Environmental Justice Toolkit, Volume 2

DRAFT

12/31/2008 Transportation Equity Cooperative Research Program

Environmental Justice and Transportation Toolkit, Volume 2 Submitted to Monica McCallum Equal Opportunity Specialist, Region 10 Office of Civil Rights Federal Transit Administration 915 Second Ave, Suite 3142 Seattle, WA 98174 Submitted by Glenn Robinson, Project Director Principal Investigator School of Engineering and Institute for Urban Research Morgan State University

Report No.

Government Accession No. #20.514

Title and Subtitle Baltimore Region Environmental Justice and Transportation Project Authors: Glenn Robinson et. al Performing Organization Name and Address School of Engineering and Institute For Urban Research Morgan State University, 1700 E. Cold Spring Lane Baltimore, Maryland 21251 Sponsoring Agency Name and Address Monica McCallum Equal Opportunity Specialist, Region 10 Office of Civil Rights Federal Transit Administration 915 Second Ave, Suite 3142 Seattle, WA 98174 Supplement Notes: Project Officer Monica McMillan

Recipient’ Catalog No. TBA Report Date December 2008 Performing Organization Code Performing Organization Report No.

Work Unit No. (TRAIS) TBA

Contract or Grant No. MD -26-8001-01

Abstract This final report contains the completed EJT Tool Kit. It takes the form of a guide whose function is to provide clarity to practitioners on how to identify, understand and approach environmental justice issues at all levels. Like the Community Guide, Toolkit and Technical Documentation found on the http://www.brejtp.org website this final report will endeavor to quickly educate the user in the nature of the issues, orienting them to the key regulatory requirements guiding EJ, a synopsis of how the requirements have been responded to, characterization of good vs. deficient responses, and general instructions on how to use and benefit from the Tool Kit. Following the general structure of the EJT framework, major sections in this manual include:    

A categorization and discussion of EJT issues; Orientation to public outreach strategies Institutional factors and alternative approaches to deal with EJT A guide to applicable analytical tools and procedures, including measures of performance and tradeoff analysis  Work sheets, directions and look up guides to support application of an EJT analysis using the Tool Kit Dissemination will be by Internet, print media, TRB, publications, on project sponsor, team member, and various interest groups’ websites, with links to downloadable .pdf versions for users to copy. Products will also be disseminated through the GBUL affiliates network, Environmental Health Centers and the Transportation Advocacy Networks. Our intent is to: Better identify and address EJ issues using the enhanced community involvement and technical analysis procedures and techniques in the EJ & Transportation Toolkit, which is fully integrated into Baltimore’s regional transportation planning. Key Words Baltimore Region Environmental Distribution Statement Type of Report and Period Justice and Transportation Project (BREJT) Interim Report Covered Final Report Security Classification of this report Security Classification of this page No of Pages Unclassified (NA) Unclassified (NA) 82

4

Research Team Glenn Robinson, Morgan State University, School of Engineering and Institute for Urban Research Aaron Golub, Arizona State University Tim Buckley, OHU, Department of Environmental Health Studies Brendan Nee, IT Yohannes Hailu, Michigan State University, Land Policy Institute Jackie Grinshaw, Center for Neighborhood Technology National Experts Center for Community Change, Rich Stolz Smart Growth of America, Don Chen University of Utah, Tom Sanchez Transportation Equity Network, Laura Barrett Outreach Team Wallace Watson, PIIN Sarah Mullin, Isaiah Pamela Hardway, Moses Lisa, Hussain, Urban Habitat

Acknowledgements Baltimore Region Environmental Justice and Transportation Project Team and the Low Income and Minority Communities of Baltimore

Disclaimer

The opinions, findings, and conclusions expressed in this publication are those of the Transportation Equity Cooperative Research Program project team who are responsible for the facts and accuracy of the data presented herein. This report does not constitute a standard, specification, or regulation. 5

Table of Contents Disclaimer ......................................................................................................................................5 Introduction .................................................................................................................................8 Goals .....................................................................................................................................9 Application and Objectives .............................................................................................10 Lessons Learned ................................................................................................................10 Need for Additional EJ Analysis Tools..........................................................................12 Approach............................................................................................................................13 Hypothesis .........................................................................................................................14 Analysis Framework: Public Participation, Accessibility and Public Health ..................14 Environmental Justice Impact Analysis.................................................................................16 Phase 1 - Community-Driven Intergovernmental Engagement and ............................16 Cooperation - I-95 Corridor Community Voices..........................................................16 Issues...................................................................................................................................17 Phase 2 - Community Assessment and Citizen Input Investigations (Identify the Local Problems) .....................................................................................................................17 Resident’s Concerns..........................................................................................................17 Phase 3 - Information Gathering and Analysis (Due Diligence)....................................18 Phase 4 - Developing a Community Profile (Analytical) Mapping Analysis ..............18 Phase 5 - Drill Down to Evaluate the EJT Issues (Evaluation) .......................................28 Accessibility Calculator........................................................................................................28 Motivation..........................................................................................................................28 Accessibility Calculator Structure ..................................................................................29 Data Requirements ...........................................................................................................30 Basic Accessibility Calculation........................................................................................30 Accessibility for Particular Neighborhood (TAZ ‘I’) ...................................................31 Analyzing a Selection of Neighborhoods by Characteristics ..............................................35 Assessing Low Income and Minority Travel Behavior ...................................................39 Motivation..........................................................................................................................39 Methodology......................................................................................................................40 Results: Sample Report ....................................................................................................43 Public Health Rationale as a Transportation Decision-Making Factor .........................44 Background and Rationale ..............................................................................................45 Assessing Transportation-Related Health Risk in Baltimore, Maryland..................47 Data .....................................................................................................................................47 Methodology......................................................................................................................47 Results.................................................................................................................................49 Discussion ..........................................................................................................................54 Vehicle Miles of Travel Drill-down Analysis....................................................................54 Understanding VMT – What Influences VMT Trends ................................................56 VMT Reduction – Econometric Model...........................................................................57 6

Steps Followed to Generate Estimations (Results).......................................................57 Regional Impact Analysis ....................................................................................................58 Low Income and Minority Community Area Analysis ..............................................60 I-95 Corridor Analysis......................................................................................................69 Summary ............................................................................................................................73 Phase 6 - Being Heard (Communicating) Policy Implications .......................................74 Conclusions................................................................................................................................76 Appendix 1: Bottom up Categorization and Discussion of EJT Issues .............................77 Appendix 2: Performance Measures, Analytic Tools, and Distributive Impacts ............82 Appendix 3: Look Up Guide to Support Application of an EJT Analysis........................83 Figures Figure 1: Cherry Hill Community/Land Cover (2000) .......................................................19 Figure 2 Overall structure of accessibility calculator and interface...................................30 Figure 3 Basic Accessibility Analysis Calculation................................................................31 Figure 4. Menu to select specific neighborhood for analysis..............................................32 Figure 5 Example output from specific neighborhood analysis ........................................33 Figure 6 Tabular output for particular neighborhood accessibility analysis ...................34 Figure 7 Output for analysis of neighborhoods with at least 40% African-American households .................................................................................................................................37 Figure 8: Table of accessibility to “Manufacturing” jobs for neighborhoods with at least 40% African-American households........................................................................................38 Figure 9 Table of accessibility to “Manufacturing” jobs for neighborhoods with at least 50% households with incomes below 200% federal poverty line ......................................39 Figure 10 Personal Information Entry Page ..........................................................................43 Figure 11 Underserved Comunities .......................................................................................45 Figure 12. Risk index applied to Cherry Hill .......................................................................49 Figure 13 Risk index applied to Federal Hill .......................................................................50 Figure 14. Risk index applied to Kirk Ave ...........................................................................51 Figure 15. Risk Index applied to "Hightway to Nowhere..................................................52 Figure 16. Scatter plot of vehicle miles within 200 feet of Baltimore residences in relation to median household income. ..................................................................................53 Figure 17. Scatter plot of ordinal transformation of transportation and socio economic status indicators for Baltimore households...........................................................................53 Illustrations Illustration 1: I-95 Corridor Transit Access ...........................................................................22 Illustration 2: African Americans and Hispanic/Latino Communities ........................................23 Illustration 3: Median Household Income and Household Density .................................24 Illustration 4: Change Median Household Income and Number of Office Workers......25 7

Illustration 5: Change in Other Workers and Population ...................................................26 Illustration 6: Change in Worker Density and Total Number of Workers .......................27 Tables Table 1: EJ Impact Analysis Tools...........................................................................................10 Table 2: Lessons Learned..........................................................................................................12 Table 3: I-95 EJ Impact Analysis Tools....................................................................................12 Table 4 : Major Low Income Low Income and Minority Issue...........................................15 Table 5: Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000..20 Table 6 Sample report that pulls profile and trip data from the database .......................44 Table 7: Total Vehicle Miles Traveled ........................................................................................54 Table 8: Baltimore Area VMT and Emission Summary by Functional Class.............................55 Table 9: Regional Vehicle Miles Traveled Analysis .............................................................59 Table 10 Regional Analysis with Income Classes.................................................................60 Table 11: All Case Study Areas vs. Region............................................................................61 Table 12: Cherry Hill Study Area Growth Patterns .............................................................62 Table 13 Econometric Analysis Results for Cherry Hill ......................................................62 Table 14 Kirk Ave. Bus Depot Study Area Growth patterns:.............................................63 Table 15 Econometric Analysis for Kirk Ave ........................................................................64 Table 16: Lexington Market Study Area Growth Patterns..................................................65 Table 17 Econometric Analysis for Lexington Market ........................................................65 Table 18: Highway-to-Nowhere Study Area Growth Patterns ..........................................67 Table 19 Econometric Analysis Lexington Market ..............................................................67 Table 20: Sensitivity Analysis Changes in Socioeconomic Factors on VMT (2005-2030)69 Table 21: Regression Results – I-95 Corridor ........................................................................70 Table 22: I-95 Corridor Study Area Growth Patters ............................................................71 Table 23: Measuring Equity.....................................................................................................83 Table 24: Performance Measures by Planning Goal Area...................................................84

8

Introduction The purpose of this project is to use the lessons learned in the BREJT project to incrementally advance public participation in the field of transportation where the literature speaks clearly with examples of low income and minority, urban and rural neighborhoods that were once viable but are now unstable losing population and suffering declining land values. In some cases development pressures take advantage of disadvantaged neighborhood vulnerabilities and often result in pressures that lead to abandonment, dismay and gentrification. Underserved population groups seek ways to create healthier living environments see environmental justice planning as an opportunity to address what they perceive to be a lack of universally accepted definitions and procedures for analysis and evaluation of local issues. That the fuzzy nature of the existing environmental justice law that weakens rather than strengthens environmental justice’s mitigation through enforceable regulations is confirmed by the “Environmental Justice Law Handbook” which cites that legal outcomes have been mixed and notes that it has only been since 1997 that plaintiffs began winning environmental justice cases, which did not require proof of intent. Prior to 1997 proof of intent was an absolute requirement. As a result of changing legal rulings and in an genuine effort to improve the public participation process planning organizations who are obliged to treat EJ communities equally in terms of affording them meaningful public participation opportunities and ensure that they have opportunities equal to those of the most “important” stakeholders. Federal regulations recognize that planning organizations must give EJ communities extra assistance to take full advantage of opportunities for meaningful participation.

Goals The overall goal of the Transportation Cooperative Equity Research Project (TCERP) project is to apply and build upon the Baltimore Region Environmental Justice and Transportation platform, to incrementally advance the integration of Environmental Justice into metropolitan planning process and develop a second volume EJT Toolkit that has a focus on helping communities and their planning agents better understand the impact of public health, transportation accessibility and travel demand on Low income and minority communities. And in doing so we seek, to validate and broaden a wider range of tools for finding solutions to EJT issues and problems. Achieving these goals will provide additions to the family of procedures and applications used in the Baltimore Region Environmental Justice and Transportation Project and enable the user to:   



Extend the voice of the community into the planning process through collaboration rather than agitation; Demonstrate the use of analytical for identifying and evaluating EJ considerations in the planning process; Provide planners, community representatives and decision-makers with better performance variables to measures consequences and tradeoffs when evaluating alternative impacts; and Reinforce the need to establish an institutional structure with appropriate authority and expertise to ensure objective review and response to important EJ issues.

9

Application and Objectives In developing this 2nd Volume of an Environmental Justice Toolkit the Baltimore Region Environmental Justice and Transportation (BREJT) Project Toolkit is applied using the I-95 corridor as the case study example. This is done in support of the TCERP projects key objective of reapplying the toolkit in Baltimore, independent of the Baltimore Metropolitan Councils participation, to obtain feedback on the functionality of the Toolkit, whether it is useful, and ways it should be enhanced and improved. In doing so this project advances the primary goal of being responsive to the concerns raised by the community in Phase I of the BREJT project, work earnestly toward identifying reasonable solutions, gain sufficient experience and insight in trying to address these concerns and create a toolkit, for use by others locally or nationally. These objectives will be accomplished through a coordinated set of eight work tasks that are designed to look at two levels of Problems – ones that exist, and ones that arise because of some new project or policy that is proposed. Within each level, the following kinds of impacts would be examined – air pollution/health; accessibility to jobs; policy effects; and other costs and burdens that the minority community may disproportionately result from transportation services and policies.

