Dallas Transportation Equity by Institute of Urban Studies

TRANSPORTATION EQUITY & ACCESS TO OPPORTUNITY FOR TRANSIT DEPENDENT POPULATION IN DALLAS 2017

Acknowledgments The research conducted for this project was performed by the Institute of Urban Studies (IUS) and of the Center for Transportation Equity, Decisions and Dollars (CTEDD) at the University of Texas Arlington (UTA). CTEDD is funded by the U.S Department of Transportation and conducts unique and nationally significant research on transportation policy issues, equity, shared mobility, technology and autonomous transportation. The Institute of Urban Studies is a state funded research entity at UTA that engages in research and practice toward improving life quality for the people of Texas, specifically through improving the places we live. This report was made possible with support from the City of Dallas. The research team gratefully acknowledges the assistance and support of the city staff members and Steven Duong of AECOM.

Principal Investigator Dr. Shima Hamidi, Director of the Institute of Urban Studiers and Director of the Center for Transportation Equity, Decisions and Dollars (CTEDD) at the University of Texas Arlington Co-Principal Investigator Dr. David Weinreich, Postdoctoral Associate, CTEDD Graduate Researchers Somayeh Moazzeni, Research Associate, Institute of Urban Studies Reza Sardari, GISP, Travel Demand Modeler, C&M Associates, Inc. Project Team Amanda Kronk, Project Manager, Institute of Urban Studies Raha Pouladi, Researcher, Institute of Urban Studies Tahereh Granpayehvaghei, Researcher, Institute of Urban Studies We would also like to thank Maria Dolores Contreras, Behnoud Aghapour, Ahoura Zandiatashbar, Yalcin Yildirim, Reza Paziresh and Ann Mai.

Table of Contents INTRODUCTION ............................................................................................................................... 6 Dallas Area Rapid Transit (DART) ........................................................................................... 7 STUDY AREA .......................................................................................................................... 9 TRANSIT DEPENDENT CORE .................................................................................................. 12 COMPONENT 1: TRANSIT AFFORDABILITY | Is Transportation Affordable in Dallas? ................ 15 How Much Do Dallas Residents Pay for Transportation? ................................................... 15 Are DART Transit Fares Affordable for Transit Dependent Populations? ............................. 19 COMPONENT 2: TRANSIT COVERAGE | How much of the Dallas Population Is Covered By Transit? Is It Equitable? ................................................................................................................. 22 COMPONENT 3: TRANSIT FREQUENCY | How much of the Dallas Population Has Access to Quality Transit? How much of Dallas Consists of Transit Deserts? ............................................. 26 COMPONENT 4: TRANSIT & JOB ACCESSIBILITY | How Many Jobs Could Dallas Residents Reach By Transit versus Driving? ............................................................................................................ 34 CONCLUSIONS ..................................................................................................................... 47 LIST OF REFERENCES............................................................................................................. 50 APPENDIX A ........................................................................................................................ 54 APPENDIX B ........................................................................................................................ 59

Glossary of Terms Accessibility Accessibility measures ease of access to destinations and may be regional or local. In this study, regional accessibility is quantified by the percentage of regional jobs reachable within a given travel time, which tend to be highest at central locations and lowest at peripheral locations. The distance to the nearest attraction of a given type typically measures local accessibility. Transportation Equity The spatial distribution of transit service performance measures and whether the distribution is considered fair. Transportation Affordability Transportation affordability is measured in terms of transportation costs as a percentage of household income for a typical regional household. Transportation Costs According to the Center for Neighborhood Technology, Transportation Costs are calculated as the sum of automotive ownership costs, automotive use costs, and transit use costs. Dividing these costs by the representative income illustrates the cost burden placed on a typical household by transportation costs. Low-Income Households/Low-Wage Households Households with a household income below the poverty level Low-Wage Jobs Low-wage jobs are those included in the lowest earning category of income classes according to the Longitudinal Employer-Household Dynamics (LEHD) that is published annually by Census. The LEHD classifies jobs and workers in three earning categories including below or equal to $1,250/month, between $1,251 and $3,333/month, and above $3,333/month. In this report, we refer to jobs within the category of below or equal to $1,250/month as low wage jobs. Transit Dependent Core Hot spot areas in an urban context where residents are less likely to own a private vehicle due to a concentration of age, income, mobility, and/or minority groups. General Transit Feed Specification (GTFS) GTFS is a common format for public transportation schedules and associated geographic information, which is published by public transit agencies and can be used for analyzing transit frequencies (Transitfeed n.d.). For each transit agency, it contains a series of text

files collected in a ZIP folder. Each file includes a particular aspect of transit information including stops, routes, trips, and other schedule data. Transit Coverage Transit service coverage measures the areas that have access to transit services either by station or by route; it can be expressed as a percentage and includes stops or routes. Transit Frequency Transit frequency measures how often transit service is provided, either at a specific location or between two locations.

Introduction With 1,263,775 residents, Dallas is the 9th most populous city in the U.S. and the largest city in the Dallas Fort-Worth metropolitan area. Dallas, as one of the pilot cities for the 100 Resilient Cities network, has its own resilience challenges. Two of the resiliency factors that need to be addressed in this city are economic disparity and transportation. Dallas Area Rapid Transit (DART) is a regional transit agency serving thirteen cities including Dallas with rail, bus, paratransit, and ride share services. Also, it provides services to DFW International Airport and Fort Worth via the Trinity Railway Express (TRE) in collaboration with the Fort Worth Transportation Authority (The T). Hence, more than 50% of DART users are Dallas residents. Despite all of these facts, DART ranks 23 out of 39 large and medium sized transit agencies in the U.S. in terms of bus passenger miles per capita (APTA, 2014). Table 1: Transit Ridership for the Top 20 Transit Agencies with Light Rail Transit Service Stations

Year opened

Avg. daily boardings per mile

(Q4 2016)

System length

16 17 18 19 20

(2016)

Boston San Francisco Minneapolis-St. Paul 4 Central Link Seattle 5 Hudsonâ&#x20AC;&#x201C;Bergen LRT Jersey City 6 Metro Rail light rail Los Angeles 7 METRORail Houston 8 San Diego Trolley San Diego 9 MAX Light Rail Portland 10 Valley Metro Rail Phoenix 11 TRAX (UTA) Salt Lake City 12 Denver RTD: Denver 13 SEPTA light rail Philadelphia 14 RTA Streetcars New Orleans 15 DART Dallas

Annual Ridership

1 2 3

Largest city served

System

MBTA light rail: Muni Metro METRO LRT

69,236,700 52,597,300 22,963,500

26 35.7 21.8

8,711 4,602 3,344

1897 1912 2004

74 152 37

20.4 17 88.1 23.8 53.5 60 26.3 46.8 58.5 68.4 22.3 94 Rank =1/20 46 42.9 26.2 42.2 33

3,245 3,051 2,403 2,378 2,140 2,070 1,947 1,374 1,297 1,199 1,117 1,091 Rank = 15/20 996 963 814 709 691

2009 2000 1990 2004 1981 1986 2008 1999 1994 1906 1835 1996

16 24 80 44 53 97 35 56 62 >100

MetroLink Sacramento RT LRT The T: Pittsburgh LRT Santa Clara VTA LRT Baltimore Light Rail

19,121,621 15,450,736 65,829,000 18,335,800 38,068,600 40,240,300 16,322,800 19,220,300 24,585,000 25,127,600 8,084,400 29,619,500 Rank = 6/20 15,343,900 12,286,600 7,783,100 9,931,100 6,888,500

1993 1987 1984 1987 1992

37 53 53 62 33

St. Louis Sacramento Pittsburgh San Jose Baltimore

The same applies to the light rail transit service. As shown in Table 1, DART actually has the 6 th highest annual transit ridership (total unlinked passenger trips) in the U.S. However, when controlling for the size of the system, DART’s ranking drops noticeably. Indeed, DART ranks 15 out of the 20 largest transit agencies in terms of light rail average transit trips per mile (APTA, 2014 and 2016). A comparison of DART to other top transit agencies in Table 1 demonstrates that DART’s overall ridership relative to its size is below the average (among the lowest). What could be the reason? Is it because transit is not affordable in Dallas? Is it because of the quality and hours of transit service, or perhaps is it due to the spatial distribution of the DART transit system? How many of the transit dependent areas in Dallas are covered by DART? The City of Dallas and the DFW region as a whole are also facing equity challenges relative to transportation. Dallas has one of the lowest rates of upward mobility (6%) in the U.S. (Chetty et al., 2014). Additionally, more than 77% of Section 8 housing units in DFW are unaffordable due to transportation costs (Hamidi and Ewing, 2015). These facts call for a comprehensive study of transportation equity in the City of Dallas. This report seeks to address these challenges and to study the current state of transportation equity in Dallas. Our proposal includes a comprehensive quantitative study of the transportation metrics that impact equity and efficiency of the transportation system for transit dependent populations.

