perform the following actions to calculate the average number of jobs nearby, and the same method can be used for other destination types. 1. Select the appropriate travel-time thresholds and whether the transit trip times will include only in-vehicle time or also time spent walking, waiting, or transferring. (If the agency also produces forecasts of the number of jobs nearby, then the agency can use their existing approach. Many agencies use the region’s average travel time for work trips. This demonstra- tion uses a 20-minute drive threshold and a 40-minute transit threshold.) 2. Run the model to generate the travel-time skims. 3. Calculate the number of jobs accessible within a 20-minute drive and a 40-minute transit trip from each TAZ. 4. When summing the TAZs into the regional totals for each population group, weight the average number of jobs accessible from each TAZ by that TAZ’s share of the regional popula- tion of each population group.

Step 3: Measure Impacts of Proposed Agency Activity 49 Percentage of Population with Reasonable Access to Important Destinations The agency may want to understand how many people (and how many of each population group) live within a reasonable travel time of major destinations such as colleges, hospitals, grocery stores, or major retail destinations. These numbers are measures of accessibility. The actions listed in this section describe an approach for measuring accessibility to grocery stores using a traditional four-step travel-demand model. The same approach could be used for any destination of interest to the agency and the equity stakeholders in its region. 1. Determine which TAZs have grocery stores that qualify for inclusion in the assessment. 2. Select a travel-time threshold that is appropriate for trips to grocery stores (such as 20 min- utes), and use the travel-time skims to determine which TAZs contain grocery stores within the selected travel-time threshold. 3. For the TAZs that contain grocery stores within the selected travel-time threshold, sum the results by each population group. 4. For each population group, divide the number of persons who can reach the grocery stores (as calculated in Action 2) by the number of persons who cannot, and multiply by 100 to calculate the percentage. Example in Practice: Population-Weighted Approach The MORPC uses this population-weighted approach and has documented the approach well in the equity analysis appendices to its plans and programs (MORPC 2017). The MARC piloted the approach as part of the development of this guide (MARC 2015). Following its initial research, the project team also identified the Northwestern Indiana Regional Planning Commission (NIRPC 2011) and the Licking County (Ohio) Area Transportation Study (LCATS 2016) as additional examples of the population-weighted approach. Run the Scripts and Collect the Data Once the population-based scripts have been written for selected indicators, the next step is to run the scripts for the current conditions and the forecast conditions. It is important to compile the script results into a table that compares results across population groups, such as the example shown in Table 9. Performance Measure Population Group Current Conditions Forecast Conditions Commute Travel Times Minority Non-Minority LEP Non-LEP Low-Income Non-Low-Income Regionwide Table 9. Sample format for organizing data.

50 Equity Analysis in Regional Transportation Planning Processes Current conditions are included to help fulfill FHWA EJ guidance that the analysis consider “the cumulative effect of a decision in combination with past actions and all other reasonably foreseeable future actions” (FHWA 2015). Therefore, the analysis needs to describe whether minority persons and low-income persons are currently experiencing disproportionate benefits/ burdens as well as the distribution of benefits and burdens in the forecast year. Apply the Data Collected to Any Postprocessing or Other Forecasting Efforts Once the model results are available, use the model data to forecast other outcomes, such as exposure to mobile source emissions. Use the region’s air dispersion model to analyze mobile source emissions to determine the degree to which underserved persons and their relative con- trol populations are exposed to roadway emissions. If the region also has a noise and vibration analysis approach, use that to measure the burdens experienced by different populations. Limitations Limitations apply to any exercise that relies on the travel-demand model, including those described in this section. For example, many models are not good at capturing travel by walking or bicycling. Transit travel times might not be well represented and may not include time spent walking, waiting, or transferring. Measures of access to jobs often do not consider differences in education levels and the appropriateness of jobs. Also, rapid changes in transportation technol- ogy are leading some researchers to question the validity of some traditional models. Document Measurements for Use in Next Steps Fully document the rationale and assumptions made during the analysis process. This docu- mentation will enable an MPO to dig into the data more deeply if disparate impacts/DHAE are found, and it will assist in communicating the findings and perspective to the public, equity stakeholders, and agency decision makers. The data collected in this chapter will likely reveal some differences in impacts or effects among the various population groups. Chapter 6 (covering Step 4) will assist in determining whether those differences are disparate or disproportionately high and adverse, and Chapter 7 (covering Step 5) will provide some ideas for mitigating identified disparate impacts/DHAE. Resources AARP. 2018. AARP Walk Audit Tool Kit (and Leader Guide). Retrieved from: https://www.aarp.org/livable- communities/getting-around/info-2014/aarp-walk-audit-tool-kit.html. FHWA. 2015. Environmental Justice Reference Guide, FHWA-HEP-15-035, FHWA, April 2015 [Online] Avail- able: https://www.fhwa.dot.gov/environment/environmental_justice/publications/reference_guide_2015/ index.cfm. FHWA. 2016. Transportation Performance Management Toolbox, Appendix C. Retrieved from: https:// www.tpmtools.org/wp-content/uploads/2016/09/guidebook-final-appendix-c.pdf. FTA. 2012. Title VI Requirements and Guidelines for Federal Transit Administration Recipients. FTA C 4702.1B. Retrieved from: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/FTA_Title_VI_FINAL.pdf. Licking County Area Transportation Study (LCATS). 2016. Transportation for Progress 2040. Retrieved from: http://www.lcats.org/documents/documents/2040Plan/Transportation_Plan_2040_Final_Draft_ 05102016.pdf MARC. 2015. Transportation Outlook 2040. Retrieved from: http://www.to2040.org/.

Step 3: Measure Impacts of Proposed Agency Activity 51 MATPB. 2017. Regional Transportation Plan 2050 for the Madison Metropolitan Area, Appendix B: Envi- ronmental Justice Analysis. Retrieved from: http://www.madisonareampo.org/planning/documents/ RTP_2050_Report_Final.pdf. Memphis Urban Area MPO. 2016. 2040 Regional Transportation Plan. Retrieved from: http://memphismpo.org/ sites/default/files/public/livability-2040-all-chapters.pdf. MTC (San Francisco Bay Area Metropolitan Transportation Commission). 2015. Regional Equity Working Group. Retrieved from: https://mtc.ca.gov/about-mtc/what-mtc/mtc-organization/interagency-committees/ regional-equity-working-group. MORPC. 2017. 2016–2040 Columbus Area Metropolitan Transportation Plan, Appendix 3, Environmental Justice Analysis. Retrieved from: http://www.morpc.org/wp-content/uploads/2017/12/MORPCTIP2018- 2021Appendix3EJ.pdf. Northwestern Indiana RPC (NIRPC). 2011. 2040 Long-Range Transportation Plan: Chapter 2, Transportation. Retrieved from: http://www.nirpc.org/wp-content/uploads/2017/01/ch.2_transportation.pdf Wichita Area MPO (WAMPO). 2015. Move 2040 Long-Range Transportation Plan. Environmental Justice Supplemental. Retrieved from: http://www.wampo.org/Boards/Pages/MTP-PAC.aspx.