Lessons Learned The development of this toolkit is based several notions. They are: 1) low income and minority communities are still in need of effective channels for working within the “system”, 2) finding solutions to issues that are central and urgent to the tenants of environmental justice promotes a better a better understanding of the decision making process facilitates public participation and leads to change, 3) the resolution of EJ issues will involve an intertwined and iterative analysis process that focuses on collaboration rather than agitation and 4) that the complexity of equity impact analysis overlaps with and is dependant on a complexity of the intertwined issue off ecology, housing, public health and governance. Table 1 illustrates the mix of analytical tools used to frame and evaluate the EJ impact analysis of issues used in the BREJT Project and to guide the development of this toolkit. Table 1: EJ Impact Analysis Tools

Kirk Ave Community meetings, listening sessions, diary of concerns, chart bus pullouts, demographic data, Evaluate, take noise air pollution readings and map homeownership and housing sales data. Cherry Hill Community meetings, listening sessions, map population, housing, transit and employment statistics Lexington Market Community meetings, Measure vehicle and pedestrian traffic volumes, Evaluate changes to travel time, Pedestrian counts Highway to Nowhere Community meetings Map congestion levels and travel forecast trips, Chart block census data, Select link analysis 10

The tools used in the BREJT project four previous case studies, MTA Bus Depot, Cherry Hill, Lexington Market and Highway to Nowhere combine lessons learned and several levels of analysis techniques to demonstrate how issues associated with EJT issues can be systematically evaluated using a store of traditional transportation planning impact measures and analytical tools. As such, in the Kirk Ave. case study the MTA Bus Depot concerns focus on the location and operation of a bus depot in an older, inner-city working-class neighborhood along Kirk Avenue. The Cherry Hill case study evaluates a history of public transit service changes, reductions and poor service delivery in a predominately African American, and low-income community with a large number of residents who living in public housing. While the Lexington Market case study analyzes the reaction to changes in transit service in an historic shopping destination frequented by lower-income residents from surrounding communities in central Baltimore. The Highway to Nowhere studies the concerns of communities in the U.S. Route 40 Corridor through West Baltimore regarding plans for a proposed Red Line and efforts to create transitoriented development around an existing commuter rail station (West Baltimore MARC), fearing community disruption, destruction and dislocation as occurred in the partially abandoned I-170 which divided West Baltimore in the 1960s. Important lessons were learned from using the above tools to evaluate the concerns of local residents, low income African Americans, transit dependant populations and community leaders in the BREJT project. The key lessons (Table 3) from the BREJT project informed the formulation of a fifth case study (I95 Corridor)

11

Table 2: Lessons Learned

•In these communities we found a common desire for a better living environment, a more responsive government, and the demise of blight and decay •The clear message that when individuals, neighborhoods and communities are motivated, well organized, and better educated on transportation issues and improvement options a sense of community ownership is created that can better influence project selection outcomes that reinforce regional growth with healthier neighborhoods and stable communities •That Environmental Justice Analysis informs the transportation planning process through the introduction of a community based public participation framework that encourages low income and minority communities to use performance measures and analytical tools

•That when used together by community organizers and planning professionals can result in a more equitable way of assessing environmental justice and transportation issues •That the low-income neighborhoods communities and the transportation maintenance facilities they attract may need better protections to ensure equitable treatment •Busses traveling through communities and passing near schools increase noise and exhaust pollution. Further decreases property value leading to vacant homes that disenfranchises. •Thirty years after the Highway to Now-where was constructed there is still a community memory of the destruction and a palpable bitterness about what was done •The public rightly felt that it had been marginalized by the decision-making process, and that commercial interests (such as a parking lot adjacent to the Market) were given preference over there well being. Upon review of the situation, initial concerns about serious congestion and health effects due to prolonged exposure to vehicle activity-as framed in the community discussionsappeared less severe than initially portrayed •Light rail is too far away from community leading to difficulty of access for business, workers and community use in general and it is difficulty for seniors and disabled populations to obtain access to light rail station. Seniors have difficulty riding certain buses due to over- crowding. •Planning tools are not fully employed to evaluate EJ issues •Low Income Residents have a higher incidence of congested related illness Table 3: I-95 EJ Impact Analysis Tools

I-95 Corridor Community meetings, Listening sessions, map population, housing, transit and employment statistics and conduct analysis of vehicle miles traveled

Need for Additional EJ Analysis Tools It is important to understand the process that the project used to arrive at the results for each of case study were foremost driven by: issues identified at the community level, the desire to provide a systematic process for identifying the feasibility of EJ issues and then the evaluation of those issues. Having completed the Baltimore Region Environmental Justice and Transportation Project Toolkit (go to http://www.brejtp.org), this proposal presents our plan for developing some Practical Approaches for Involving Traditionally Underserved

12

Populations in Transportation Decision Making (PAIUP). The case studies findings confirm but not necessarily prove the community perception that that there has been an environmental injustice. As a result of that analysis we concluded the need for a better defined approach to tradeoff analysis to support informed dialogue and decision making.

Approach This project is guided by using the six phases shown below to tease out what happened, who was involved and what the community wants. Using this drill down process we focus on the transportation impacts of public health, accessibility and travel demand on low income and minority communities to move community based EJ concern toward a solution. Each phase used in this approach is meant to be inclusive of both the EJ communities and the transportation agency of responsibility.

Phase 1 - Community-Driven Intergovernmental Engagement and Cooperation Phase 2 - Community Assessment and Citizen Input (Identify the Local Problems) Phase 3 - Information Gathering and Analysis (Due Diligence) Phase 4 - Developing a Community Profile (Analytical) Phase 5 - Drill Down to Evaluate the EJT Issues (Evaluation) Phase 6 - Being Heard (Communicating) The above phases are a result of the BREJT project’s listening sessions and community dialogue where over 120 individuals participated in an all-day workshop to share perceptions with elected officials, national experts, and academic specialists. Members of the region’s minority and low-income communities were particularly engaged in this neighborhood and regional dialogue to gain input on the wide range of concerns that have environmental justice implications. The concerns expressed in the Community Voices, Section of this toolkit were used to define the technical approach for analyzing the I-95 corridor environmental justice and transportation impacts. The technical approach is designed to be compliant approach developed in the BREJT project and expand the mix of tools for evaluating the range of EJ issues that were identified in the BREJT project and range from the quality of bus service in poor neighborhoods (e.g., Cherry Hill) to exposure to transportation pollution (East Baltimore), traffic congestion (Annapolis, Md.), and to overall patterns in regional transportation funding. Each of these compelling issues were studied in detail because of there importance to the community, the multiple dimensions to the problem, and the fact that the community had not found a way to extend their voices into the planning decision making process to find acceptable solutions. As well the accessibility calculator and travel diary is presented as tools that support Environmental Justice analysis, evaluation and issue resolution.

13

Hypothesis As a companion to the initial case studies the e I-95 corridors case study broadens the scope of analysis by addressing the impact of future growth and offers an opportunity to add to the collective experience by broadening the range of EJ issues studied in BREJT. The impact area for Defense Base Closure and Realignment Commission (BRAC) is the county of Harford, Howard, Ann Arundel and Baltimore City. Within the impact zone of Aberdeen and Fort Mead are low income and minority sub populations of Middle Branch, Cherry Hill and Westport as well as other middle class communities. The hypotheses upon which this research study is based are as follows: 

Low income and minority households concentrated in central-city Baltimore future access to jobs, education and will challenged by regional growth and development.



The public health risk of low income and minority residents n Baltimore is negatively impacted by proximity to traffic congestion.



The majority of jobs created by BRAC will be located outside the central city, and will be less accessible for the transit dependant.



In some cases development pressures take advantage of disadvantaged neighborhood vulnerabilities and often result in pressures that lead to abandonment, dismay and gentrification.



Underserved population groups seek ways to create healthier living environments see environmental justice planning as an opportunity to address what they perceive to be a lack of universally accepted definitions and procedures for analysis and evaluation of local issues.

Analysis Framework: Public Participation, Accessibility and Public Health The community voices described above is used to help develop the public participation framework model presented below in the next section and is one of three core environmental justice-planning components. The other two components are performance measures and analytical tools. These components combine to provide environmental justice evaluation methods and procedures that may be used to confirm or negate issues identified in low-income communities. In particular the public participation framework is designed as the starting point for vetting issues and developing analysis strategies for interrogating environmental justice and transportation issues through a triage-type activity that has multiple screening levels and explicit feedback loops. The feedback from various experts and our sponsors in the Baltimore Region Environmental Justice and Transportation was that a credible, systematic, comprehensive approach to address Transportation Decision Making in transportation had not yet been developed. Based on this feedback and an extensive literature review, we concurred that this goal had not yet been

14

realized. As a companion to the above vision we prepare this proposal to expand the identification of effective practical approaches for involving underserved population in the transportation decision-making process as a sequel doc to:     

Atlanta Transportation Benefits and Burdens Study NCHRP Project 8-36(11): Technical Methods to Support Analyses of Environmental Justice Issues NCHRP Report 532: Effective Methods for Environmental Justice Assessment Baltimore Region Environmental Justice and Transportation Project Transportation Equity Cooperative Research Program

In laying a framework to quantify the measures of impact we consider more than one approach in investigating a given issue as well as accessibility and public health risk factors, in order to appraise the tradeoff between difficulty of application vs. accuracy of the response, for planning purposes. These measures were selected using the criteria that: realistically reflect the central concerns, may be used to measure and compare both current/unaltered conditions with solution alternatives, can support enlightened dialogue on EJ topics, lead to resolution; and are based on the results from community dialogue held in Baltimore, Pittsburg, Meaneapolis, Oakland and

Detrioit. Chart 1 demonstrate the importance of identifying and framing community issues. The issues described below were framed by four groups of low income and minority community participants. Approximately 100 issues were categorized into twelve major categories (Figure 1). Table 4 : Major Low Income Low Income and Minority Issue M ajor Conce rns By Group

Group 4 Group 3 Group 2 Group 1

A ir po llu tio Jo in b A H ea cc lth ess A R cc ou es te s S tr uc tu re F M und ai nt ing en an ce In fo rm at io P ub n lic I np In Acc ut te e rm ss R od ibi es al lity pi ra C on to ry ne A R i l ud m en e B ts us D riv er s

20 18 16 14 12 10 8 6 4 2 0

Based on the ISTEA Planning Factors, metropolitan transportation plans now reflect a more comprehensive vision and understanding of the role of and impacts resulting from transportation. Correspondingly, the measures of performance have also broadened, as have the capabilities of analytic tools, data resources, and the application of this information in the planning and decision making process. Fittingly, it seems, these comprehensive transportation-planning goals should also serve as the policy framework for evaluating

15

Environmental Justice needs and concerns in the context of both metropolitan planning as well as more perfunctory or topical issues and concerns. The issues and concerns elicited from the Baltimore community during Phase I of the TCERP project speak to this breadth of coverage and specificity that will be required of the Toolkit and the tools and performance measures it contains. A perusal of the concerns summarized in Exhibit 1 suggests an abundance of concern in the following major areas:     

Delivery of transit service: Frequency, proximity, reliability, quality, professionalism Access and mobility: ability to reach jobs, health care, other needs, particularly by transit Funding parity: priorities in poor vs. affluent areas, bus vs. rail transit, condition of transportation infrastructure, inclusion in decision making Environmental: Exposure to traffic, noise, air pollution Quality of Life: Community health, individual health, safety

A reflection on these issues also suggests a spectrum of factors that may be contributing to the concerns that could occur at all levels of planning, funding or operations. Many of the voiced concerns may simply be the result of a change in operating policy that had more deeply reaching effects than anticipated or recognized; in this case it may be sufficient to simply reestablish the communications link between the community and the agency. In other cases, however, the Problems may not be simple in nature or source, and a higher level of assessment and intervention may be required, particularly if the problem is widespread and/or is the result of shifted funding or program priorities. In such a case, it may likely be necessary to deepen the assessment and intervention to better understand the nature of the problem or to investigate alternative solutions. Given this “hierarchy” of issues, their causes, and the potential responses, the analysis tools and the measures in the toolkit must have enough dexterity to permit an analysis which is appropriate and credible for the issue at hand, but which leaves open the option to “dig deeper” if the problem proves to be more complex or difficult to resolve with simplistic methods. Ultimately, the Toolkit will attempt to provide its users with the ability to identify the most appropriate measures and analyses to address a particular issue. Thus, the simultaneous development of the measures of performance along with the analytic tool options in the context of addressing specific issues in a case study context is the preferred vehicle for understanding the salient impacts of environmental justice issues.

Environmental Justice Impact Analysis Phase 1 - Community-Driven Intergovernmental Engagement and Cooperation - I-95 Corridor Community Voices 16

For the 2007 – 2008 period community meetings were was called by the BCDP and the Cherry Hill Trust members with community residents. The meeting participants provided written comments to the BCDP on the draft of the Middle Branch Master Plan, rezoning issues and the finalization of the Cherry Hill Master Plan.

Issues There primary concerns are listed below: Middle Branch Master Plan proposal Notice of the Cherry Hill Master Plan submittal deadline Area Rezoning What will be the impact of the projected growth impact traffic congestion? What additional transportation services will be needed? How will other low income and minority communities be impacted? Will transportation accessibility be improved? What are the potential direct/indirect impacts on the Cherry Hill community?

Phase 2 - Community Assessment and Citizen Input Investigations (Identify the Local Problems) Resident’s Concerns In response to the Middle Branch Master Plan, the community noted its intention to review the plan and tailor its master plan accordingly. The participants rejected the deadline date to complete its master plan. The BCDP noted a future date to meet with the community to extend the community’s input and support. The Cherry Hill Trust and the community residents agreed to meet immediately to formulate a plan to take before the Planning Commission on the rezoning issue (BREJT, research assistant, 2007). Where will the workers come from to fill these jobs, what are their socioeconomic characteristics, and what travel time/cost burden will they have to bear to commute to the corridor? Is there sufficient current or committed transportation capacity to support this level of development and the anticipated travel patterns? What level of new investment will be needed to maintain adequate levels of service? What was the planning and programming process that led to the regional decision to support this growth concept in the regional longrange plan and the Transportation Improvement Program (TIP)? What planning discussions and provisions have occurred to either provide housing and transportation to meet these future worker needs and/or what concurrent plans/investments have been considered to encourage new job creation nearer to the lower-income/minority worker base? How was access to jobs by minority/low-income workers addressed by this plan?