Dallas Area Rapid Transit (DART) Agency History The Dallas Area Rapid Transit (DART) system began in 1983 through a voter initiative in favor of regional transportation; 14 cities joined in 1983 including Addison, Coppell, Carrollton, Dallas, Farmers Branch, Flower Mound, Garland, Glenn Heights, Highland Park, Irving, Plano, Richardson, Rowlett, and University Park. By late 1984, the system had expanded by 74 additional buses on 54 routes, rush hour service on 33 routes, and additional crosstown service. Over the next decade, DART acquired land and funding for further expansion and construction of rail lines and HOV lanes. In June of 1996, the first 11.2 miles of DART’s 20-mile light rail transit starter system opened, resulting in initial ridership far exceeding expectations. Ridership was originally projected to average 15,000 daily passengers, but averaged more than 18,000 daily passengers. Just after its 20th anniversary in 2004, DART opened the Malcolm X Bus Shelter which was to serve as a model for future shelters as it featured air ventilation, heaters, landscaping improvements, telephones, and passenger information. In November of 2013, the D-Link was made available to serve as the connection between major tourist attractions and employment centers in downtown Dallas and Oak Cliff. In June of 2016, DART Rail celebrated its 7

20th anniversary with system highlights including 90 miles of rail making it the longest system in the country, over 360 million passenger trips, $8 billion in economic impact, and $5 billion in private transit oriented development at or near stations.1

Transit Services Offered DART currently offers a variety of transit services including bus, light rail, the Trinity Railway Express (TRE), On-Call, FLEX Service, Paratransit Services, Bike, Vanpool/Carpool, and Collin County Rides. The DART Bus system offers in excess of 130 routes within the service area, with localized ridership options including FLEX Service in many suburbs, a limited stop Rapid Ride, and the free D-Link which serves to connect major attractions within downtown Dallas. The DART Rail system is 93 miles in length and consists of four lines: Red (servicing Parker Road to Westmoreland), Blue (servicing downtown Rowlett to UNT Dallas), Green (servicing North Carrollton to Buckner), and Orange (servicing DFW Airport to Parker Road or LBJ/Central). Each of these lines pass through downtown Dallas. The Trinity Railway Express (TRE) is a commuter train that connects Dallas and Fort Worth with stops in between, as well as a connection to DFW Airport. On-Call is a demand-responsive curbto-curb van service offered by DART, which seeks to connect riders with local destinations. The FLEX Service is comprised of six routes (serving the Telecom Corridor area of Plano and North Richardson, South Irving, South and East Plano, Rowlett, and southeast Dallas), and is a combination of fixed route services and curbside pickup/delivery. The Streetcar is a free service extending 2.45 miles from Union Station in downtown Dallas to the Bishop Arts District. It is owned by the City. The City provides funding to DART for operations. The M-Line Trolley is also a free service that is owned and operated by the Mckinney Avenue Transit Authority. It utilizes vintage trolley cars to link the Dallas Arts District with Uptown and West Village. Paratransit Services provide 150 accessible vehicles for curb-to-curb transit to people with disabilities who cannot use other modes of transportation. Vanpools and Carpools utilize passenger vans to accommodate large group travel. And finally, Collin County Rides is a program that provides services to enrolled residents over the age of 65 or those with certified disabilities. DART services 13 cities in North Texas including Addison, Carrollton, Cockrell Hill, Dallas, Farmers Branch, Garland, Glenn Heights, Highland Park, Irving, Plano, Richardson, Rowlett, and University Park.2 As a multi-modal provider, DART operates 93 miles of light rail serving 64 stations, a commuter rail line with 10 stations, 149 bus routes provided with over 600 buses, a paratransit service for disabled passengers, a vanpool program operating nearly 200 van pools, and demandresponsive service for the public. DART also provides senior and disabled transportation in four 1 2

Source: https://www.dart.org/about/history.asp Source: http://www.dart.org/riding/riding.asp

Collin County cities using a taxi voucher program. DART services are also integrated with the transit services provided by the Fort Worth Transit Authority, Denton County Transit Authority, and three rural transit districts. DART is also the recipient of a Federal Transit Administration (FTA) Formula grant and periodic capital funding. DART generates over $70 million annually in farebox revenue and other operating revenues.

Study Area While the major focus area of this report is the City of Dallas, we included two other study areas (DART service area and the transit dependent core) in addition to the city boundary. The reason for this is simple: residents are not limited by city boundaries in order to gain access to jobs and other major destinations. Therefore it is important to study transit service efficiency and equity in a broader context than just the city boundary. Also, comparing the City as a whole to the transit dependent core with a concentration of transit dependent populations provides a better understanding of the spatial distribution of transit services with regard to equity. The study area covers three different geographies, as shown in Figure 1. 1) City of Dallas with the population of 1,263,775 (475,879 households) 2) DART service area, which is larger than the city itself and together with the city, has a population of 2,359,632. DART provides access to around 13 cities with 579 bus routes and 54 rail routes that extend mostly to the north of the city limits. The DART service area has about 1,644,811 jobs, with 865,985 of them existing within the City of Dallas. 3) The extended suburban area (subarea) in the form of a 5-mile ring around the DART service area. It has a much lower density (1,470 pop/sq. mi.) compared to the DART service area (3,398 pop/sq. mi). There are nine bus routes and 2 rail routes with 38 stops in the extended subarea. The City of Dallas boundary, the DART service area, and the extended subarea constitute the final study area that is demonstrated in Figure 1. This area involves a total population of 4,608,143 (more than 1.6 million households) in 2,224 sq. mi. of land. Finally, a total number of 2,522,309 jobs, 21% of which are low wage, are studied in this report. Other socioeconomic characteristics of the study area are provided in further detail in Table 2.

Figure 1: Study Area 10

Table 2: Socioeconomic Characteristics of the Study Area Measurements

City of DART Service Dallas Area 1,263,775 2,359,632

Extended Subarea 2,248,511

Study Source Area 4,608,143 ACS 2014

Population % Population growth 0.40% 0.61% 2.71% 1.53% (2000-2016) Households 475,879 868,857 772,704 1,641,561 % Households with Kids 68% 70% 84% 77% % Low Wage Jobs 18.8% 18.5% 25.8% 21.0% % Low Wage Workers 23.0% 21.6% 20.7% 21.2% * DART Service Area includes the City of Dallas ** Study Area includes both DART Service Area and Extended Subarea

NCTCOG ACS 2014 ACS 2014 LEHD 2014 LEHD 2014

As Table 3 demonstrates, job growth in the Extended Suburban area is happening at a much faster pace than the City of Dallas (3.1 % and 0.7 %, respectively). Interestingly, from 2002 to 2014, the DART service area experienced a negative growth rate of -0.8% with respect to low wage jobs. This trend is worse in the City of Dallas with a 1.4% decrease in the number of low wage jobs (see Table 3). Table 4 shows a relatively systematic trend of low wage jobs moving further from the city center toward the outer suburban areas. While the annual low wage job growth in the study area is just 0.2%, it ranges from -1.4% in the city limits of Dallas, to 1.9% in the extended subarea. Based on our analysis, Dallas and DART Service Area are witnessing slow population growth over a period of 16 years (0.40% and 0.61%, respectively) while the Extended Suburban area and the Study area are increasing with a higher rate. From 2000 to 2016, population growth in the Extended Suburban area is 2.71% and in the Study area it is increasing with a rate of 1.53%. This might reflect the attraction of suburban area for the population. Table 3: Job Growth in Dallas and Surrounding Area. Source: (Longitudinal Employer-Household Dynamics. Data, 2002 & 2014) Jobs City of Dallas DART Service Area Extended Subarea