17

Phase 3 - Information Gathering and Analysis (Due Diligence) The BCDP hosted a meeting to elicit community comments for the Cherry Hill Master Plan and the community’s Capital Budget Program. This collaborative task force meeting held in partnership with the Cherry Hill Trust organization presented viewpoints and ideas with the community perspective to the forefront. The Cherry Hill Master Plan is a working document with the BCDP as an active partner in the development and implementation of the plan (BREJT, research assistant, 2007). The Cherry Hill community voiced disappointment of the lack of information provided prior the meeting. The rezoning issued was only handled with the participants of the Middle Branch community. The participants agreed to meet later to brain-storm a strategy to take before the Planning Commission on the appointed date (BREJT, research assistant, 2007). Short term recommendations by the community were made and put into the budget to address excessive loitering in front of the community’s Town Center area. Further, the BCDP provided funding for the distribution of fliers for community safety initiatives, and for the efforts of promoting shopping in the Town Center by working in partnership with the local business associations. The BCDP acknowledged the community’s recommendations, informed the team that further investigations for program feasibility will be required. An emergency community meeting was called by the BCDP and the Cherry Hill Trust members with community residents. A majority of the community voiced opposition towards the Cherry Hill Master Plan deadline date, stating that they were not forewarned or allowed time to respond. Secondly, a request for review of the Middle Branch plan is also needed time to allow comments to be incorporated in Cherry Hill’s plan if necessary. The meeting participants provided written comments to the BCDP on the draft of the Middle Branch Master Plan, rezoning issues and the finalization of the Cherry Hill Master Plan. In response to the Middle Branch Master Plan, the community noted its intention to review the plan and tailor its master plan accordingly. The participants rejected the deadline date to complete its master plan. The BCDP noted a future date to meet with the community to extend the community’s input and support.

Phase 4 - Developing a Community Profile (Analytical) Mapping Analysis As in any analysis this evaluation begins by framing a series of drill down questions that once answer defines to what extent low-income residents are impacted by dynamic growth and development of an expanding urban area. The residents of West Port and Middle Branch like many other low – income and minority residents fear that they will be displaced and unconvinced by growth. With the understanding that transport enables societal objectives to be pursued, such as access to various sorts of opportunities the EJ question then become not only which but how societal objectives are pursued and how the distribution of services dictate the nature and level of services that are provided to assist disadvantaged persons. The question then becomes are these fears justified or unjustified. Our approach to answering this

18

question is to first attempt to reasonably determine understand whether minority/low-income households will continue to be concentrated in central-city Baltimore and if so will their future access to jobs, education, health care and other opportunities will be as limited, or even more limited, than they are today.

Figure 1: Cherry Hill Community/Land Cover (2000)

19

Table 5: Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000

20

Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000

*Note: (Census Tracts should be updated to only include only 250207,250230, 250204) which indicate the 1990 population at 10,897 with 49 Caucasians. In 2000 the population dropped to 7,664 and the number of Caucasians grew to 131.)

21

Illustration 1: I-95 Corridor Transit Access

22

Illustration 2: African Americans and Hispanic/Latino Communities

23

Illustration 3: Median Household Income and Household Density

24

Illustration 4: Change Median Household Income and Number of Office Workers

25

Illustration 5: Change in Other Workers and Population

26

Illustration 6: Change in Worker Density and Total Number of Workers

27

Phase 5 - Drill Down to Evaluate the EJT Issues (Evaluation) Accessibility Calculator The Environmental Justice in Transportation Project is developing an integrated approach for calculating and analyzing accessibility and travel that will enable impacted groups, MPO’s, CBO’s citizens and city planners to conduct a preliminary analysis of current problems and future plans and offer some evaluative insights. The first phase of the toolkit includes two basic modules: an Accessibility Calculator and a Community Travel Survey. Beta versions of both of these tools have been completed, and are presented in this paper with the hope of updating the environmental justice community about the progress of this project and to facilitate feedback and discussion about its direction. Task I-3D of the Environmental Justice in Transportation Project is underway, and what has emerged is an integrated approach for calculating and analyzing accessibility and travel that will enable impacted groups, MPO’s, CBO’s citizens and city planners to conduct a preliminary analysis of current problems and future plans and offer some evaluative insights. The tool will be developed in three phases: a first phase will generate cumulative opportunity measures from selected neighborhoods or for certain demographic groups, combined with community selfsurvey capabilities. A second phase will add further details, such as access measures to certain transportation infrastructure, etc. A third phase will combine travel data with environmental information to measure public health risks and exposure from travel. This document deals with the development of the first phase. The first phase of the toolkit includes two basic modules: 



Accessibility Calculator Understand accessibility to certain land uses (jobs, schools, education) from a given neighborhood or all neighborhoods with a certain demographic characteristics (e. g. low-income) and compare that accessibility to other neighborhoods Community Travel Survey Allow individuals or communities to record and map their own travel patterns and compare this to the regional transportation investment plans

An introduction to these two modules is covered in this report. For more detailed information about the development of the tools, see: [TERP Technical Docs]

Motivation The calculator can be accessed at: http://www.brejtp.com/travel-diary . Accessibility measures the ability to reach a desired destination within a time, distance, or cost limit. Threshold measures analyze who can reach a desired destination within some threshold of time, distance, or cost. The prototype tool will take the Cumulative-Opportunity approach and will measure the number of essential destinations reachable within various times or distance bands by transit and automobile. This is particularly useful in describing how well the

28

transportation network works in relation to the distribution of transportation improvements and how they serve total transportation needs for particular sub-groups. It is also perhaps the most simple and direct to set-up with the typical datasets available from city or regional planning agencies.

There are two basic accessibility measures included in the first phase of the tool development: 1. For a specific neighborhood: a. Calculate the total number of important destinations (jobs, jobs of certain types, schools, medical facilities, etc) reachable within certain time bands (15, 30, 45 minutes) by public transit and automobile. b. Compare this measure with all other neighborhoods c. Compare this measure for the various future regional transportation scenarios 2. For a specific Demographic group: a. Calculate the total, and average, number of important destinations (jobs, jobs of certain types, schools, medical facilities, etc) reachable within certain time bands (15, 30, 45 minutes) by public transit and automobile from all neighborhoods with the specified representation of the demographic under study (e.g. >50% Low-Income) b. Compare this measure with the balance of the neighborhoods and all neighborhoods c. Compare this measure for the various future regional transportation scenarios

Accessibility Calculator Structure The accessibility calculator will use transportation and demographic data and output accessibility measures for each neighborhood. These outputs will be assembled in a database that will allow users to look at the accessibility of different neighborhoods (using the TAZs). It

will be accessed with a front-end interface that supports user query. The interface will then display output through result tables and statistical comparisons. Figure 1 shows the overall layout of the accessibility calculator. The sections which follow will present the basic structure of the databases, the four sections of the accessibility calculator, and the overall web interface.

29

Web-based interface Basic Tabular and Statistical Output

Accessibility Calculations Accessibility for a Particular Demographic

Accessibility for Particular Neighborhood (TAZ ‘I’)

Basic Accessibility Analysis (AA): Accessibility for Particular TAZ ‘I’

Figure 2 Overall structure of accessibility calculator and interface

Data Requirements For simplicity, the urban area is divided into traffic analysis zones (TAZs), which therefore becomes the basic unit of analysis in the tool. The data requirements fall into two types: the demographic and land-use data for each TAZ (called TAZ data) and the TAZ to TAZ travel times for Transit and Automobiles, called Transit and Auto Skims. Each of the these databases are needed for each future planning scenario. The TAZ Data contains all of the information concerning demographics and numbers and types of land-uses contained in each TAZ. The Skims data set is a square set of size TAZ by TAZ with the TAZ to TAZ travel time for every TAZ to TAZ O-D pair.

Basic Accessibility Calculation At the core of the tool is the measure of accessibility to destinations from each reachable TAZ within a given time, T. Those reachable TAZs contain certain numbers of jobs, etc (desirable destinations). The core accessibility calculator will generate a database of the reachable destinations within the time bands specified (15, 30, 45 minutes) for each TAZ. The basic accessibility analysis calculations steps are shown in Figure 2 below.

30

Figure 3 Basic Accessibility Analysis Calculation

The output from the Basic Accessibility Analysis Calculation is a dataset for each scenario, showing the total destinations reachable for each TAZ. This output is used in the later calculations in the tool. From there, the calculation will depend on whether the analysis is done for a particular neighborhood or for neighborhoods with a particular demographic. We look at these two options now.

Accessibility for Particular Neighborhood (TAZ ‘I’) To analyze the accessibility for a particular neighborhood, the accessibility database generated from the basic calculator will be queried for the accessibility measures from the selected TAZ, and all TAZs. Comparisons and maps can then be produced. When the TAZ number is shown, it can be entered into the menu shown circled in Figure 3.

31

Figure 4. Menu to select specific neighborhood for analysis

After you click “Go” the output page is given, an example of which is shown below in Figure 4. Several different kinds of tabular and graphical outputs are presented from top to bottom, and each is discussed in the following sections.

32

Tabular Accessibility Output

Graphical Accessibility Output

Figure 5 Example output from specific neighborhood analysis

33

1 3

2

4

5

6 Figure 6 Tabular output for particular neighborhood accessibility analysis Figure 5 shows the top of the output with numbers pointing to particular parts. Each numbered part will be described now. Item 1 refers to the tabs across the top of the output. The database of accessibility includes various types of “essential destinations.” These different types of destinations correspond to these tabs. So, in any analysis, one can look at the accessibility to Food Stores, Heath facilities, Social Services, Elementary Schools, etc. Some of these “essential destinations” are actual facilities, such as Elementary Schools, and others, such as “Trade” are types of jobs. For facilities, two kinds of outputs are given – the number of jobs in those facilities, and the number of facilities.a Item 2 is pointing out the title of the section: “Food Stores – Number of Jobs.” If you scroll down, there is another section below with the same output for “Food Stores – Number of Facilities.” Item 3 is referring to the columns of accessibilities for the selected neighborhood, TAZ #10. The top row of numbers: 5,028 , 14,724 , 26,306 , etc. refer to the number of jobs in food stores accessible by automobile within 15, 30 and 45 minutes, respectively. This is for the 2000 Base scenario, to be explained in a moment. a

Both are important because the number of facilities shows the availability of the service, but the number of jobs indicates the size of the facilities. For example, the number of food stores indicates availability, but doesn’t indicate whether they are corner “beer and wine” stores or larger full service groceries. That is better understood by the number of jobs in food stores, as larger facilities have more jobs. In communities of concern, there is often a lacking of larger full service groceries, the inclusion of both measures is important. The same measures are produced for schools and other services.

34

Item 4 refers to the columns of accessibilities for all 1400 neighborhoods in the region, in order to facilitate a comparison with the selected neighborhood. The top column refers to the same measures, averaged for all neighborhoods. Item 5 is the five “scenarios” included in the database. These are five different transportation plans, each of which has different characteristics. The 2000 Base scenario is the present day (at the time the travel model was made) and includes existing projects, including highway, road and public transit. The Financially Constrained scenario is the regional transportation plan for 2025 with a set of new investments (including highway, road and public transit) included up to a projected budget (i.e. “Financially Constrained”). The “No Project” scenario is a projection for 2025 which assumes certain population and job growth with no new investments in any transportation projects (including highway, road and public transit). The “Project” scenario is the regional transportation plan for 2025 with a set of new investments (including highway, road and public transit) included up to a larger, more liberal, projected budget than the Financially Constrained scenario. Finally, the Transdef, is a regional transportation plan for 2025 with a different set of new investments (including highway, road and public transit) which places more emphasis on “smart growth” options and public transit investments than the Project scenario. Item 6 is showing how the results of a particular scenario are presented in terms of auto, transit and “composite” accessibility. These are the numbers of destinations reachable within the different time bands by these travel modes. The composite accessibility takes into account a neighborhood’s automobile availability. The database of accessibility for each neighborhood is stored in two ways – the number of opportunities reachable by public transit and the number reachable by automobile. This is done because of the amount of time it takes a public transit users to reach a destination will often differ from the amount of time an automobile user will take. The composite number takes into account the ownership level of automobiles in a particular neighborhood and computes one accessibility number from a combination of the automobile and public transit numbers. The more households with automobiles in a neighborhood, the closer the neighborhood’s accessibility number will be to the automobile number. The fewer automobiles in a neighborhood, the closer the neighborhood’s accessibility will be to the public transit number. For example, for a neighborhood where all households have automobiles, the composite would equal the number of reachable destinations by automobile, while for a neighborhood where half of households have automobiles; the composite would be an average of the number of reachable destinations by automobile and by public transit. Below the table and the eight graphs described thus far, are one more table and eight more graphs presenting the accessibility information for neighborhood #10 for food stores by number of facilities. The destinations which have both number of facilities and number of jobs will have this “double-length” output. The ones with only number of jobs, will only have one set.

Analyzing a Selection of Neighborhoods by Characteristics To analyze the accessibility for a particular demographic, the TAZ database is used to generate a list of TAZs which meet the required demographic, such as “>50% Low-Income” or “<25%

35

White.” Once the TAZs, which qualify, are flagged, the accessibility database generated from the basic calculator will be queried for the total desired uses within the travel time bands for those flagged TAZs, non-flagged TAZs and all TAZs. Comparisons and maps can then be produced. For the most part, the analysis by selection is similar to that for one neighborhood. The two menus #2 and #3 in the calculator are both used for analyzing selections. Here, we will step through an analysis and discuss the various outputs. The same menu shown in Figure 3 above is used to perform a selection by racial/ethnic group. Using the first menu, you can choose the percent of the neighborhood population you wish to represent the racial group you want to investigate in order for the neighborhood to be included in the selection. The next menu allows you to choose in the racial group you want to investigate. In the example, 40% and AfricanAmerican are chosen, which means that any neighborhood where 40% or more of the households are African-American will be included in the selection. Once the choices are made, press “go” and the output is produced. Unlike for the single neighborhood analysis, the output for neighborhood selections is only in table form. Figure 7 below shows the output for the 40% African-American selection made above. The first table shown is accessibility to Food Stores (by number of jobs). The tabs across the top (Item 1) show the same categories of destinations as discussed above. Item 2 shows the tab selected and how the measure is shown. Here, accessibility to Food Stores (by number of jobs) is show. (Scrolling down the page shows the table for the Food Stores by number of facilities.) Note also that “68 TAZ have more than 40% households that are African American out of all 1454 TAZ” tells us how many neighborhoods ended up in the selection made.

36

1

2

3

4

5

6

7

8

Figure 7 Output for analysis of neighborhoods with at least 40% African-American households Item 3 refers to the column of average accessibility measures for the 68 selected neighborhoods. These numbers have the same meanings as those discussed above. For example, for the 2000 Base scenario, the 68 neighborhoods in the selection have an average of 2,315 food store jobs reachable to them within 15 minutes by automobile. Item #4 refers to the column of accessibility averages for all of the neighborhoods not in the selection. There are 1454-68 = 1386 such neighborhoods. Item #5 refers to the column of accessibility averages for all of the 1454 neighborhoods in the Bay Area. This is similar to the column shown in the individual neighborhood analysis. Item 6 refers to a column of “T-scores,” which show the statistical significance of the differences between the accessibility measures for the neighborhoods in the selection and all neighborhoods. The statistical test is performed to see if the selected group of neighborhoods is significantly different from all of the neighborhoods in the Bay Area. The details of how the Tscore is generated are unimportant. What is important, is that the larger the absolute value of the T-score (positive or negative), the more the difference between the group of neighborhoods is significant. A “*” is placed next to T-scores which are significant at the 90% level, meaning they are significant. Two “*” s are placed next to T-scores which are significant at the 95% level, meaning they are even more significant. Three “*” s are placed next to T-scores which are significant at the 99% level, meaning they are extremely significant. T-scores with no stars mean that the differences between the selected neighborhoods and all neighborhoods are not

37

very great. A positive T-score means that the selected neighborhoods enjoy a higher accessibility to the destinations than all neighborhoods. Item 7 and 8 show again the same scenarios and travel modes which were explained in the last section. Scrolling down the page shows the table for the Food Stores, by number of facilities instead of by jobs. The output is read in the same manner as above. Going to the “Manufacturing” tab (shown in Figure 8), shows the table for accessibility to the number of “Manufacturing” jobs. For example, – Item 1 points to the T-score of -3.04 for the difference in “composite” accessibility within 45 minutes between the selected neighborhoods and all neighborhoods, for the 2000 Base scenario. Item 2 points to the T-score of -2.90 for the difference in “composite” accessibility within 45 minutes between the selected neighborhoods and all neighborhoods, for the Project scenario. Both scores show that the 45-minute “composite” accessibility to “Manufacturing” jobs is significantly lower for the selected neighborhoods than all neighborhoods.

1

2

Figure 8: Table of accessibility to “Manufacturing” jobs for neighborhoods with at least 40% African-American households Returning to the main accessibility calculator menu, we can also chose neighborhoods by income levels and automobile-ownership levels. These menus work in the same way that the demographic characteristic choice menus work. Again, the percent of households is chosen first, followed by the characteristic. In this example, we seek to analyze all neighborhoods with at least 50% of households below 200% of the federal poverty line.

38

Going to the “Manufacturing” tab (shown in Figure 9 below), shows the table for accessibility to the number of “Manufacturing” jobs. For example, – Item 1 points to the T-score of -3.69 for the difference in “composite” accessibility within 45 minutes between the selected neighborhoods and all neighborhoods, for the 2000 Base scenario. Item 2 points to the T-score of -3.10 for the difference in “composite” accessibility within 45 minutes between the selected neighborhoods and all neighborhoods, for the Project scenario. Both scores show that the 45minute “composite” accessibility to “Manufacturing” jobs is significantly lower for the selected neighborhoods than all neighborhoods.

1

2

Figure 9 Table of accessibility to “Manufacturing” jobs for neighborhoods with at least 50% households with incomes below 200% federal poverty line

Assessing Low Income and Minority Travel Behavior Motivation For a variety of reasons, it is likely that unique characteristics of this population’s transportation needs and patterns are not well known, and this is an important disadvantage when trying to better account for these needs in the transportation planning or funding process. Low income and minority populations often experience greater separation from jobs and other needed services and activities, as those activities increasingly shift in number and quality from the center city to outlying areas. Difficulty in accessing these opportunities is

39

magnified by lower rates of vehicle ownership and public transportation systems that are not well suited to serving reverse-flow travel patterns. This research combines a variety of survey data collection techniques into a single instrument to aim at better understanding the transportation challenges and needs of low income and minority individuals. In this project we identify categorical situations where data are insufficient for planning and decision-making purposes, and subsequently investigate and combine a different survey and market research approaches for obtaining the appropriate data into a single tool. The all in one survey data collection application technique enhances community understanding of transportation equity issues, provide useful data for transit agencies seeking community input, and further the efforts of the local participating groups to secure job training and internships for minority and low-income residents under the provisions of the 2005 SAFETEA-LU federal highway funding bill. The initial purpose of the questionnaire is to provide support to EJ communities that the project has connected with as well the random user. The BREJT Project and TECR Program provided an unique opportunity to explore survey approaches that are appropriate to particular problems in depth in one area (Baltimore), while obtaining original information on minority/low-income travel behavior and needs from a cross-section of metropolitan areas using a common survey instrument and administrative approach. It attempts to recognizes how low income and minority user of the public transportation infrastructure are more likely to think of their experience in terms of their overall time and accessibility, i.e., the total time too two arbitrary intermediate service points. The travel diary tool attempts to get a handle on “satisfaction during the trip along the arterial”. In terms of transit this would suggest (bus cleanliness, seat comfort, security cameras, degree of crowding (as measured by number of passengers on board or standing). And for highway this would measure usage (vehicle miles traveled VMT). It has often suggested the VMT is a rich folk performance measure. Thus addressing situations that range from sufficient representation of key population segments in regional household travel surveys to application of special methods to statistically ascertain unmet needs and the potential effectiveness of various alternative solutions, as well as measuring the impact of an existing or proposed transportation project or policy. Subsequently, this survey will be administered to a significant sample of households (or individuals) in the Baltimore region using a variety of administration media, ranging from electronic/web-based approaches to contact and personal administration through an intermediary, such as one of the community outreach organizations. The survey was intended a three pronged drill down process. From the Community Assessment the user would see three choices: Level 1 Neighborhood Profile Level 2 Travel Profile Level 3 Activity Based Travel Profile

Methodology The survey techniques were developed and tested in the Baltimore area the primary site for development of the Toolkit. In particular, a range of survey techniques were identified for

40

appropriate application to specific types of issues in Baltimore, ranging from comprehensive household travel surveys to more targeted market studies and needs assessments. Ongoing case studies already defined for the project served as test beds for testing this alternative survey technique. Each area, however, will test the application of the household travel survey and needs assessment, making use of the special capabilities represented in the outreach groups. This group-wide activity will support the overall objective of obtaining better base information on the travel characteristics and special needs of minority and low-income households, when viewed across a variety of settings. Subsequently, the techniques will be made available for review and possible application in the other participating areas. The survey methods developed and tested anticipate the full range of situations where improved market research data is desired. At the top of this list is the need for improved data on the travel behavior patterns and needs of low income and minority populations. The survey instrument obtains a reliable and in-depth profile of current travel behavior via a travel diary approach. The intent of this diary will be to capture the full schedule of trips, regardless of purpose or mode, made by all members of the household. Often, trips for non-essential purposes, or those made on foot, are imperfectly recorded in travel surveys. It is important to know about these trips when describing travel mobility, and it is also important to know about trips or destinations that could not be made due to limitations in travel options or affordability. We made an overt effort in designing this approach to ascertain “unmet needs”, employing a combination of objective assessments of accessibility to different types of activities, teamed with contextual information derived from the household which helps describe economic or other limitations to travel. The diary can be accessed at: http://www.brejtp.com/travel-diary . The form has been split up into 3 pages—the other questions have been moved to pages 2 and 3. The first page is the trip specs—time/cost/location information for each leg. The second page is questions about the whole trip, not a specific leg. The third page is general questions about the user, not about a specific trip. If the user already has a profile saved they can skip the last page, unless something about their profile has changed. The first page allows up to 10 legs of a trip to be added by clicking the “continued your trip” button, and the arrival location and time are prepopulated in the departure location and time of the next leg. This high-level survey is intended to fill some important gaps in our knowledge of the travel patterns and needs of the populations which are the subject of environmental justice concerns. A similar survey will then be administered in each of the other four BOSMP locations (Pittsburgh, Saginaw, Oakland and Minneapolis) drawing upon the team outreach capabilities at those sites. From this information, we should be able to begin to trace a more enlightened profile of the actual travel opportunities and constraints facing the low income traveler. Ideally, this is information that can be used to support both regional and national debate on travel needs, and help shape existing and upcoming planning and decision-making exercises in the respective areas.

41

Figure 9 Trip entry page

42

Figure 10 Personal Information Entry Page

Results: Sample Report Sample report that pulls profile and trip data from the database. It sorts by create data in descending order so you can use it to monitor new data as it comes in. There is also a link to

43

download it to Excel. The report is set up to show 20 rows at a time, but when you click the download to Excel link, you’ll get all the data. Table 6 Sample report that pulls profile and trip data from the database Variable Starting Location: , Departure Time: 8:00 AM Final Destination: , Arrival Time: 8:00 AM Trip Cost: $0.00 Trip Distance: 0 miles Method of Travel: train Trip Purpose: work Mobility Limitations: Traveling Alone? Intermediate Stops: Is auto travel cost affecting travel? Were transfers required? Was your travel time reasonable? Do you have any health related issues that affect your travel? Did the conditions of the streets and roads cause any problems? Is the transit usually on time and reliable? Are you able to reach essential services and recreational activities?

Auto, Public Transportation, Land Use and Public Health Performance Measures

Public Health Rationale as a Transportation Decision-Making Factor A primary consideration to be addressed by the toolkit is public health risk impacts that include accessibility and includes the recurring issue of service by transportation service providers including a discussion of services dedicated by medical condition vs. pooling of transportation resources and reservations/dispatching. This is a first and particularly important step in improving participation among traditionally underserved populations and it is equally important in helping to determining who these populations are, where they live and travel, how best to communicate with them and how best to serve them. We argue through the demonstration below that: •Public health has evolved out of a history recognizing and remedying health disparity and environmental justice. Some of the approaches and strategies used in public health are likely relevant here.

44

•Transportation has a significant impact on public health and there is strong evidence that the impact is disproportionate among communities of color that are socio economically disadvantaged. As summarized in the literature there is extensive proof which: 1) delineates the significant impact of traffic on community health and 2) provides evidence that the health burden is disproportionately born by communities of color that are socio-economically disadvantaged. Therefore, we can argue that public health is one of a couple primary considerations to be addressed in the toolkit. Other considerations include the impact of accessibility underserved citizens. Appropriately, these interests and views are represented both upstream down stream of transportation decisions. Figure 11 Underserved Comunities The goal of the current project is to present a practical approach, i.e. a toolkit, for providing underserved populations with greater influence over transportation decisions so that there interests are better served by those decisions (Figure 11). This is important since it is this population that is both disenfranchised from, and most strongly impacted by, the decision making process.

Background and Rationale Traffic-related air pollution has been implicated as a serious public health threat by a growing and increasingly convincing body of epidemiologic literature, which has linked traffic pollutant exposure with non-specific mortality (Friedman et al. 2001), cancer (Pearson et al. 2000; Knox 2005), and a variety of cardiovascular (Bigert et al. 2003) and respiratory effects (Friedman et al. 2001; Brunekreef et al. 1997; Wjst et al. 1993; Weiland et al. 1994). In addition, risks from this exposure are disproportionately borne by racial minority and socioeconomically disadvantaged subpopulations (Green et al. 2004; Apelberg et al. 2005; Gunier et al. 2003). While the adverse health consequences, epidemiology, and social disparities are already compelling, it is clear that further elucidation is necessary of the magnitude, chemical composition, and variability of human exposure, and source-to-effect mechanisms. Community exposure to a complex array of traffic-related pollutants is determined by vehicle volume, as well as varied emissions characteristics of vehicles, such as differing tailpipe emissions, heat soak, tire and brake wear, and road dust re-suspension. This underlying variability in emissions drives highly dynamic concentrations of traffic-related pollutants, which are further modified by meteorology, source proximity, and human time-activity patterns.

45

Automobiles and trucks are a major source of air pollution including such toxins and irritants as carbon monoxide (CO), nitrogen oxides (NO), volatile organic compounds (VOCs), particulate matter, and particle-bound polycyclic aromatic hydrocarbons (PAHs). In the urban environment, high-density traffic is brought in close proximity to densely populated communities. This is particularly true in some older East Coast cities like Baltimore where rowhouse neighborhoods are within a couple of meters of heavily traveled urban corridors. Environmental justice is a term used to describe the movement concerned with inequities in the distribution of adverse environmental and health consequences of industrial activities and environmental policies (U.S.EPA 2004a). The movement grew from early observations that a seemingly unequal burden of pollution fell on disenfranchised and disadvantaged communities, often characterized by lower incomes and high proportions of minorities (Brown 1995). With the issuance of Presidential Executive Order 12898 in 1994, achieving “environmental justice” was integrated into the missions of all federal agencies (Clinton 1994). The U.S. Environmental Protection Agency (EPA) defines environmental justice to mean that “no group of people, including a racial, ethnic, or a socioeconomic group” should be disproportionately affected by “industrial, municipal, and commercial operations or the execution of federal, state, local, and tribal programs and policies” (U.S.EPA 2004a). There is ample evidence that minority and low-income communities bear a disproportionate burden of exposure to many environmental contaminants (Brown 1995; Institute of Medicine 1999), including air pollution (Samet et al. 2001; Schweitzer and Valenzuela 2004). Because nationwide ambient monitoring data are available for the criteria air pollutants (carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide), we have some means for assessing exposure and risk in disadvantaged and minority communities. However, considerably less is known about the distribution of exposure to and risk from the wide range of hazardous air pollutants (HAPs or “air toxics”) identified by Congress in the Clean Air Act Amendments (1990), because nationwide ambient monitoring is not possible due to the sheer number of pollutants and their diverse chemical properties (Caldwell et al. 1998; MorelloFrosch et al. 2000; Woodruff et al. 1998). A recent analysis of modeled national estimates suggests that ambient concentrations of HAPs exceed benchmark risk levels for cancer and non-cancer endpoints in many areas of the country (Caldwell et al. 1998; Woodruff et al. 1998; Woodruff et al. 2000). Furthermore, several recent studies have documented a disproportionate burden of air toxics exposure and/or risk falling on minority and low-income populations. These studies have included varying sources of exposure, including high traffic density (Green et al. 2004; Gunier et al. 2003), location of Toxic Release Inventory (TRI) and other treatment, storage, and disposal facilities (MorelloFrosch et al. 2002; Pastor et al. 2001; Perlin et al. 2001), and modeled estimates from EPA’s CEP (Lopez 2002; Morello-Frosch et al. 2002). Given the compelling evidence of a health threat that is exacerbated by environmental injustice, we have developed a strategy for identifying communities at risk using available public data. The identification of such communities is a necessary first step to empower communities, design epidemiological studies to further elucidate the threat, and implement intervention studies to address the threat.