2002

2014

793,371 1,414,845 609,604

865,985 1,644,811 877,498

Annual Growth Rate 0.7% 1.3% 3.1%

Table 4: Low Wage Job Growth in Dallas and Surrounding Area. Source: (Longitudinal EmployerHousehold Dynamics. Data, 2002 & 2014) Low Wage Jobs City of Dallas DART Service Area Extended Subarea

2002

2014

193,079 334,583 180,599

163,147 303,692 226,761

Annual Growth Rate -1.4% -0.8% 1.9%

Transit Dependent Core In order to address the transportation equity challenges, the first step is identifying the transit dependent population as the key users of transit. Quantifying and identifying the transit dependent core where the majority of the transit dependent population lives along with employment centers would be critical for a better understanding of potential spatial mismatch for disadvantaged populations in Dallas. According to The Federal Transit Administration, â&#x20AC;&#x153;transit dependentâ&#x20AC;? refers to groups of people that are too young, too old, too poor, or who are physically unable to (Litman, 2008). Various indicators such as age, income, and access to a private vehicle are used to determine dependency. One of the earliest studies conducted by Pucher in 1982 suggests that lowerincome, non-white, students and the elderly can be considered transportation-disadvantaged residents in an urban context (Pucher, 1982). In a more recent study, Steiss suggests that in identifying a transit dependent population, instead of focusing on the reasons behind the transit dependency of an individual, it is better to find locations with vehicle scarcity (Steiss 2006). Since transit-dependent populations comprise individuals who rely on transit systems for access and mobility, this population will benefit most from investments made in high quality, reliable, and frequent transit. Transit-dependent populations mark a notable group of people who are often excluded from access to employment opportunities, access to retail options, and overall participation in society Given that on average between 10%â&#x20AC;&#x201C;12% of American households do not have a car, public transport can mean the difference between getting to work and accessing vital services, or not (Chetty et al., 2014). Identifying transit dependent populations is an important tool for determining where new transit service should be provided or how existing systems can be modified to better serve the population in need. Unfortunately there are no clear guidelines on how to calculate a single index for transit dependent areas. Population groups often considered as transit dependent include the elderly, the young, low-income individuals, and households 12

without vehicles available. The census provides data on individual groups that may be considered transit dependent but often these groups overlap spatially. While the census information is useful in studying these groups individually, there is a lack of a composite, multidimensional index to capture where transit dependent populations exist. This section seeks to address this gap by changing the focus from individuals to identifying areas where residents are less likely to own a vehicle. Areas that have the largest disparity between drivers and automobiles available are more likely to be transit dependent than areas that have nearly a one to one ratio between drivers and automobiles available. For those areas that have a large disparity between drivers and automobiles available, there may be multiple reasons why this disparity exists. It could be due to age, income, mobility, or a combination of factors. In order to identify transit demand catchment areas, we used ArcGIS and SPSS to conduct a cluster analysis (hot spots) and a spatial regression model. Spatial statistic techniques are practical tools in planning, public health, and social science fields to identify spatial patterns (hot spots) for factors such as crime, poverty, and disease. Identifying hot spots requires multiple techniques and no single technique is sufficient to examine all types of transit dependent populations by addressing income, age, life cycle, and other socioeconomic factors. To identify hot spots for transit demand, four steps were completed as follows: First, regression models were developed to identify the relationship between transit demand and socioeconomic characteristics of neighborhoods. Second, the spatial pattern of selected socioeconomic factors was examined using spatial autocorrelation (Global Moran's I). Spatial autocorrelation was used to determine if there was an underlying geographic clustering of the data based on both location and socioeconomic characteristics of block groups (Getis. and Aldstadt, 2010). Third, Z-scores of socioeconomic factors were calculated and then aggregated together to define the transit demand for each block group. Finally, the resulting average of these criteria was implemented for hot spot analysis. The result of this step identified statistically significant spatial clusters of high values (hot spots) and low values (cold spots). For a detailed description of methodology, data, and variables, see Appendix A. The final results of the hot spot analysis are presented in Figure 2.

Figure 2: Transit Dependent Population Core

Component 1: Transit Affordability | Is Transportation (and Transit) Affordable in Dallas? How Much Do Dallas Residents Pay for Transportation? Transportation cost is the second largest expenditure for an American family (CNT, 2006). According to HUD, if a householdâ&#x20AC;&#x2122;s housing cost is no more than 30% of its income, it is regarded as affordable. Transportation is considered affordable if a household spends no more than 15% of its budget on transportation costs (CNT, 2006; HUD 2015; Hamidi et al., 2016). According to CNT (and LAI) methodology, Figure 3: City of Dallas: Average Housing and household transportation costs are Transportation Costs as Percentage of Income. (HUD, estimated at the census block group level H+T Index Data 2017) as three separate cost components: costs of automotive ownership, automotive use, and transit use. Automotive use is modeled based on household VMT data from Chicago and St. Louis. Automotive ownership is modeled based on vehicle ownership data from the American Community Survey. Transit use and associated costs are based on Google transit feeds. Later in 2013, the Departments of Transportation and Housing and Development funded the development of refined H+T-like index, called the Location Affordability Index. The LAI is based on the same methodology as H+T Affordability Index but uses the most recent and better quality data with more coverage. For each block group in the U.S., the LAI T, LAI H and LAI H+T Indices show the percentage of income a typical household spends on transportation, housing, and housing and transportation respectively. According to HUDâ&#x20AC;&#x2122;s Location Affordability Index (LAI), a typical household in the City of Dallas spends more than 19% of its income on transportation, which is much greater than the 15% transportation affordability threshold. The same applies to the DART service area (20.2%), and the extended subarea (22.6%), going way beyond the 15% delineated by the index as the threshold for transportation affordability (Figure 3). Due to limited choices of transportation modes and in search of affordability, vulnerable lowincome families make critical trade-offs between housing and transportation costs (Salvin, 2014). It is not only the provision of affordable housing that matters, but also the access to daily 15

needs and availability of a supportive system to enhance the well-being and livability of people and communities. In an auto-oriented region like DFW, the situation might be exacerbated for low-income families lacking private vehicles. Transportation costs in compact and mixed use neighborhoods with better access to transit and job centers, referred to as â&#x20AC;&#x153;Location Efficientâ&#x20AC;?, tend to be lower than those located in car dependent, inaccessible neighborhoods. According to a national study by the Center for Neighborhood Technology (CNT), the City of Dallas is identified as being far from having location efficient neighborhoods, with only 2% of city neighborhoods described as being location efficient (see Figure 4).

Figure 4: Percentage of Location Efficient Neighborhoods (CNT, H+T Index Data 2017) Figure 5 shows the spatial distribution of transportation affordability for block groups in the study area. The dark color shows unaffordable areas and the light color represents affordable block groups where transportation costs are less than 15% of typical low-income households. Other than a few block groups in downtown Dallas, almost the entire study area is shown to be unaffordable in terms of transportation costs. The affordable areas are generally in downtown in accessible and transit-served locations. This is primarily due to proximity to a transit station, which eliminates the need for driving to major destinations. Even though almost all of the study area is unaffordable with regard to transportation costs, the extent of unaffordability varies substantially. As shown in Figure 6, the spatial distribution of transportation affordability is uneven particularly in the transit dependent core. The majority 16

of households, living in southwestern and southeastern neighborhoods in Dallas, spend between 20%-22% of their income on transportation, which is almost the same percentage as housing costs for these households. This amount is even higher in the southeastern portion of the DART service area such as Hutchins and Lancaster, ranging from 21% to 26%.

Figure 5: Transportation Affordability for the Typical Regional Household (Source: Location Affordability Index, HUD) 17

The Typical Regional Household is defined as a household with a household income that is the median income for the region ($58,356 for DFW), the average household size for the region (2.78 for DFW), and the average commuters per household for the region (1.26 for DFW).

Figure 6: Percentage of Income Spent on Transportation in Transit Dependent Core (HUD, H+T Index Data 2017) Low-income households qualified for HUD assistance programs also face the transportation affordability challenge. A recent study by one of the authors of this report found that 18

households in 44% of all Multifamily Section 8 properties in the nation, spend on average more than 15% of their income on transportation costs, making these properties effectively unaffordable. According to this methodology, more than 73% of Section 8 Multifamily properties in Dallas are unaffordable (Hamidi et al., 2015). The disparity among Dallas neighborhoods with regard to transportation affordability is evident. It is not evident, however, which factors led to this spatial disparity. Is it due to a lack of access to quality transit service? Or does it have to do with unaffordable transit fares? In the next section, we review DART’s transit fare policies compared to other major transit agencies in the nation.