46

Assessing Transportation-Related Health Risk in Baltimore, Maryland As discussed earlier, the impact of transportation on public health is a growing concern in many metropolitan areas world-wide. Here we focus on Baltimore, Maryland a major metropolitan area in the United States to illustrate how existing information can be used in identifying communities at risk. Baltimore is typical of many old large east coast cities with a housing stock that is dominated by row homes built in close proximity to busy urban arterial roadways. In this section we describe existing relevant data and their analysis for the purpose of identifying communities at risk from transportation-related air pollution. Using these existing data, we have developed a risk index to identify communities at risk due both to socio-economic status and proximity to traffic emissions.

Data Central to identifying communities at risk is the acquisition and analysis of data sources that can be used to derive indicators known to influence community exposure to transportation activities. Several such indicators include proximity of residential locations to transportation infrastructure (and related activity) as well as some measure of socio-economic status. Increased proximity to transportation activity is thought to increase health risk posed by transportation while decreased socio economic status is thought to increase risk of health impacts to effected populations. Several types of geographic information are required for calculation of these risk indicators. First, one must account for the location of residential locations as well as how they are to be represented in the analysis. Residential locations can be represented as either the sites of individual buildings (provided the availability of this data) or as some aggregation of residential locations such as a zip code area, census block group or a transportation analysis zone (TAZ). In this application, a geographic information system (GIS) database containing building footprints for all areas within the city of Baltimore was obtained from (2000). To compliment the building footprints, another GIS dataset detailing the location, extent, and land use for land parcels in the city was also obtained from (2000). Second, geospatial information on the location and usage of transportation infrastructure is needed. In this case, the regional planning transportation network from (2005) was used to facilitate this task. This planning network was developed to model and assign transportation activity between TAZs in the region and as such, various types of inter-TAZ traffic for the year 2005 had been already assigned to road segments in the network. That is, the volume of cars and trucks using each road segment in the network had already been estimated and attributed to each segment by (2005). Finally, socio-economic information (e.g., race, median income, education, etc.) on the study area’s population must be known to characterize socio-economic status. Here, year 2000 median income for each U.S. Census block group was used as a proxy for socio-economic status. Block group data was selected since it is the smallest spatial unit for which census tabulations of household income data are available. Methodology Two indicator variables are used to characterize community health risk due to traffic exposure: 1) proximity and 2) socio-economic status. One way of assessing proximity to traffic is to compute the total vehicle miles (# vehicles on road segment*length of road segment) within a

47

threshold distance (e.g., 300 feet) of a residential structure or within a tabulation area (e.g., TAZ). As discussed earlier, socio-economic status can be represented in a variety of ways (e.g., median household income). Thus, using these two basic indicators of health risk associated with traffic exposure, a simple index (RIi) can be computed representing the level of risk associated with each residence or a tabulation area (i.)

RI i  TVMTi  SES i

(1)

Where, i = unit of analysis (e.g., building or tabulation area) RIi = Risk index for unit i TVMTi = Total Vehicle Miles Traveled (# of vehicles * length of road segment) within some proximity threshold of unit i SESi = Socio-economic status for unit i Obviously, an issue with this index is that both variables are of a ratio nature, thus it is necessary to convert them to ordinal measurements to facilitate their integration and comparison. One way to accomplish this is to consider the range associated with each variable and split the range into bins of equal intervals. Thus, both TVMT and SES variables can be split into 10 intervals of equal size indexed 1 through 10, such that a value of 1 indicates the least risk and a value of 10 indicates the greatest level of risk. For example, those residences with the lowest levels of TVMT would be assigned a value of 1 while those with the highest TVMT would be assigned a value of 10. Similarly, those residences with the highest levels of SES would be assigned a value of 1 while those with the lowest SES would be assigned a value of 10. Therefore, computing RIi using the transformed ordinal variables results in a RIi with values ranging between 2 (lowest TVMT and highest income) and 20 (highest TVMT and lowest income). TransCAD, a GIS specifically oriented toward the analysis of transportation data was used to facilitate analyses of the datasets discussed above to compute the components of the risk index. Although TransCAD is primarily oriented toward transportation analysis, it is also well suited as a general purpose GIS and, hence, has proven useful in addressing broad and diverse research questions such as those involved in deriving meaningful discoveries related to traffic, health, and environmental justice. First, TransCAD was used to derive a variable indicating proximity to transportation activity. Proximity for each residential building is defined here as all transportation activity falling within 200 feet of the building. Proximity to transportation was derived using the following GIS methodology: a. Select buildings falling within city parcels denoted as residential. b. Calculate vehicle miles traveled (VMT) for each road segment in the transportation network. Here, VMT relates to daily vehicle (cars and trucks) use of a road segment. c. Generate a 200 foot buffer for each residential building polygon.

48

d. Overlay buffer polygons with transportation network to compute the total VMT (TVMT) falling within 200 feet of each residential building. Again, it should be noted that while residential buildings were used in this analysis, larger spatial units of analysis could also be used as well. For instance, one could use TAZs and compute an overlay with the transportation planning network as is done above with each building and compute total VMT for each TAZ. Next, TransCAD was used to attribute each residential building in the building footprint dataset with the median income of the census block group the building falls within. This measure of socio-economic status was integrated with the building data by first attributing each residential building polygon with the median income of the census block group in which the building’s centroid is located.

Results The environmental justice risk modeling was applied to four Baltimore communities (Cherry Hill, Federal Hill, Kirk Avenue, and Highway to Nowhere) to exemplify it as a tool as a part of the Baltimore Region Environmental Justice in Transportation Project (BREJTP).

Figure 12. Risk index applied to Cherry Hill

49

Figure 13 Risk index applied to Federal Hill

50

Figure 14. Risk index applied to Kirk Ave

51

Figure 15. Risk Index applied to "Hightway to Nowhere

These maps identify building-level “hot spots” within communities where it would be reasonable to hypothesize that individuals are at risk due to the combined influence of low SES and proximity to high levels of traffic. This index is effective in differentiating neighborhoods that are both socio-economically disadvantaged and in close proximity to busy roadways. As would be expected, it can be observed that the highest index values are associated with homes in close proximity to highways and busy urban arterials (Figure 14), however, this influence and risk can be offset by blocks with high income (Figure 15). We recognize that SES and roadway proximity are not independent. The scatter plot in Figure 49 illustrates the relationship between median income and proximity to transportation activity for households in Baltimore. As hypothesized, the plotted relationship between these two variables does appear to indicate a general trend whereby traffic exposure is decreasing with increasing income. 52

Figure 16. Scatter plot of vehicle miles within 200 feet of Baltimore residences in relation to median household income.

Now that TVMT and SES have been generated for each building, the ratio data can be converted to ordinal measurements and the risk index RIi for each building can then be computed. Figure 6 shows a scatterplot of the relationship between the new ordinal measurements of median household income and proximity to transportation activity. In this case, smaller values for income represent groups of households with higher median income levels. Smaller vehicle mile values indicate households with lower proximity to transportation activity. Thus, those households in higher vehicle mile intervals and in higher median income intervals are those at greatest risk for transportation health related impacts.

Figure 17. Scatter plot of ordinal transformation of transportation and socio economic status indicators for Baltimore households

53

Discussion Although there is strong evidence that proximity to heavily trafficked roadways and SES conspire to place communities at risk for health threats that range from cardiovascular disease to cancer, research is needed to fully elucidate the nature of the risk and develop strategies mitigation. The current analysis provides a strategy for identifying communities at risk. This strategy relies on publicly available data combined using a GIS platform that accommodates assessment of both community-level traffic and demographics. Using this platform, we developed a risk index that accounts for factors known to place communities at risk including level of traffic, roadway proximity, and SES. The identification of such communities using this approach is a necessary first step to fully evaluating the risk and developing strategies for mitigating that risk. Furthermore, this analysis tool can be used to empower communities.

Vehicle Miles of Travel Drill-down Analysis Over the last many decades, vehicle miles traveled has increased significantly in the U.S. There are numerous contributing factors to this rise, including the rise in real income, sprawl and expansive land uses, the rise in car ownership, historically lower gas prices (until recent years), the rise in population, and so on. Among these factors, such factors as income and population are still projected to increase, and are expected to further contribute to VMT increases in the future. Table 7: Total Vehicle Miles Traveled

Projected increases in VMT have a number of implications, ranging from infrastructural investment to accommodate rising VMT, to environmental pollution, health effects and

54

environmental justice as a result of the nature of the spatial distribution of pollution. In the Baltimore metropolitan area, the pollution implication of rising VMT is indicated in the table below. From highway to local roads, rising VMT has numerous contributions to pollution, and some places could have disproportionately more environmental and health effects than others. Given that most-low income families are concentrated in dense and urban areas, the distributional effect of VMT related pollution on low-income communities is particularly an environmental justice concern. Table 8: Baltimore Area VMT and Emission Summary by Functional Class

The main goal of this brief report is to: (1) identify some of the factors that determine the level of VMT and its growth overtime in the Baltimore metropolitan area and the study areas of Kirk, Lexington Market, Highway-to-Nowhere, and the I-95 Corridor. These study areas constitute a significant concentration of low income and minority communities, hence understanding of the factors that determine the growth rate of VMT will provide insight into the potential implication to these case study area low-income and minority communities; (2) to assess growth patterns in income, population, households and workers in the Baltimore metropolitan region and the study areas; and (3) to provide a scenario analysis of how projected growth indicated in (2) are projected to affect VMT levels in the future. 2. VMT Generation – What Influences VMT Trends? VMT generation by a transportation zone is an important indicator that can provide information on such factors as: (1) the distribution of VMT generation in the Baltimore metropolitan region and specific study areas; (2) association of VMT with environmental pollution and other social costs; (3) the distribution of externalities (environmental and other unaccounted costs to society) as a result of such distribution and environmental justice implications; and (4) an understanding of the relationship between zonal VMT distribution

55

and its association with other socioeconomic factors that can potentially explain VMT generation. The above questions have significant implications to environmental justice related to transportation system design and resultant distribution of pollutants across communities. This section focuses on analysis of the above mentioned four factors based on a regional and specific case study area analysis. A regional analysis sheds some light on VMT generation across transportation zones in a region and perhaps the factors that explain observed regional VMTs. The study-area-specific information sheds light on VMT generation in specific areas, and how they compare with the overall regional pattern. Such observation can inform on unique VMT patterns and their implication to environmental justice concerns.

Understanding VMT – What Influences VMT Trends VMT generation by a transportation zone is an important indicator that can provide information on such factors as: (1) the distribution of VMT generation in the Baltimore metropolitan region and specific study areas; (2) association of VMT with environmental pollution and other social costs; (3) the distribution of externalities (environmental and other unaccounted costs to society) as a result of such distribution and environmental justice implications; and (4) an understanding of the relationship between zonal VMT distribution and its association with other socioeconomic factors that can potentially explain VMT generation. The above questions have significant implication to environmental justice related to transportation system design and resultant distribution of pollutants across communities. This section focuses on analysis of the above mentioned four factors based on a regional and specific case study area analysis. A regional analysis sheds some light on VMT generation across transportation zones in a region and perhaps the factors that explained observed regional VMTs. The study-area-specific information sheds light on VMT generation in specific areas, and how they compare with the overall regional pattern. Such observation can inform on unique VMT patterns and their implication to environmental justice concerns. The focus of this section is particularly to address the following questions: 1. 2. 3. 4. 5.

What factors determine differences in regional VMT generation? How are case study area VMTs different from the overall region? Do low income communities generate more or less VMT? Do high population (households) communities generate more VMT? What type of economic activity (retail, office, industrial, etc) has more propensity to contribute to VMT?

Based on the above five questions, the analysis can inform on the policy implications and environmental justice considerations.

56

VMT Reduction – Econometric Model To address the goals specified in section 1, it is important to develop an econometric model that helps answer the 5 indicated questions. To do so, a model that relates VMT at the regional and case study level with other relevant socioeconomic factors at the transpiration zone level is important. For the regional analysis, the following model can be specified:

VMTi   0  1TPOP   2 D( Low _ Inc)  3 D(Med _ Inc)   4 Re t  5Off  6 Ind   i (1) VMT refers to the vehicle mile travels for communities in the region (i), the parameters

 0 to  6 are values to be estimated to indicate the relationship between variables and the

dependent variable (VMT), TPOP refer to zonal total population, Low_Inc and Med_Inc refer to low income transportation zone (if median income is below $25,000) and medium income transportation zone (if median income is between $25,000 and $75,000), Ret refers to zonal retail employment, Off refers to zonal office employment, and Ind. refers to zonal industrial employment. Note that the D indicates a dummy variable, i.e., the D for low income, for instance, identifies transportation zones that are identified only as low income (in that case will have a value of 1), otherwise will carry a value of 0 for zones that are not classified as low income. An econometric model that investigates the relationship of case study VMT patterns with the regional VMT patterns can be specified as:

VMTi  0  1TPOP   2 D( Low _ Inc)  3 D(Med _ Inc)   4 Re t  5Off  6 Ind 

7 D(Ch _ Hill)  8 D( Kirk)  9 D( Lex _ Mkt)  10 D( Hwy _ to _ NW )   i

(2)

Where all variables remain as defined previously, and D(Ch_Hill) refers to transportation zones in Cherry Hill study area, Kirk refers to transportation zones in Kirk study area, D(Lex_Mkt) refers to transportation zones in Lexington Market study area, and D(Hwy_to_NW) refers to Highway-to-Nowhere study area. Estimation of equations (1) and (2) provides answers to questions (1) through (5) in the previous section. The above two models are estimated by using an Ordinary Least Squares method. In estimating these models, econometric tests are conducted, and corrected when needed, for basic econometric problems in cross-sectional data.

Steps Followed to Generate Estimations (Results) Section 3 provides a model that can help understand the relationships between zonal VMT and other socioeconomic factors, such as zonal median household income, number of workers, and population (households). Such analysis would provide information on the relationship between these factors and VMT generation, and consequently potential policy implications to manage regional and local VMT.

57

To conduct the analysis based on the model indicated in section 3, the following steps are followed: 

First, data on VMT by transportation zone is collected. Then, data on socioeconomic factors, such as income, population and employment levels are matched with VMT data for each transportation zone.



Next, data is identified both for the entire transportation region and for specific case study areas through an identification variable, called a dummy variable. A dummy variable is a 0 and 1 code that labels 1 if the data is for a specific case study area and 0 otherwise. This helps identify regional and case study data.



Next, the data is checked for completeness and consistency. This data is then brought to LIMDEP econometric software to estimate the relationship between VMT and other socioeconomic variables (factors).



Finally, the result is summarized and presented for analysis.

Based on the model in section 3 and the steps outlined in this section, the results from the analysis are provided in section 4. Note that section 4 also includes the following additional analysis: 

Detailed examination of the relationship between income and VMT generation. First, the relationship between general income and VMT is considered. Next, to examine the impact of different income classes on VMT, median zonal income is categorized into low income, medium income and high income categories. Low income zone is defined as one where the medium income is below $20,000 per year. Medium income zones are defined as on where income is between $20,000 and $75,000 per year. High income zones are defined as those with income above $75,000 per year. This helps analyze the role of income in VMT determination by income class. 

Detailed examination of shifts in VMT generation by case study area compared to the overall region. To analyze whether case study areas have a distinct VMT generation pattern compared to the overall transportation region, a case study comparison with the region is conducted.

Analysis of results is presented next in section 4.

Regional Impact Analysis Focusing first on regional transportation zones, the analysis considered 1,123 transportation zones in the region. Consider, first, a basis analysis that explains differences in VMT generation across transportation zones based on zonal differences in total population, median income, and retail, office, and industrial employments. Econometric results are provided in the table below.

58

The following key results are observed: 

Transportation zones with more population generate significant amount of VMT. The result was highly statistically significant. For every 1 person difference across zone, VMT generation differs by 529.



Median income has a statistically significant impact on VMT generation. Zones with higher median income generate more VMT. For every $1 rise in median income in a zone, zonal VMT is expected to increase by 8.68.



In terms of employment types, the result suggests that retail businesses contribute significantly higher VMT, followed by office employment. However, zones with predominantly industrial employment have a lower VMT compared to non-industrial employment zones. Additional retail job difference across zones is related to a corresponding VMT difference of 2,835. The number is 2.592 for office jobs.

Table 9: Regional Vehicle Miles Traveled Analysis Regression Results – Regional Analysis Variable Description of Variable

Coefficient

Constant TPOP MEDINC RETAIL OFF IND

Intercept of the model Total Population Medina Income Retail workers Office Workers Industrial Workers

-2,154,054.75 528.97 8.68 2,834.83 2,591.62 -2,894.17

R-Squared

66%

P-Value (% of error) 0.00 0.00 0.00 0.00 0.00 0.00

The model’s R2 (predictive ability) is 66%. For limited number of variables considered in the analysis, the result is robust. One important question at this juncture is whether low income communities contribute higher or lower VMT. This questions has significant implications to environmental justice concerns. By estimating equation (1) with further categorized income data, the result from the econometric estimation is provided in the table below. The results can be summarized as follows: • As discussed in the previous result, transportation zones with higher population and retail and office jobs have significantly higher VMT generation. • Focusing on the impact of income on VMT generation, the result is interesting. Two income groups are included in the estimation (low and medium income) and high income group is excluded to hold it as a comparison group. The result suggests that while low income transportation zones have significantly lower VMT generation, there is no much difference

59

between medium and high income zones. The implication of this finding at the regional level is that low income areas generate low VMT, but if they are located close to medium or high income areas, they will experience more VMT generated from outside their region. The environmental justice implication across income classes is thus clear from this finding.

Table 10 Regional Analysis with Income Classes Regression Results – Regional Analysis with Income Classes Variable Description of Variable Coefficient Intercept of the model Constant -1,419,003.71 Total Population TPOP 519.86 Low Income Zones Low_Inc -877,516.29 Middle Income Zones Med_Inc -113,556.01 Retail workers RETAIL 2,800.33 Office Workers OFF 2,596.57 Industrial Workers IND -2,918.59 R-Squared 66%

P-Value 0.00 0.00 0.00 0.50 0.00 0.00 0.00

The model’s R2 (predictive ability) is 66%. Again for limited number of variables considered in the analysis, the result is robust.

Low Income and Minority Community Area Analysis This section focuses on analyzing the VMT generation by zone by specifically separating attributes of the following case study areas: Cheryl Hill (TAZ codes 871, 875, 877, 864, and 335), Kirk (TAZs 432, 477, and 935), Lexington Market (TAZs 970, 969, and 968) and Highway-tonowhere (TAZs 122, 117, 116, 133, 1005, 1038, 1061, 1072, 1050, 1027, 1016, 1127, 1116 and 1105). For the purpose of identifying VMT generation comparison between the region and the case study areas, model (2) is utilized. The econometric analysis results for case study specific areas are provided in the table below.

60

Table 11: All Case Study Areas vs. Region. Regression Results – All Case Study Areas Compared to the Region Variable Description of Variable Coefficient Intercept of the model Constant -13,837,666.98 Cherry Hill CH-HILLD 246,559.85 Kirk KIRK_D 107,785.10 Lexington Market LEX_D 156,236.96 Highway-to-Nowhere HTONW_D -204,399.57 Total Population TPOP 514.88 Low Income Zones Low_Inc -892,417.26 Middle Income Zones Med_Inc -127,104.20 Retail workers RETAIL 2,790.67 Office Workers OFF 2,611.99 Industrial Workers IND -2,949.56 R-Squared 67%

P-Value 0.00 0.81 0.94 0.91 0.01 0.00 0.00 0.45 0.00 0.00 0.00

The results suggest the following: 

All of the previous regional analysis still holds when the data is separated between the region and case study areas. The fact that transportation zones that have more population, more retail and office jobs, and middle and higher income generate more VMT is still confirmed in this model.



In terms of VMT differences by geographic location, the result suggests that Cherry Hill, Kirk, and Lexington Market do not have remaining systematic difference with regional VMT once we account for income and population differences across zones. However, even after accounting such factors, the Highway-to-Nowhere case study area still has significantly lower zonal VMT generation.



The results overall suggest that socioeconomic factors have more explanatory power than location differences. Thus, low income and low population areas have lower VMT generation, but if they are located close to high income and high population areas, they may experience larger VMT from outside their zone.

The model’s R2 (predictive ability) is 67%. Again for limited number of variables considered in the analysis, the result is robust. The analysis is repeated by testing each case study area individually. The results were the same as above. The results are provided below:

61

Table 12: Cherry Hill Study Area Growth Patterns Cherry Hill Study Area Growth Patterns TAZ 877

TAZs

TAZ 875 TAZ 871 TAZ 864 TAZ 335 0

20000

40000

60000

80000

100000

120000

TPOP TPOP1 TPOP2 HH HH1 HH2 MEDINC MEDINC1 MEDINC2 WORKERS WORKERS1 WORKERS2

Table 13 Econometric Analysis Results for Cherry Hill Regression Results – Cherry Hill Compared to the Region Variable Description of Variable Coefficient Intercept of the model Constant -1420616.05 Cherry Hill CH-HILLD 264496.58 Total Population TPOP 520.11 Low Income Zones Low_Inc -876808.03 Middle Income Zones Med_Inc -114319.58 Retail workers RETAIL 2801.22 Office Workers OFF 2596.74 Industrial Workers IND -2919.15 R-Squared

P-Value 0.00 0.81 0.00 0.00 0.49 0.00 0.00 0.00

66%

The results suggest the following:

 

Similar to previous findings, high population, retail and office jobs expansion, and higher income zones generate higher VMT. The evidence suggests that Cherry Hill case study area does not have a significantly different VMT generation compared to other zones in the region after accounting for the impact of income, population and job differences in the region.

Scenario Analysis Cherry Hill is expected to have the following growth patterns from 2005 to 2015 and 2030:

 62

Population is expected to grow by 718 between 2005 and 2015, and by 1,376 from 2015 to 2030.

   

Median income is expected to increase by $3,601 between 2005 and 2015 and by $14,924 from 2015 to 2030. The number of retail workers is expected to increase by 52 between 2005 and 2015 and by 228 from 2015 to 2030. The number of office workers is expected to increase by 343 between 2005 and 2015 and by 897 from 2015 to 2030. The number of industrial workers is expected to increase by 124 between 2005 and 2015 and by 228 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using the regional regression estimates. These estimates provide the following relationships: VMT = 529*POPULATION + 9*INCOME + 2,835*RETAIL + 2,592*OFFICE – 2,894*INDUSTRY By utilizing the above estimation equation and utilizing the average estimated impact for growth of workers, the following VMT scenario can be predicted for Cherry Hill:

 

From 2005 to 2015: VMT is expected to rise by 287,229. From 2015 to 2030: VMT is expected to rise by 901,172.

Table 14 Kirk Ave. Bus Depot Study Area Growth patterns: Kirk Study Area Growth Patterns TPOP TPOP1 TPOP2

TAZs

TAZ 935

HH HH1

TAZ 477

HH2 MEDINC MEDINC1 MEDINC2

TAZ 432

WORKERS WORKERS1 0

20000

40000

60000

80000

100000

120000

140000

160000

WORKERS2

63

Table 15 Econometric Analysis for Kirk Ave Regression Results – Kirk Compared to the Region Variable Description of Variable Coefficient Intercept of the model Constant -1,419,572.91 Kirk Kirk_D 125,611.37 Total Population TPOP 519.89 Low Income Zones Low_Inc -877,127.41 Middle Income Zones Med_Inc -113,512.19 Retail workers RETAIL 2,800.58 Office Workers OFF 2,596.58 Industrial Workers IND -2,918.44 R-Squared 66%

P-Value 0.00 0.84 0.00 0.00 0.50 0.00 0.00 0.00

The results suggest the following:



Similar to previous findings, high population, retail and office jobs expansion, and higher income zones generate higher VMT.



The evidence suggests that Kirk case study area does not have a significantly different VMT generation compared to other zones in the region after accounting for the impact of income, population and job differences in the region.

Scenario Analysis Kirk is expected to have the following growth patterns from 2005 to 2015 and 2030:

 

Population is expected to grow by 125 between 2005 and 2015, and by 116 from 2015 to 2030. Median income is expected to increase by $3,818 between 2005 and 2015 and by $15,788 from 2015 to 2030.



The number of retail workers is expected to increase by 2 between 2005 and 2015 and by 10 from 2015 to 2030.



The number of office workers is expected to increase by 8 between 2005 and 2015 and by 15 from 2015 to 2030.



The number of industrial workers is expected to increase by 5 between 2005 and 2015 and by 6 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using the regional regression estimates. These estimates provide the following relationships:

64

VMT = 529*POPULATION + 9*INCOME + 2,835*RETAIL + 2,592*OFFICE – 2,894*INDUSTRY By utilizing the above estimation equation and utilizing the average estimated impact for growth of workers, the following VMT scenario can be predicted for Cherry Hill:



From 2005 to 2015: VMT is expected to rise by 93,700.



From 2015 to 2030: VMT is expected to rise by 218,219.

Table 16: Lexington Market Study Area Growth Patterns Lexington Market Growth Patterns TPOP TPOP1

TAZ 970

TPOP2

TAZs

HH HH1

TAZ 969

HH2 MEDINC MEDINC1 MEDINC2

TAZ 968

WORKERS WORKERS1 0

20000

40000

60000

80000

100000

120000

140000

WORKERS2

Table 17 Econometric Analysis for Lexington Market Regression Results – Lexington Market Compared to the Region Variable Description of Variable Coefficient Intercept of the model Constant -1,419,677.75 Lexington Market LEX_D 170,482.44 Total Population TPOP 519.84 Low Income Zones Low_Inc -876,989.41 Middle Income Zones Med_Inc -113,478.70 Retail workers RETAIL 2,800.55 Office Workers OFF 2,596.65 Industrial Workers IND -2,918.27 R-Squared 66%

P-Value 0.00 0.89 0.00 0.00 0.49 0.00 0.00 0.00

The results suggest the following:



Similar to previous findings, high population, retail and office jobs expansion, and higher income zones generate higher VMT.

65



The evidence suggests that Lexington Market case study area does not have a significantly different VMT generation compared to other zones in the region after accounting for the impact of income, population and job differences in the region.

Lexington Market is expected to have the following growth patterns from 2005 to 2015 and 2030:



Population is expected to grow by 135 between 2005 and 2015, and by -39 from 2015 to 2030.



Median income is expected to increase by $3,994 between 2005 and 2015 and by $16,528 from 2015 to 2030.



The number of retail workers is expected to increase by 20 between 2005 and 2015 and by 36 from 2015 to 2030.



The number of office workers is expected to increase by 38 between 2005 and 2015 and by 30 from 2015 to 2030.



The number of industrial workers is expected to increase by 9 between 2005 and 2015 and by 7 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using the regional regression estimates. These estimates provide the following relationships: VMT = 529*POPULATION + 9*INCOME + 2,835*RETAIL + 2,592*OFFICE – 2,894*INDUSTRY By utilizing the above estimation equation and utilizing the average estimated impact for growth of workers, the following VMT scenario can be predicted for Cherry Hill:

 

66

From 2005 to 2015: VMT is expected to rise by 147,065. From 2015 to 2030: VMT is expected to rise by 217,480.

Table 18: Highway-to-Nowhere Study Area Growth Patterns Highway-to-Nowhere Growth Patterns TAZ 1127 TAZ 1116 TAZ 1105

TPOP TPOP1 TPOP2 HH HH1 HH2 MEDINC MEDINC1 MEDINC2 W ORKERS W ORKERS1 W ORKERS2

TAZ 1072 TAZ 1061

TAZs

TAZ 1050 TAZ 1038 TAZ 1027 TAZ 1016 TAZ 1005 TAZ 133 TAZ 122 TAZ 117 TAZ 116 0

20000

40000

60000

80000 10000 0

12000 0

14000 0

16000 0

The econometric analysis results for Lexington Market case study area are provided in the table below. The results suggest the following:



Similar to previous findings, high population, retail and office jobs expansion, and higher income zones generate higher VMT.