Are DART Transit Fares Affordable for Transit Dependent Populations? DART offers a variety of fare options for transit riders. Some of the most commonly used fares include Day Passes, Two-Hour Pass, Mid-day Passes, Monthly Passes, 7-Day Passes, and Senior Citizen Annual Passes. These options are listed as below:      

Day Pass: Provides unlimited rides on the date of purchase until 3 a.m. the following day. Two-Hour Pass: Provides unlimited rides but only for a two-hour window. Mid-day Pass: Provides unlimited rides between 9:30 a.m. and 2:30 p.m. available from Monday through Friday. Monthly Pass: Provides rides for local and regional travel for 31 days 7-Day Pass: Provides rides for 7 consecutive days Senior Citizen Annual Pass: Provides riders of 65 years of age and older with an annual pass.

DART provides services at both local and regional levels. Both local and regional services include “all DART buses and trains” and “DART On-Call and FLEX service”. The local service also provides “Trinity Railway Express trains between Union Station and Centre Port/DFW Airport Station”; while the regional service offers “All Trinity Railway Express service, as well as The T in Fort Worth and DCTA in Denton County”3 DART also provides reduced fares for different groups of the transit dependent population. The reduced fare is valid on all DART, TRE, The T, and DCTA buses and trains with the following conditions: 

Seniors (65 or older) with valid DART photo ID Non-paratransit certified persons with disabilities with valid DART photo ID Medicare card holders Children ages 5-14 (Children under age 5 may ride free when accompanied by an adult with valid local, regional or reduced fare).

  

http://www.dart.org/fares/fares.asp

ď&#x201A;ˇ

High school students with valid DART or student high school photo ID. High school fares are applicable on bus and rail and valid Monday through Friday only. College/Trade School students with valid DART-issued student photo ID for full-time undergraduate students registered at schools, which are located in the DART Service Area and are not participating in the Higher Education Program.

ď&#x201A;ˇ

The list does not include low-income (and very low income) groups of the population as qualified recipients of the reduced fare options. Several other top transit agencies include lowincome groups in their fare reduction policy. Los Angeles Metro, for instance, in addition to senior/disabled/Medicare and students cover adult riders with income levels meeting the eligibility criteria4. The list also does not include students who are participating in Higher Education Programs. Not all university students are transit dependent, but perhaps combining it with income eligibility could provide an opportunity for low income transit dependent university students to take public transit as their major mode of transportation. Agencies such as UTA TRAX in Salt Lake City and Chicago CTA5 provide reduced fare options for all students. Finally, free transit is another effective fare policy adapted by other transit agencies for transit dependent groups. Philadelphia (SEPTA)6 offers free transit to only seniors (65 or older). Chicago CTA provides free transit to active U.S. military personnel, disabled veterans, seniors (65 or older), and people with disabilities. The conversation about transit fare is timely since DART is in the process of revisiting its fare policy with the possibility of increasing transit fares in the near future. To ensure transit dependent groups are not affected by the fare increase, DART could adapt these policies proved to be effective in other major cities. Incentives such as reduced passes either partial/non-payment or gross payroll deductions could provide more affordability for population groups who rely on transit as their only mobility option. Still the question remains of how affordable transit fares are for Dallas residents. Table 5 provides a comparison of DART to other transit agencies in major U.S cities. We included various fare options and their costs (both reduced and regular). To provide an apple-to-applecomparison of income and fare affordability, we also included the fare amount as the percentage of annual income for low-income households who qualify for HUD assistance programs. According to HUD, the income limit for a qualified household is 30% of the HUD adjusted median income for the city/county. 4

https://www.metro.net/projects/rider_relief/ http://www.transitchicago.com/travel _information /fares/reduced.aspx 6 http://www.septa.org/fares/pass/key.html 5

Table 5: Transit Fare and Affordability for the Eight Largest Cities in the U.S Day Pass Fare Option cost per % of pass annual income Dallas DART Local $5 10.3% Regional $10 20.7% Reduced* $2.5 5.2% LA Metro Local $5 11.8% Reduced* $2.5 New York City Local $5.5 8.7% MTA Reduced* $2.75 Chicago CTA Local $10 19.2% Reduced* FREE Houston Metro Local $3 13.2% Reduced* Phoenix Valley Local $4 8.9% Metro Regional $5.2 11.7% Reduced* $2 San Diego MTS Local $5 8.3% Regional $12 20.0% Reduced* San Jose Local $6 7.6% Regional $12 15.2% Reduced* $2.5-5

Monthly Pass 7 Day Pass $25 $50 $25 $32 $16 $28 FREE $20 $10 $22 -

cost per pass $80 $160 $40 $100 $40 $121 $60 $100 FREE $64 $32 $72 $100 $18-36 $70 $140 $25-45

% of annual income 5.4% 10.9% 2.7% 5.5%

Annual Pass

$800 $1,600

6.5% 6.3%

3.3%

2.3% 3.2%

$770 1.7% 3.5% $275-495

Although transit agencies such as the Phoenix Valley Metro, San Diego MTS, San Jose MTS, and New York MTA offer relatively more affordable transit fare options, they are still affordable for low-income populations and are more affordable than many other transit agencies. As shown in Table 5 as compared to other transit agencies, DART fare options are relatively affordable. A low-income household would spend on average about 10% of its income on transit fares if they used the daily pass for the entire year (365 days). The amount is much lower if the household is qualified for the reduced fare or if the household opts to use the annual pass. DART is currently considering a phased increase in transit fare effective January 2018 through August 2018. Table 6 provides a simple comparison of the current fare and the proposed fare for the monthly pass.

Table 6: DART Current and Proposed Transit Fare and Affordability for the Monthly Pass

Fare Option

Dallas DART

Local Regional Reduced*

Monthly Pass Monthly Pass (current) (proposed) cost per % of cost per % of pass annual pass annual income income $80 5.4% $96 6.5% $160 10.9% $192 13.1% $40 2.7% $48 3.3%

According to our analysis, fare affordability may not be the most important barrier of transit use in Dallas (with reservation in the case of the regional pass). Still, as recommended, there are more aggressive policies that could be adopted by DART to provide affordability for target groups of the population needing it the most. However, it is likely that fare affordability may not be a key contributor to the relatively low transit ridership rate in Dallas. In the next sections, we take a close look at and an in-depth analysis of other key players of transit ridership. We begin by quantifying and analyzing transit coverage to see how well transit dependent areas are covered by transit as compared to the rest of the city and DART service area.

Component 2: Transit Coverage |

How Much of the Dallas Population Is Covered By Transit? Is It Equitable? Using the General Transit Feed Specification (GTFS), we analyzed the level of walking access to transit stations from each census block in the study area. GTFS is a common format for public transportation schedules and associated geographic information, which is published by public transit agencies and can be used for analyzing transit frequencies (Transitfeed n.d.). For each transit agency, it contains a series of text files collected in a ZIP folder. Each file includes a particular aspect of transit information including stops, routes, trips, and other schedule data. We used a one quarter mile buffer from each block centroid to bus stations and a one half mile buffer from each block centroid to transit stations as the transit catchment area according to transportation literature (Ewing and Hamidi 2014).

The spatial distribution of transit coverage within the study area is presented in Figure 7. With DART bus and rail routes depicted on the map in orange and red lines, the yellow buffers indicate a one quarter mile walking distance from bus stations and a one half mile distance from rail stations. The gray color presents the areas without walking distance access to a transit station. This accounts for 29% of the population with no transit coverage in the City of Dallas and 44% of the DART service area. Using GTFS data, we also computed transit coverage in the study area by the average number of transit (bus or rail) trips per hour for each transit station (see Figure 8). As shown in the map, there are several hot spots of transit coverage within the DART service area, most of them being business locations and university campuses. Downtown Dallas, the Addison area, UTD campus, University Park, South of Dallas Love Field, and Irving Convention Center at Las Colinas are the main hot spots in which a higher transit coverage can be observed. We also examined the distribution of population below the poverty level in the study area to see to what extent areas with a concentration of poverty are Figure 7: Spatial Distribution of Transit covered by transit. The below-povertyCoverage. (Source: GTFS Data, 2017) level households have a lower car ownership rate and tend to use transit more. Therefore, the unavailability of public transit for this population makes mobility difficult for this population, with further related short-term and long-term consequences such as social and racial exclusion. Our analysis confirms that areas with a higher concentration of the population below the poverty level, particularly in south Dallas, receive better coverage and more frequent transit availability (Table 7). About 37% of the population in the City of Dallas is not covered by transit during any time of the day while in the transit dependent core about 32% of residents are not covered by transit. On the other hand, 26% of the population in the City of Dallas and 31% of

the population living in the transit dependent core are covered by one station with a daily average of 3-6 trips per hour. Table 7: Transit Coverage for the City of Dallas vs. the Transit Dependent Core 0 trips

>2 trips

3-6 trips

<6 trips

City of Dallas

36.83%

36.24%

25.83%

1.10%

Transit Dependent Core

32.34%

35.05%

31.11%

1.51%

Even though the transit dependent core has slightly better transit coverage than the rest of the city, more than a third of the population in both the transit dependent core and the city is not covered in walking distance by transit during any time of the day. No physical access to transit stations means no transit use by a third of the population in Dallas which could contribute to the relatively low rate of transit ridership. Our findings call for more investigation on first/last mile gaps in the DART transit system and the best public transit solutions tailored for each area. About a third of riders in the transit dependent core do not have walking access to a transit station. Conducting the analysis at the census block level provides relatively precise information to assist DART with decision making regarding future stations and transit route planning to ensure transit is available to the transit dependent population. Of course, being within a walkable distance from a transit station is not the only indicator of walkability. Land use and built environmental factors (also known as D variables) such as development Density, land use Diversity, street Design, Destination accessibility, in addition to Distance to transit could significantly affect the walkability of a neighborhood (Ewing and Cervero, 2010) which calls for a more in-depth analysis of walkability close to transit stations in order to have a better understanding of first mile/last mile access to transit. According to our analysis, physical distance to transit stations could be a major barrier of transit use in southern Dallas. The main question in this study remains unanswered. What are the drivers of the relatively low transit ridership in Dallas and the DART service area? In the next section, we study one of the potential contributors to transit ridership: transit frequency by time of day.

Figure 8: Transit Coverage (Average Number of Transit (bus or rail) Trips per hour for Each Transit Station) Overlapped with the Population below Poverty Level. (Data, GTFS 2017) 25

Component 3: Transit Frequency |

How Much of the Dallas Population Has Access to Quality Transit? How Much of Dallas Consists of Transit Deserts? Low transit frequency is the major contributor to the low quality and efficiency of transit service. This is particularly vital for the transit dependent population relying on the timely provision of public transit to schedule their trips (e.g., business) on a daily basis. Not only do these groups need frequent service at the start of the day, but also reliable frequency at the end of the day as well. Using GTFS data, we computed transit frequency levels as the average number of public transport service headways per hour. Transit frequency was classified based on the frequency classes, which are defined as follows in Table 8. The classification is based on the recent work by Bok and Kwon (Bok & Kwon, 2016) and is modified by the UTA team. Table 8: Classification of Transit Service Quality by Frequency (Source: GTFS Data, 2017) Transit Average Headway Frequency High More than 10 Services per Hour Less than 6 min Medium 4 - 10 Services per Hour From 6 min to 15 min Low Less than 2-4 Services per Hour More than 15 min-30 Very low

Less than 2 Services per Hour

More than 30 min

Transit deserts

No Service

No operation

Figure 9 shows the distribution of transit frequency modeled for the DART service area by census blocks for average weekdays (6am â&#x20AC;&#x201C; 9pm). While the concentration of high and medium transit frequency can be observed in the Dallas CBD and surrounding areas (shown in red), offering access within walking distance to transit users, the majority of areas experience low transit frequency and no transit service shown with yellow and gray respectively.

Figure 9: Average Transit Service Frequencies for a Typical Weekday in DART Service Area

Figure 10: Average Transit Service Frequencies for a Typical Weekend in DART Service Area 28

Transit service frequency for the average weekday, however, may not be the best indicator since the frequency of transit services varies based on the time of the day (peak hour versus off-peak hour) and day of the week (weekday versus weekend). To account for this variation, we computed the average transit frequency for 6 time frames for all census blocks in the study area as shown below:      

Weekday morning peak hour: 6am – 9am Weekday afternoon peak hour: 4pm – 7pm Weekday off-peak hour: 11am – 1pm Weekday evening and night hour: 9pm – 11pm Average weekday: 6am – 9pm Average weekend: 6am – 9pm

Figure 11 illustrates the same modeled transit frequency service for four different time frames of the weekday. All four maps show the same spatial patterns of frequency distribution. The downtown area, as expected, receives the best frequency of service, followed by neighborhoods southwest of downtown. The transit service frequency in other neighborhoods in the city is mixed. We can see census blocks with high service quality and, on the other hand, the considerable number of census blocks that are transit deserts (receive no service). Comparing the four weekday time frames, we found the patterns of transit frequency service to be relatively similar. High quality service is noticeable in areas such as downtown Dallas, Addison Circle, south of Dallas, and University Park. DART offers relatively less service frequency to the areas outside the city boundary of Dallas, which is a result of a lower population density in these areas. Modeling the midday frequency from 11am to 1pm for the DART service area, we found that apart from a high concentration of high quality service in the downtown area, University Park, North Oak Cliff, and Bishop Arts District, the level of transit frequency reduces again mostly to a low level for most areas. The frequency of transit service in late hours (9pm – 11pm) decreases considerably. Except for a few places offering high frequency transit service such as downtown Dallas and further to the south, as well as North Oak Cliff and UTD Campus in Richardson, a great portion of transit dependent groups have to wait for more than 15 minutes for the arrival of transit. An even greater number of census blocks are transit deserts lacking any service provided for these groups shown in gray.

Figure 11: Average Transit Service Frequencies for Four Different Time Frames of a Typical Weekday in DART Service Area 30

Transit service frequency could also vary between weekends and weekdays (See Table 9, and Figures 9 & 10). The percentage of the population living in transit deserts is similar on weekdays and weekends. District 12 is the highest with 56% on weekdays and 63% on weekends followed by District 8 (48% on weekdays and 54% on weekends) and District 3 (44% on weekdays and 58% on weekends). For populations who do not have access to DART transit services, there is no difference between weekdays and weekends. On the other hand, on an average weekday, more than 33% of the population in District 1 and about 32% of the population in District 14 has the best quality transit service (which means their transit waiting time is less than 6 min.). Table 9: Transit frequency for Weekday versus Weekend by Council District in Dallas Average weekday

High

Medium

Low

Very Low

Transit Desert

Low

Transit Desert

Medium

Very Low

High 33

Transit Frequency

Council District 1 Council District 2 Council District 3 Council District 4 Council District 5 Council District 6 Council District 7 Council District 8 Council District 9 Council District 10 Council District 11 Council District 12 Council District 13 Council District 14

Average weekend

Across council districts on average weekdays, only 5% of the population receives very low frequency transit service (waiting time is more than 30 min.). District 3 is again an exception with 13% of residents having to wait more than 30 minutes for transit. Speaking of areas with very low transit frequency, the weekend timeframe show a different pattern. On average, more than 25% of the population in all council districts receives very low frequency transit service (waiting time is more than 30 min.).