The evidence suggests that Highway-to-Nowhere case study area does have a significantly lower VMT generation compared to other zones in the region, even after accounting for the impact of income, population and job differences in the region.

Table 19 Econometric Analysis Lexington Market Regression Results – Highway-to-Nowhere Compared to the Region Variable Description of Variable Coefficient Intercept of the model Constant -1,381,123.86 Highway-to-Nowhere HTONW_D -2,045,794.10 Total Population TPOP 514.63 Low Income Zones Low_Inc -893,908.73 Middle Income Zones Med_Inc -126,512.48 Retail workers RETAIL 2,789.42 Office Workers OFF 2,611.75 Industrial Workers IND -2,949.49 R-Squared 67%

P-Value 0.00 0.01 0.00 0.00 0.44 0.00 0.00 0.00

67

Highway-to-Nowhere is expected to have the following growth patterns from 2005 to 2015 and 2030:



Population is expected to grow by 2,795 between 2005 and 2015, and by 5,003 from 2015 to 2030.



Median income is expected to increase by $2,549 between 2005 and 2015 and by $10,599 from 2015 to 2030.



The number of retail workers is expected to increase by 85 between 2005 and 2015 and by 267 from 2015 to 2030.



The number of office workers is expected to increase by 58 between 2005 and 2015 and by 2,800 from 2015 to 2030.



The number of industrial workers is expected to increase by 356 between 2005 and 2015 and by 1,065 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using the regional regression estimates. These estimates provide the following relationships: VMT = 529*POPULATION + 9*INCOME + 2,835*RETAIL + 2,592*OFFICE – 2,894*INDUSTRY By utilizing the above estimation equation and utilizing the average estimated impact for growth of workers, the following VMT scenario can be predicted for Cherry Hill:

68



From 2005 to 2015: VMT is expected to rise by 953,620.



From 2015 to 2030: VMT is expected to rise by 1,122,049.

Table 20: Sensitivity Analysis Changes in Socioeconomic Factors on VMT (2005-2030) Study Area

Factors Considered Years 2005-2015

Cherry Hill

Kirk

Lexington Market

Highway-toNowhere

Total Population Change Change in Median Income Change in Retail Workers Change in Office Workers Change in Industrial Workers Total Population Change Change in Median Income Change in Retail Workers Change in Office Workers Change in Industrial Workers Total Population Change Change in Median Income Change in Retail Workers Change in Office Workers Change in Industrial Workers Total Population Change Change in Median Income Change in Retail Workers Change in Office Workers Change in Industrial Workers

VMT Impacts Years 2016-2030

287,229

901,172

93,700

218,219

147,065

217,480

953,620

1,122,049

I-95 Corridor Analysis Focusing on the I-95 Corridor transportation zones, a separate econometric analysis is conducted to project VMT in this corridor. The analysis followed similar procedure as in the above discussed cases. VMT generation is, among other things, determined by growth in population, income, and job opportunities in a region. Projected growth in of each of these growth factors in the I-95 Corridor is reflected in the graphs below. From the analysis of the impact of growth in population, income and jobs on VMT increases, the following key results are observed: 

Population in I-95 corridor is positively related to VMT generation. The result was highly statistically significant. For every 1 additional people in the zone, VMT is expected to increase by 331.



Median income did not have a significant relationship with VMT in this Corridor.



In terms of employment types, the result suggests that office workers significantly higher VMT, followed by retail workers. However, zones with predominantly industrial employment have lower VMT. For every additional job in the office and retail sectors, VMT is expected to increase by 5,949 and 4,246 respectively.

69

Table 21: Regression Results – I-95 Corridor

Variable Constant TPOP INCOME RETAIL OFF IND R-Squared

Regression Results – I-95 Corridor Description of Variable Coefficient Intercept of the model -911,965.59 Total Population 331 Median Household Income -2.89 Retail workers 4,246 Office Workers 5,949 Industrial Workers -10,980 99%

P-Value 0.10 0.00 0.66 0.00 0.00

The model performance is indicated by the R-squared, which stands at 99%. This indicates a robust model performance. Scenario Analysis I-95 Corridor is expected to have the following growth patterns from 2005 to 2015 and 2030: 

Population is expected to grow by 12,433 between 2005 and 2015, and by 20,707 from 2015 to 2030.



Median income is expected to increase by $3,707 between 2005 and 2015 and by $15,373 from 2015 to 2030.



The number of retail workers is expected to increase by 1,378 between 2005 and 2015 and by 3,996 from 2015 to 2030.



The number of office workers is expected to increase by 4,401 between 2005 and 2015 and by 21,587 from 2015 to 2030.



The number of industrial workers is expected to increase by 958 between 2005 and 2015 and by 3,105 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using the regional regression estimates. These estimates provide the following relationships: VMT = 331*POPULATION + 4,246*RETAIL + 5,949*OFFICE – 10,980*INDUSTRY By utilizing the above estimation equation and utilizing the average estimated impact for growth of workers, the following VMT scenario can be predicted for Cherry Hill:   70

From 2005 to 2015: VMT is expected to rise by 25,629,020. From 2015 to 2030: VMT is expected to rise by 118,149,196.

Table 22: I-95 Corridor Study Area Growth Patters

I-95 Corridor Study Are a Growth Patte rns

873 872 860 859 857 856 855 853 852 850 847 846 351 RETAIL2 RETAIL1

350

RETAIL IND2

349

IND1 IND

TAZs

348

OFFICE OFF1

347

OFF MEDINC2

346

MEDINC1 MEDINC

337

TPOP2 TPOP1

336

TPOP TAZ

335 334 333 332 331 330 329 328 327 326 325 324 323 0

20000

40000

60000

80000

100000 120000 140000

71

I-95 Corridor Stuy Area Growth Patterns

1106 1022 1021 1020 1019 1015 1014 1013 1012 1011 1010 1009 1008 1007 1006 978

RETAIL2 RETAIL1

973

RETAIL IND2

956

IND1 IND

TAZs

955

OFFICE OFF1

953

OFF MEDINC2

952

MEDINC1 MEDINC

951

TPOP2 TPOP1

950

TPOP TAZ

924 897 896 895 894 893 892 891 890 886 883 881 879 878 877 874

72

0

50000

100000

150000

200000

Summary This report focused on understanding the relationship between VMT generation of transportation zones and the potential association with other socioeconomic characteristics inherent to such zones. The report focused on the following key questions that would have implications to environmental justice: 1. 2. 3. 4. 5.

What factors determine differences in regional VMT generation? How are case study area VMTs different from the overall region? Do low income communities generate more or less VMT? Do high population (households) communities generate more VMT? What type of economic activity (retail, office, industrial, etc) has more propensity to contribute to VMT?

To answer the above questions, a model that lings VMT to key socioeconomic variables, such as income, population and employment is developed. Based on data gathered for each transportation zone in the region, the relationship between VMT and the factors that generate VMT are tested and analyzed. In general the results of this work demonstrate that work in the

following areas is still needed to advance the objectives of National Environmental Justice Policy. These areas consist of: Public Outreach and Involvement: An open and ongoing process through which active effort is made to sample and extract concerns from the EJ community, to inform them on key issues, and to provide feedback throughout and closure at the end of an EJT review on a particular issue. Triage Process: This is a unique institutional element which functions as the nerve center of the EJT process, serving as an independent Review Board which screens and conducts objective review of identified EJT concerns. Analytic Tools: Introduction to a range of Techniques and Procedures for evaluating EJT issues in the context of regional transportation plans or projects, scaled to the size and complexity of the particular concern. Evaluation Framework: How to identify and use relevant Performance Indicators to quantify EJT concerns, and their application in Tradeoff Analysis to support informed dialogue and decision making With respect to the I-95 Corridor the main findings of the analyses are the following: 1. Transportation zones with high population have more VMT levels. This may further indicate that high population growth areas can potentially generate increasing VMT. 2. In general, high income transportation zones are likely to generate more VMT. The findings suggest that low-income zones contribute less to VMT. However, if low income zones are located close to high income and high population zones, they may

73

face higher VMT generated from outside their zone. This can have serious environmental justice implications to low income zones since outside zone VMT rises can cause higher pollution, health problems and property value impacts. 3. Transportation zones with high office and retail jobs generate higher VMT. However, more industrial job based zones have lower comparative VMT. 4. The higher R square along the i95 corridor provides some evidence that the 4-step gravity model is less accurate when it comes to non-corridor travel impacts. 5. The models are probably understating and possibly not representing travel impacts in low income communities accurately which raises some interesting possibilities.

Phase 6 - Being Heard (Communicating) Policy Implications The focus of this component of the toolkit is on potential environmental justice implications of our finds. Based on the summary of results in the previous section, the potential policy considerations and implications can be summarized as follows: 1. High population and high population growth areas are likely to cause majority of the VMT increases over the coming years. A comprehensive integrative transportation and growth management policy will be needed. Attempts at managing growth at the local and regional level, through such instruments as land use regulation, transportation investment, and urban and regional planning need to consider the VMT implications of alternative growth scenarios for optima growth and VMT management. As such environmental problems as pollution, environmental health, and quality of life in general have increasingly become relevant, such comprehensive growth and transportation integrated management schemes seem very important. 2. Transportation and travel behavior is significantly tied to income profile. Particularly, high income communities are more likely to generate significantly higher VMT and consequent pollution, while low income communities generate lower VMT but could be affected by high VMT of neighboring high population and high income communities. Since sprawl particularly created an environment where low income-low, VMT communities absorb high VMT from the demand of sprawling residents, the potential environmental justice implications are numerous. Low income communities that are next to high population or population growth, or that are next to middle and upper income communities are particularly at risk as such zones are likely to generate higher VMT that can be passed to lower VMT zones. Since higher VMT means more pollution, the potential health and property value impacts could be significant. 3. Places with growth retail and office employment are likely to generate higher VMT. Thus, low income community zones that are in the way of major local and regional retail and office job opportunities are likely to be impacted by VMT increases from

74

outside their zone. As such, the impact of regional job growth and job markets on VMT of other low income communities need to be carefully considered. 4. The findings in this report overall suggest that higher increases in VMT are expected from high population growth, high income, and high job growth transportation zones. As such, effective long-term VMT reduction policies will be most effective if implemented with a focus in such communities. However, the cost to low-income and low VMT communities should be gauged in assessing the potential cost-saving and other benefits of such policies.

75

Conclusions Reported in this summary document are the key research components of the community guide, EJ Toolkit and its technical documentation. The TCERP Project was very successful in gathering and analyzing information from low-income and minority communities on transportation and environmental justice in a “bottom up” manner. The challenge now facing transportation planners in the Baltimore region will be how best to incorporate the anecdotal information gathered during the TCERP Project into the transportation planning process. The TCERP Project Team has proposed that the public participation process should be expanded and a quantitative, data driven analytic tool be developed to accomplish that goal. Building upon the findings in Phase I, Phase II of the project developed and organized the EJ & Transportation Toolkit through selected case studies of the most relevant accessibility and air quality related issues in highly impacted and representative environmental justice communities in the Baltimore region. The overall methodology and approach to each case study will involve a series of steps that will revolve around the cooperation and participation of community stakeholders and various agencies. All of the anecdotal information and data from the Listening Sessions and the Community Dialogue indicate that low-income and minority communities in Baltimore share the perception that: 1) transportation resources and services are not equitably distributed throughout the Baltimore region; 2) the public participation process for transportation planning needs to be improved; 3) transportation problems (air quality, access to jobs and health care, etc.) have a direct impact on low-income and minority communities; and 4) more information should be available to the general public on how transportation planners decide where resources and services should be targeted. Two of the “lessons learned” from the Public Dialogue project were that: 1) developing a good database of community leaders, local ministers and community activists is time consuming; and 2) conducting sustained outreach to community leaders, local ministers and community activists is essential for achieving a good turnout for public hearings like the Listening Sessions, 3) coordinate meetings with the community at times and locations advantageous to the community and 4) continue efforts such as this one to enlightening low-income and minority communities through a process of education. The higher R square along the i95 corridor provides some evidence that the 4-step gravity model is less accurate when it comes to non-corridor travel impacts. The models are probably understating and possibly misrepresenting travel impacts in low income communities.

76

Appendix 1: Bottom up Categorization and Discussion of EJT Issues Kirk Ave Bus Depot The Midway Community is one in which residential and industrial uses collide. The Kirk Avenue bus yard has been a point of contention between the surrounding community and the MTA for some time. The primary complaint has to do with noise and emissions impacts from operations at the yard on the Community. The bus lot sits in a traditional setting between industrial land to the north and east, and residential neighborhoods that seem to have somewhat receded over time on the west and south. What is not clear is the extent to which the operations at the Kirk Avenue have directly caused the decline of the neighborhood. Problems - Residents suffer from respiratory ill nesses and some have died - Residents have a significant increase in asthma - Negative impact on resurgence of neighborhood - Residents complain of physiological health impacts and respiratory health impacts - Enhancements as alternate land use scenarios that strengthen rather than continue the cycle of decline - impact upon the community and has had no aesthetic improvement in the last 50 yrs. - Adjacent to a bus yard - Noise and air pollution Problems - Depot is perceived as having a neg. - Residents have appealed to the MTA on numerous occasions to address these conditions and nature of bus operations Analysis and Findings - Kirk Ave. Depot is the 2nd largest facility in terms of daily bus pullouts. - All 4 four bus depots have had a significant decrease in bus pullouts in 97-2007, however -Kirk Ave. has had the largest decrease (22.5%) - Routes from Kirk Ave. are primarily suburban commuter routes and do not serve the local community. - Lack of accessibility options for the Kirk Ave. residential community. -Noise pollution from Depot: 1) Announcement over loudspeakers 2) Engines run all day 3) Repairs and servicing -research by Hopkins found that noise levels exceeded the ordinance level during day and night, nearly every day. This could result in loss of sleep, high levels of stress, affecting the health of the resident population. Although the daily average of air pollution didn’t exceed the USEPA standard, the 2 week average provides indication that the annual federal health standard may be exceeded -Effects of air pollution put residents at an increased risk for adverse health effects. -measured air pollutants were: Black Carbon (BC) Polycyclic Aromatic Hydrocarbons (PAHs)

77

Assessment & Recommendations -Community should have structured negotiations with MTA regarding near-term and longterm strategies that will begin to provide some relief from the impacts which are substantially attributed to the bus depot. -MTA has attempted to respond to the community’s concerns with mitigation measures: *new operational procedures in response to idling *suggestion of newer and cleaner alternative fuel busses -Community should pressure the MTA to get a clear statement of the likely impacts of the new facility, and to obtain meaningful mitigation in the time between -Burdens created by the Kirk Ave. Depot *property values are lower *busses at Kirk Ave. provide little benefit to residents *noise levels (documented cases of psycho- logical impacts *emissions from bus pullouts and idling Cherry Hill The Cherry Hill community geographically is located in the southern section of Baltimore City, south of the Inner Harbor/Central Business District of Baltimore City. The Cherry Hill community was established in the late 1940’s when the Housing Authority of Baltimore City chose it as a site of a federal project for African American war workers migrating from the South. In those days of segregated housing, no neighborhood in the city was avail- able for an influx of African Americans. Today, Cherry Hill is a mostly residential area with apartment complexes, row houses, and public housing projects. Some of the public housing has been demolished leaving large tracts of land in the middle of the community that can be redeveloped in the future Problems -Residents fee there are too few busses -The busses do not run on schedule -Bus stops, shelters, sidewalks are poorly -Para transit buses are poorly equipped -Poor community depends on transit -People miss appointments or are left stranded -Employers see Cherry Hill residents as unreliable Analysis & Findings Impact of Changes on Regional Accessibility -Decreased transit access overall -Major areas of E. Baltimore inaccessible with 1 hour of travel time. -BWI corridor has major improvements in travel time. -Overall access to jobs for transit dependent households in Cherry Hill has declined.