This is mainly due to the fact that transit demand is typically lower on weekends versus weekdays and it may not be financially feasible for transit agencies to operate on the same frequency levels on weekends. However, 30 minute wait times for transit causes transit to be a far less viable and efficient mode of transportation which in turn leads to an even lower demand. Perhaps one possible alternative for DART is to look at other modes of public transit in addition to fixed-way routes for weekends. The idea of public Uber, or other types of demand-response transit options that offer ride sharing and are particularly designed to serve low-income transit dependent populations could be more efficient and cost effective than traditional bus or rail service. Weekday and weekend trips are also different in terms of the trip purpose. Typically, the majority of weekday trips are work-related while weekend trips are mostly non-work (shopping, entertainment, etc.). This could be another reason to justify the adoption of other creative and innovative transit options for weekends. We also computed and compared transit service frequencies for four time frames (2 peak hour time frames, 1 off-peak hour time frame, and 1 late evening hour time frame) as shown in Table 10. The two peak hour time frames effectively show the same patterns. In all time frames, on average about 30% of the Dallas population is located in transit deserts with no transit service. This is consistent across council districts with few exceptions. More than half of residents in Districts 3, 8, and 12 live in transit deserts with no transit service. This ratio also depends on the time of the day. In District 3, for example, the percentage of residents living in transit deserts increases from 44% to 54% when transitioning from peak to off-peak hours. With respect to access to high quality transit, again the challenge is the transition from peak to off-peak hours particularly in the late evening hours. On average, 18% and 22% of the population in Dallas have access to high frequency transit during the morning and afternoon peak hours, respectively. Another 40% and 42% have access to medium quality service (6-10 min. waiting time) during the morning and afternoon peak hours, respectively. However, when transitioning to off-peak hours, only 9% of the Dallas population has access to high quality (less than 6 min. waiting time) transit and only 26% has access to medium quality (6-15 min. waiting time). This is even worse for late evening trips when more than half of the population in Dallas has to wait for 30 minutes for transit or has no transit service at all. A considerable portion of low-income and transit dependent populations have to work more than the regular 9am - 5pm schedule. Therefore, the late evening transit schedule could be their only mode of transport if they donâ&#x20AC;&#x2122;t drive. The same applies to off-peak early morning service frequency (4am - 6am). 32

As mentioned previously, the lower transit demand for the general population in early morning and late evening off-peak hours makes it financially and functionally a challenge for DART and other transit agencies to provide high frequency transit service despite the necessity for low income transit dependent populations. This challenge could be addressed partially by rethinking transit routes and the level of service for areas with a concentration of poverty and transit dependent populations. It could also be addressed by thinking beyond the traditional fixed-route transit options by using more creative and innovative public transit options that are complementary to the existing system. Table 10: Transit Frequency for Four Different Time Frames of a Typical Weekday by Council District in Dallas. 6am - 9am

4pm - 7pm

9pm - 11pm

High

Medium

Low

Very Low

Transit Desert

High

Medium

Low

Very Low

Transit Desert

High

Medium

Low

Very Low

Transit Desert

High

Medium

Low

Very Low

Transit Desert

42 28 7 24 16 21 20 7 7 17 5 4 21 32

44 48 28 49 39 40 44 28 46 47 44 33 26 47

1 13 10 7 13 8 5 9 7 1 16 4 7 14

0 2 11 5 4 6 5 8 1 6 10 3 2 2

12 9 44 15 28 24 27 48 39 29 25 56 44 4

29 15 1 7 1 11 10 1 1 8 1 4 3 34

38 29 25 39 24 25 32 14 22 33 20 4 27 29

9 27 6 25 10 16 6 11 20 6 11 2 14 15

12 20 14 13 37 23 24 23 18 22 42 34 13 18

12 9 54 16 28 25 28 51 39 31 25 56 44 4

45 28 13 25 19 21 24 13 16 19 11 6 21 45

41 60 28 50 44 43 42 28 43 45 52 34 32 48

1 1 5 6 6 4 2 4 1 1 1 2 1 0

1 2 10 5 4 7 5 7 1 6 10 3 2 2

12 9 44 14 28 25 27 48 39 29 25 56 44 4

17 10 0 6 1 9 4 0 0 1 0 4 2 11

45 28 20 43 24 17 31 13 22 28 24 4 25 43

4 12 9 16 10 24 1 7 7 3 1 2 13 9

21 41 17 19 37 19 36 24 33 37 49 34 16 29

12 9 54 16 28 31 28 55 39 31 25 56 44 8

Transit Frequency

11am - 1pm

Recently, as one of 11 cities in the nation, DART was awarded a Federal Transit Administration grant to pilot-test new technological solutions to coordinate first/last mile transportation. DART has used the $1 million federal grant, in addition to $300,000 of its own funds to develop the technology, in addition to a combined total of over $1.5 million in federal and DART resources to apply this technology to Plano. The Plano pilot-test included the application of a range of micro-transit and on-call services, designed to serve suburban locales more efficiently and less expensively than fixed route service. This service could be expanded to transit dependent neighborhoods with concentrations of poverty and transit-captive populations.

Component 4: Transit & Job Accessibility | How Many Jobs Could Dallas Residents Reach By Transit versus Driving? To measure job accessibility by transit, we developed a unique multimodal transit network that measures door-to-door transit travel time for block groups in the study area. The multimodal transit network accounts for the first and last mile of travel time, waiting times, transfer times, and the time transit riders spend on transit vehicles (both bus and light rail). This network provides a more realistic and in-depth understanding of transit user experience with regard to travel time and transit frequency.

Figure 12: 45-Minute Multimodal (Walking and Transit Commute Buffer) For Monday at 4:00 PM

Optimal Transit Travel Time and Accessibility? Travel time is an important factor in decision making for commuting by vehicle or public transit (Litman, 2008). The costs of travel time tend to increase considerably for the trips that exceed 34

40 minutes (Pratt et al., 2000; Li, 2003; Litman, 2006). In addition, travel time affects accessibility rates inversely. Previous studies show that greater accessibility is associated with shorter commute times by vehicle and transit and the degree of association is stronger for transit. Therefore, determining a specific threshold for travel time is crucial (Kawabata and Shen, 2007). In this study, we used 45 minutes as the acceptable travel time based on evidence from the literature as explained below: 



Perhaps the most popular accessibility tool is the “Smart Location Database” (SLD), a nationwide database developed by the Environmental Planning Agency (EPA). The SLD uses a 45-minute transit time to measure access to employment by transit (Ramsey and Bell, 2014; EPA, 20177) Another notable study, “Access Across America” evaluates job accessibility by transit for 46 metropolitan areas in the nation based on the distance decay function. Maximum time threshold by transit is 60 minutes, but the jobs with 60-minute transit times receive the lowest weight (Owen and Levinson, 2014). AllTransit is another popular tool that uses a 30-minute travel time to measure job accessibility by transit. AllTransit provides a ranking of U.S. cities based on their transit performance (AllTransit, 20178). Travel time by transit has also been used as a performance measure to identify funding priorities for major transportation projects. The State of Virginia, for example, has recently used this method requiring the Virginia Department of Transportation (VDOT) and the Commonwealth Transportation Board (CTB) to develop a prioritization process for making funding decisions based on economic development, accessibility, safety, congestion mitigation, and environmental quality. For accessibility features, Virginia considered access to jobs within a 45-minute drive and 60-minute transit ride (SMART SCALE, 2017).

In addition to the professional applications, the optimal travel time for transit is also a topic of discussion in the academic literature. Shen (2001), for instance, studied spatial variation in job accessibility in the Boston Metropolitan Area. Using a 30-minute travel time for vehicles and transit, the research concluded that vehicle commuters have higher access rate than transit commuters; public transit provides very few residential locations with access rates above average. The study recommended removing spatial barriers to access to employment opportunities especially for low-income neighborhoods. On the same note, Kawabata and Shen (2007) have examined commuting inequality between vehicles and public transit in the San Francisco Bay Area. They also used a 30-minute travel time 7 8

https://www.epa.gov/smartgrowth/smart-location-mapping#Trans45 http://alltransit.cnt.org/methods/

to calculate job accessibility by transit. The study showed significant inequality in job accessibility and commuting time between vehicles and public transit and in different locations in the study area. The results suggested shorter transit commutes to support public transit. Hess (2005) also focused on accessibility for low-income individuals in the Buffalo-Niagara region. The study again used a 30-minute travel time for both vehicles and transit.

Job Accessibility by Transit We used the General Transit Feed Specification (GTFS) for transit modeling. GTFS is a common format for public transportation schedules and associated geographic information, which is published by public transit agencies and can be used for analyzing transit frequencies. We then used the network analyst tool in GIS to create a 45-minute buffer from the centroid of each census block group. In order to compare weekday transit accessibility with weekend transit accessibility, 45-minute buffers were created for different times of the day. For instance, Figure 12 presents an example of a 45-minute transit buffer for a typical weekday (Monday at 4:00 PM). The outcome of this analysis shows the percentage of jobs that can be accessed within a 45minute distance at census block group level. As shown in Table 11, on average each block group in the City of Dallas has access to 5% of jobs by a 45-minute transit commute. This ratio is even less for the DART Service Area with 4% of jobs being reachable by transit. Considering public transit as a critical mode of transportation for low wage workers, only 4% of low wage jobs in Dallas and 3% of them within the DART service area are reachable with a 45 minute transit commute. Table 11: Average Job Accessibility by 45 Minute Multimodal Transit Commute in Dallas Weekday

Saturday

Sunday

Total Jobs

5.3%

4.2%

3.9%

Low wage jobs

4.3%

3.4%

3.2%

A closer look at the level of access to jobs by transit (Table 12) confirms that on average about 30% of the population in Dallas and 41% of the population in the transit dependent core have access to less than 1% of regional jobs within a 45 minute transit commute. Indeed, more than 65% of residents living in the transit dependent core have access to less than 4% of regional jobs by a 45-minute transit (and walking) commute time. In other words, more than 65% of residents in the transit dependent core have to spend at least 1.5 hours commuting to gain access to less than 4% of jobs if they want to (or have to) take transit. 36

Compared to other aspects of the transit system we studied, lack of adequate access to jobs by transit appears to be the greatest barrier for considering transit as the major mode of transportation for all and particularly for the low income, transit dependent population. Table 12: Comparison of Job Accessibility by 45 Minute Multimodal Transit Commute for Various Geographies in the Study Area access to less than 1% of jobs

access to 1-4% of jobs

access to 4-10% of jobs

access to more than 10% of jobs

DART Service Area

37.06%

33.41%

20.31%

9.22%

City of Dallas

30.41%

24.61%

29.64%

15.34%

Transit Dependent Core

41.57%

23.83%

26.95%

7.66%

Figure 13 illustrates the spatial disparity of access to jobs by transit in Dallas. Block groups with the best job access (by transit) are located in downtown and a corridor in the northeast area of Dallas while block groups with the worst access to jobs by transit are located in Oak Cliff, Pleasant Grove, and Southwest Dallas. The same pattern could be observed from Figure 14 for access to low wage jobs by a 45-minute transit travel time.

Figure 13: Percentage of Regional Jobs Accessible by a 45-Minute Transit Time In the Study Area (LEHD Data, 2014) 38

Figure 14: Percentage of Regional Low Wage Jobs Accessible by a 45-Minute Transit Time In the Study Area (LEHD Data, 2014) 39

Figure 15: Percentage of Regional Jobs Accessible by a 45-Minute Transit Time By Council Districts in Dallas (LEHD Data, 2014)

Figure 16: Level of Job Accessibility by 45 Minute Transit Travel Time for Council Districts in Dallas (the darkest hue of each color represents better access; this graph corresponds to Table 13) Table 13: Percentage of Block Groups with Different Levels of Job Accessibility by Transit in Each Council District in Dallas Council District Council District 11 Council District 14 Council District 10 Council District 2 Council District 13 Council District 1 Council District 6 Council District 7 Council District 9 Council District 4 Council District 12 Council District 5 Council District 3 Council District 8

access to less than 1% of jobs 0% 1% 2% 4% 8% 13% 29% 32% 36% 38% 41% 72% 80% 92%

access to 1-4% of jobs 52% 1% 46% 12% 42% 12% 29% 15% 30% 26% 52% 24% 14% 8%

access to 410% of jobs 40% 43% 42% 48% 39% 68% 39% 51% 32% 34% 7% 4% 6% 0%

access to more than 10% of jobs 8% 55% 9% 35% 11% 6% 3% 2% 3% 3% 0% 0% 0% 0%

As shown in Figure 15 and Figure 16, the disparity among Dallas council districts in terms of access to jobs by transit is noteworthy. More than 92% of block groups in District 8 have transit access to less than 1% of regional jobs while another 8% has access to 1-4% of regional jobs by transit. On the other side of the spectrum, District 14 has the highest access to jobs by transit. About 55% of block groups in District 14 have access to more than 10% of regional jobs. Similarly, about 98% of block groups in District 14 and 84% of block groups in District 2 have good or very good access to jobs by transit. Also, between 72-92% of block groups in Council Districts in South Dallas (Districts 3, 5, 8) have access to less than 1% of jobs using a 45minute transit time. In other words, households in these block groups have to spend 1.5 hours commuting by transit to gain access to only 1% of jobs in the region. This presents a barrier for households in these areas to actually obtain a job if they donâ&#x20AC;&#x2122;t have physical access to it. These areas also face a concentration of poverty (see Figure 17). The median household income in the majority of these areas is less than $25,000 and as shown in Figure 17, the majority of households in these areas end up spending 2326% of their income on transportation.

Figure 17: Median Household Income by Block Group in the Study Area

The lack of access to jobs and an efficient, reliable transportation system coupled with spending about a quarter of their income on transportation essentially offers these households little to no chance of upward mobility. The areas with a concentration of poverty, in the long run, remain or possibly even face growth in the concentration of poverty.

From the planning perspective, these trends would cause the city to become even more spatially segregated and consequently Dallas could experience even more isolation in areas of concentrated poverty.

Figure 18: Percentage of Regional Jobs Accessible by a 45-Minute Transit Time by Neighborhood in Dallas (LEHD Data, 2014) 43

Figure 18, Figure 19, and Table 14 confirm the same pattern of job accessibility by transit for Dallas neighborhoods. Southwest Dallas and Pleasant Grove neighborhoods have the lowest transit access to jobs. More than 91% of block groups in Southwest Dallas and 76% of block groups in Pleasant Grove have access to less than 1% of regional jobs by a 45-minute transit time. Also, except for downtown Dallas which is expected to have sufficient access to employment due to the concentration of jobs, other neighborhoods north of downtown Dallas have relatively good access to jobs by transit and thus provide partial mobility options.

Figure 19: Level of Job Accessibility by a 45-Minute Transit Travel Time for Neighborhoods in Dallas (the

darkest hue of each color represents better access; this graph corresponds to Table 14) Table 14: Percentage of Block Groups with Different Levels of Job Accessibility by Transit in Each Neighborhood in Dallas

Dallas Neighborhood Downtown – Uptown – Design District Lake Highlands – Vickery Meadow South Dallas - Fair Park Park Cities – Highland Park – University Park – Bluffview North Dallas - Preston Hollow

access to less than 1% of jobs 1% 5% 7% 7%

access to 1-4% of jobs 0% 34% 13% 29%

11%

55%

access to access to 4-10% of more than jobs 10% of jobs 42% 57% 40% 21% 72% 9% 31% 33% 26%

Northwest Dallas – Dallas Love Field Airport Far North Dallas – Prestonwood – Northwood Hills East Dallas – Casa View - Lakewood – Lower Greenville West Dallas Oak Cliff – Kessler – Bishop Arts District Pleasant Grove Southwest Dallas – Mountain Creek Lake

11% 24%

46% 55%

37% 18%

6% 2%

30%

15%

42%

13%

40% 44% 76% 91%

3% 18% 16% 8%

57% 36% 8% 2%

0% 2% 0% 0%

We also computed access to jobs by driving to generate a comparison between the two modes and to gain a more comprehensive understanding of how efficient the transportation system is in Dallas. For each block group in the study area, we computed the total number of jobs that could be reached in a 30 minute driving time. We found that on average, 34% of jobs can be reached by in 30 minutes of driving for the City of Dallas resident. The DART service area has almost the same level of access to jobs by driving (see Table 15). When categorizing jobs by income, we found no significant difference between access to jobs from different income categories (ranging from 34%-35%). Table 15: Average Percentage of Jobs Accessible by a 30-Minute Drive Time Total Jobs

Total Jobs

Low-Wage Jobs

High-Wage Jobs

33.4%

Medium-Wage Jobs 34.0%

City of Dallas

34.6%

DART Service Area

34.2%

32.7%

33.4%

35.4%

35.7%

Table 16: Comparison of Job Accessibility by a 30-Minute Drive Time for Various Geographies in the Study Area

DART Service Area City of Dallas Transit Dependent Core

access to less than 25% of jobs

access to 25-30% of jobs

7.72%

16.70%

30.44%

33.17%

11.97%

6.01%

11.53%

31.36%

40.86%

10.23%

7.68%

17.36%

35.24%

31.18%

8.55%

access to 30-35% of jobs

access to 35-40% of jobs

access to more than 40% of jobs

Figure 20: Percentage of Regional Jobs Accessible by Driving (LEHD Data, 2014)

Figure 20 shows the spatial distribution of job accessibility by driving in the study area. The western part of Dallas and the DART service area have the greatest access to jobs by driving. This is mainly due to higher highway density and the regional accessibility to jobs in these areas. On the other hand, neighborhoods in southern parts of Dallas experience lower job accessibility by driving. While we found a notable difference in terms of access to jobs by transit between the transit dependent core and the City of Dallas as a whole, according to Table 16, there is no difference between the two geographies in terms of access to jobs by driving.