78

-Access to substantial areas of N.E. Baltimore are no longer reachable without a one hour of travel time. Community Profile and Changes -1990-2000 saw a marked change in the size of Cherry Hill community Here are some of the findings: -Population decline of 21.1% half of which were white. -12.9% decline in the number of households. *indicates larger h0useholds-primarily married couples with children left in larger numbers -51.4% decrease in households with married couples and children. -Single person households increased by 15.8%. -31.7% decline in population aged between 18 to 44 years. -29.6% decline in population of children under 5 -10.3% decline in population over 65 years. -9.3% reduction in the number of housing. 11% increase in the number of vacant homes. -37% decrease in unemployment *these numbers are still well above average levels of unemployment in the population at large Assessment & Recommendations: - Initial review shows Cherry Hill community has experienced deferential treatment with regard to transit service–several additional investigations should be undertaken to quantify and legitimize their claims. - Establish that the reductions in bus services that occurred at the time when Light Rail services were in 1992 were not part of a much larger, system wide reduction in service. *produce a list to access thes changes - An independent monitoring and assessment program should be undertaken to document the concerns regarding service reliability, up- keep of equipment and facilities and driver conduct. *number of ADA compliant housing units *number of physically challenged tenants -MTA should undertake an independent assessment of service complaints and delivery to Cherry Hill community. *address concerns/improve services *develop monitoring/reporting practices Lexington Market Lexington Market is a major commercial destination in downtown Baltimore, providing fresh produce, meats, seafood, and a variety of vendors selling items in a large, historic warehouse building. The market is not only a major tourist attraction for visitors, but also a mainstay for a large portion of Baltimore’s minority community, who prize its selections, freshness and tradition. In 2001 the City of Baltimore Police Department, the Market Authority, and the MTA introduced a set of controversial changes to transit operations at the market when they moved the stops for several of the bus routes to the adjacent block. Problems - Public felt it had been marginalized and left out of the decision-making

79

- Commercial interests were given preference over community well-being *parking lots - Shoppers complained they had to walk longer distances to connect with busses - Public are exposed to walk to connect to busses - Public are exposed to vehicle exhaust as they walk to connect with busses - Public has to navigate busy street traffic, and street activity whilst carrying shopping bags and shepherding small children Analysis & Findings - Historically, a large number of the city’s minority and low-income residents have traveled to the market by public transportation. - The net effect of the added rail services seem to have improved transit access to the market. - Improvements in transit access to the market are improved in the communities to the north and west of the market, along Liberty and Reisterstown Roads Changes in Regional Transit Access - The northeastern corridor shows a decline in transit service between 1999 and 2000. - Absence of rail services and reductions in bus services in this corridor that are mainly African American residents Chronology of events and Transportation Statistics -On June 12, 2001 Fayette Street (farside) stop was disconnected -On January 23, 2002 the Marion Street (farside) stop was established -On March 4, 2002, the Lexington Street (nearside) stop was disconnected *Marion St. is half-a block south of the Lexington Market Eutaw St. Entrance -Pedestrians are in substantial numbers at all hours along both sides of Eutaw Street -Main crosswalks are located on the north at Saratoga Street and south of the Market Entrance at Lexington Street -The crosswalk at Lexington Street is not signalized. This crosswalk supports major pedestrian traffic ade up from visitors and transit users -1991 City of Baltimore traffic reported revealed a combined vehicle volume of 601 vehicles per hour. *amounts to one vehicle a second–making crossing without a signal difficult -Saratoga Street carries a combined vehicle volume of 525 vehicles per hour in the A.M. and 884 vehicles per hour in the P.M. *pedestrian volumes counted at the same times indicated 443 persons attempting to cross Saratoga Street along North Eutaw going north in the A.M. peak hour, only 68 crossing to go south, 141 crossing Eutaw going east, and 392 heading west Assessment & Recommendations -Some hardship may have been visited upon riders to Lexington Market as a result of the movement of bus stops. *further information is needed to access the actual impacts -What is evident is the community was not included in the decision process of moving the bus stops.

80

-These “issues of process” are a concern from an environmental justice perspective than the movement of the stops themselves. -While bus services have been reduced over the past decade, overall transit access to Lexington Market appears to have actually improved. -Bus riders are being forced to walk away from the front of the market to reach relocated bus stops. *streets are crowded and street intersections are busy Highway to Non-where The “Highway to Nowhere” is a massive section of roadway that begins on the western edge of Baltimore and heads due west out of the city as part of US Route 40 through neighborhoods of Poppleton, Harlem Park, Lafayette Square and Rosemont. Once the starting point of an ambitious plan to connect I-95, as it passes through Baltimore, with I-70, which terminates at the Baltimore Beltway (I-695) in the west, the highway would have been badged as I-170. However, the plan ran out of momentum and support before it could proceed beyond the railway line, and thus it remains to this day–almost 30 years after it was opened to traffic– a grade-separated superhighway that is only 1.4 miles long. Problems •The Highway separated and isolated neighborhoods •Increase in drug-related crime. •The creation of the Highway lead to a decline in property values •A continued and systemic lack of political willingness at both the city and state levels to invest in these neighborhoods. •Substantial white population decline between 1950 and 2000 with the highest total population decrease after 1980. •Substantial black population increase between 1950 and 1960. •Negative impact on all neighborhoods •Residents feel isolated and neglected. Analysis and Findings •US/40 Highway to Nowhere corridor is comprised of minority; low-to-moderate ate incomes •Residents within a quarter of a mile of Highway are predominantly (more than 80%) African American •Low Median Household Incomes that surround Highway are between, $15,000 to $45,000 per year •48% of persons over 25 with less than a high school education •21% unemployment rate •13% married couple households •43.3% of persons below the poverty level •15% homes are owner occupied •57% homes are renter occupied •28% housing are vacant congestion, air quality, usage •The Highway to Nowhere is congested at peak weekday times.

81

•Concentrated daily emission productions of 39.2 tons a year of Hydrocarbons and 26.9 tons a year of NOx. •A substantial amount of traffic n US40 comes from outside Baltimore City. •It is clear that the communities in the W. Baltimore neighborhood adjacent to the Highway to •Now-where have had a difficult time. •Community relies on public transportation. •Community can only use public bus transportation. *Busses run late and are overcrowded •Bus operators get the blame for the quality of service Assessment & Recommendations -The Highway to Nowhere remains a testimony to poor planning and a waste of public tax dollars. -The Highway to Nowhere destroyed African American neighborhoods and dislocated several thousand residents in its wake. -The dislocation of residents left the remaining African America homeowners and communities struggling to sustain a proud past. -30 years after the fact, the ‘ditch’ still stands as a monument to how a community could be destroyed in the name of progress. -The area continues to have high drug and crime activity *despite the fact that many long-time residents remain committed to making the community work -A bona-fie community planning effort is needed. -The local residents bear the burden of 36,000 vehicles a day passing through their communities whilst the commuters from Baltimore, Howard, Frederick and even Montgomery Counties have the benefit f access. Appendix 2: Performance Measures, Analytic Tools, and Distributive Impacts NCHRP reports 8-36(11) and 532 are excellent resources on the concept of benefits and burdens, measures which can be used to quantify those elements, and technical assistance on availability and use of analytic tools and data. NCHRP 532 even attempts the important next step of suggesting when the use of particular tools and measures is most appropriate, i.e., at what level of the planning process. These reports (which build upon the initial benchmark efforts of the Atlanta Benefits and Burdens study) offer substantial aid to practitioners (chiefly planners and modeling specialists) on the tools for performing EJ analysis. There are also issues in how the analytic capabilities are used. As a primary example, most regional planning agencies have GIS capability, and most are now attuned to use of GIS tools to perform buffer analysis showing the location of target populations in relation to transportation system features or service envelopes. However, the use of GIS as a serious planning tool is still largely in the early stages. When combined with population synthesis techniques and household micro-simulation methods, GIS can be a powerful tool for analyzing impacts and their distribution across discrete population segments.

82

Appendix 3: Look Up Guide to Support Application of an EJT Analysis In general, transportation projects are evaluated using performance metrics for their effectiveness (how well a proposal meets its objectives), efficiency (the cost of a project relative to its benefits) and equality (how equal are the burdens and benefits spread across geographic, income, racial and ethnic lines). Table 5 provides guides for using performance measures to evaluate equity. To better understand and frame community-based issues and to inform the decision-making process we recommend that goal-oriented performance measures be used to focus how investments in the transportation system impact on low-income and minority communities and that objective - oriented metric measures show how improvement plans enhance transportation system performance in terms of accessibility gains associated community strategies. Table 23: Measuring Equity Community Issues

Community Driven Public Participation Goal Economic Vitality and Competitiveness

Objectives Encourage Employment Opportunities Urban Communities

Maintenance

Safety and Security (Motorized and Non)

Stop the Use of Old Equipment in Low Income Neighborhoods

Increased Accessibility

Increase Accessibility and Mobility Options

Access to Jobs

Reduce Air and Noise Pollution

Protect Environment, Conserve Energy and Improve Quality of Life

Clean Environment

Air pollution Concentrations, Incidence rates of Respiratory disorders, Number of Households exposed to noise. Asthma rates in communities adjacent to large transportation facilities,

Improved Transit Route Structure

Enhance Connectivity and Integration Across Modes for People and Freight Manage and Preserve Existing Transportation System

Access to Shopping and Services

Number of fatalities locations improved per million passenger miles

Advocate for project funding to improve local conditions.

Local Regional Statewide

Fairness in Transit Funding

Condition of roads and streets Condition of side walks Ratio of uncontested travel time between origins and destinations Per Capita Transportation expenditures Per Capita Operating Expenses Number of fatalities Identity of user who benefit Locations improved per million passenger miles

Job Access

Need Assessment

Funding Equity

Performance Measures Work Opportunities within 15, 30 and 45 minutes by car and transit door-to-door. Percent of transitdependent riders who can access jobs with 45 minutes by fixed route of transit Percent and characteristic of out of service buses coming into and area. Pedestrian/bicycle injuries & fatalities Vehicle Crashes, Age of Fleet Proximity to transit Level of Service Accessibility to health care facilities Accessibility to educational facilities

83

Table 24: Performance Measures by Planning Goal Area Performance Measures Application Analytical Method Economic Vitality and Competitiveness Accessibility to regional jobs C PL F RM GIS Accessibility to entry-level/semi-skilled jobs PL F RM GIS Employer accessibility to workers PL F RM GIS Number of jobs by type and location PL DA GIS Business receipts by location PL DA GIS Property values by location Safety and Security for Motorized and non-Motorized Travelers Pedestrian/bicycle injuries & fatalities C PL F PR DA GIS Vehicle Crashes C PR DA GIS Increase Accessibility and Mobility Options Proximity to transit by type (bus, rail, etc.) C PL F PR RM GIS Level of service (headways, days/hours of service C PL F PR DA RM GIS Average travel times for selected O/D pairs by mode C PL RM GIS Accessibility to regional educational institutions PL F GIS Accessibility to regional healthcare facilities PL F GIS Average age/condition of buses by area served C F DA GIS Protect the Environment, Conserve Energy, and Improve Quality of Life Number of households living with X-feet of busy highway C PL F PR DA GIS Air pollution concentration by type pollutant C PL PR RM GIS EM Incidence rates of respiratory disorders C PL DA GIS Number of households exposed to noise exceeding X-decibels C PL PR DA RM GIS Number of households living within X-feet of a bus terminal C PL DA GIS Percent of buses servicing area which use alternative fuels C PL F DA GIS Percent takings, household dislocations, access restrictions PL F PR DA GIS Enhance Connectivity and Integration Across Modes Number of transfers required for transit trips between select origin/destination pairs C PL RM GIS Percent of travel time accounted for by transfers in select origin/destination pairs PL F RM GIS Manage Existing Transportation System for Maximum Efficiency Percent of congested to un-congested travel time between select origin/destination pairs PL RM GIS Preserve the Existing Transportation System Condition of roads and streets PL F DA GIS Condition of sidewalks PL F DA GIS Funding Equity Transportation capital expenditures per capita PL F PR DA GIS Transportation operating expenditures per capita PL F PR DA GIS Identity of users benefiting from new project or program PL F PR DA GIS C=Current Concern, PR=Project, PL=Planning, F=Programming, DA=Data Analysis, RM=Regional Travel Models, GIS=GIS-Aided, EM= Emission Models

84