Conclusions The Dallas Area Rapid Transit light rail system is the longest and also among the oldest light rail systems in the United States, having begun in 1996 and extending in all directions around downtown Dallas. Despite this potential, transit ridership in Dallas is relatively low. DART ranks 23 out of 39 large and medium sized transit agencies in the U.S. with regard to two transit ridership indicators (passenger miles per capita and passenger trips per capita) (APTA, 2014). The city and the DFW region face equity challenges when it comes to transportation (Chetty et al., 2014). For addressing transportation equity, this study sought to identify transit dependent hot spots in Dallas and to study various components of the transit system in these areas as well as in Dallas as a whole. Accessibility to public transit plays an important role in connecting residents to jobs and opportunities. Availability of transportation can increase access to opportunity and enhance economic growth by linking residents to jobs and other essential services. In the first step, this study identified the transit dependent core in Dallas through hot spot analysis. The hot spot core covers 974 block groups that cover 34% of all block groups within the study area. While the hot spot core covers about 30% of the population, 45% of the Black population and 48% of the Hispanic population are also within this zone. The results of hot spot analysis can be used to understand transit demand catchment areas within cities and regions. Transit demand catchment areas can be defined as the core locations of transit riders with a high density of transit dependent populations. In the next steps, this study also quantified and measured various performance components of the transit system such as affordability, accessibility, coverage, and service frequency for the transit dependent core, the City of Dallas, and the DART Service Area. Below is a summary of our findings:



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A typical household in the City of Dallas spends more than 19% of its income on transportation, which is much greater than the 15% transportation affordability threshold. The City of Dallas is identified as far from having location efficient neighborhoods, with only 2% of the city neighborhoods described as being location efficient. Except for a few block groups in downtown Dallas, most of the entire study area is shown to be unaffordable in terms of transportation costs. Even though almost the entire study area is unaffordable with regard to transportation costs, the extent of unaffordability varies spatially. The spatial distribution of transportation affordability is also uneven particularly in the transit dependent core. The majority of households, living in the southwest and southeast neighborhoods of Dallas, spend between 20% and 26% of their income on transportation, which is almost at the same level as housing costs for these households. Compared to other transit agencies, DART fare options are relatively affordable. A lowincome household would spend on average about 10% of its income on transit fare by using the daily pass for the entire year (365 days). The amount is even lower if the household is qualified for the reduced fare or if the household opts to use the annual pass. According to our analysis, fare affordability is not the major barrier of transit use in Dallas except the regional pass. Still, as recommended, there are successful and effective policies that could be adopted by DART to provide affordability for target groups of the population who need it the most. Our analysis confirms that areas with a large population below the poverty level particularly in south Dallas receive better coverage and more frequent transit availability. About 37% of the population in the City of Dallas is not covered by transit during any time of the day while in the transit dependent core about 32% of residents are not covered by transit. On the other hand, 26% of the population in the City of Dallas and 31% of the population living in the transit dependent core are covered by one station with a daily average of 3-6 trips per hour. Even though the transit dependent core has slightly better transit coverage than the rest of the city, more than a third of the population in both the transit dependent core and the city is not covered by transit within walking distance during any time of the day. No physical access to transit stations means no transit use by a third of the population in Dallas which could contribute to the relatively low rate of transit ridership. Our findings call for additional investigation on first/last mile gaps in the DART transit system and the best public transit solutions tailored for each area. With respect to access to high quality transit, the challenge is the transition from peak to off-peak hours, particularly the late evening hours. On average, 18% and 22% of the 48

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population in Dallas have access to high frequency transit during the morning and afternoon peak hours, respectively. Another 40% and 42% have access to medium quality service (6-10 min. waiting time) during the morning and afternoon peak hours, respectively. However, when transitioning to off peak hours, only 9% of the Dallas population has access to high quality (less than 6 min. waiting time) transit and only 26% has access to medium quality (6-15 min. waiting time). This is even worse for late evening trips when more than half of the population in Dallas has to wait for 30 minutes for transit or has no transit service at all. A considerable portion of the low income and transit dependent population has to work more than the regular 9am - 5pm schedule. Therefore, the late evening transit schedule could be their only mode of transport if they don’t drive. The same applies to off-peak early morning service frequency (4am - 6am). One of the unique features of this study is that we developed a multimodal transit network that measures door-to-door transit travel time for block groups in the study area. The multimodal transit network accounts for the first and last mile of travel time, waiting times, transfer times, and the time transit riders spend on transit vehicles (both bus and light rail). This network provides a more realistic and in-depth understanding of transit user experience with regard to travel time and transit frequency. A closer look at the level of access to jobs by transit confirms that on average about 30% of the population in Dallas and 41% of the population in the transit dependent core have access to less than 1% of regional jobs in a 45 minute transit commute time. Indeed more than 65% of residents living in the transit dependent core have access to less than 4% of regional jobs by a 45-minute transit (and walking) commute time. In other words, more than 65% of residents in the transit dependent core have to spend at least 1.5 hours commuting to gain access to less than 4% of jobs if they want to (or have to) take transit. Compared to other aspects of the transit system we studied, the lack of adequate access to jobs by transit appears to be the greatest barrier for considering transit as the major mode of transportation for all and particularly for the low income, transit dependent population.

List of References 2014 American Community Survey 5-Year Estimates. United States Census Bureau. APTA. 2014. 2014 Public Transportation Fact Book. Washington, DC: American Public Transportation Association. Bok, J., & Kwon, Y. (2016). Comparable Measures of Accessibility to Public Transport Using the General Transit Feed Specification. Sustainability, 8(3). doi:10.3390/su8030224 Bureau, United States Census. 2014. Cervero. 1982. "The transit pricing evaluation model: a tool for exploring fare policy options." Transportation Research A: Gen 313-323. Cervero, R.,. 1990. "Transit pricing research." Transportation 17 (2) 117-139. Chetty, Hendren, Kline, & Saez. 2014. Is the United States still a land of opportunity? Recent trends in intergenerational mobility. National Bureau of Economic Research. Center for Transit-Oriented Development and Center for Neighborhood Technology. The Affordability Index: A New Tool for Measuring the True Affordability of a Housing Choice. Metropolitan Policy Program. The Brookings Institutions: Urban Markets Initiative. 2006. CNT, The Center for Neighborhood Technology. 2017. Glossary of Terms. http://htaindex.cnt.org/glossary/. DART. 2017. DART Fares. July 15. https://www.dart.org/fares/fares.asp. Data, GTFS. 2017. "GTFS Data." Specification., General Transit Feed. Ewing, R., & Cervero, R. 2010. Travel and the built environment: a meta-analysis. Journal of the American planning association, 76(3), 265-294. Ewing, R., Hamidi, S., Grace, J. B., & Wei, Y. D. 2016. Does urban sprawl hold down upward mobility?. Landscape and Urban Planning, 148, 80-88. Farber, Bartholomew, Li, Pรกez, & Habib. 2014. "Assessing social equity in distance based transit fares using a model of travel behavior." Transportation Research Part A: Policy and Practice, 67 291-303.

Garret and Taylor. 1999. "Reconsidering Social Equity in Public Transit." Berkeley Planning Journal 13 6-27. Getis and Aldstadt. 2010. Constructing the spatial weights matrix using a local statistic. Perspectives on spatial data analysis. Berlin: Springer Berlin Heidelberg. 147-163. Grengs, Joe. 2001. "Does Public Transit Counteract the Segregation of Carless Households? Measuring Spatial Patterns of Accessibility." Transportation Research Record 1753: 3-10. Hamidi and Ewing. 2015. "Is Sprawl Affordable for Americans? Exploring the Association between Housing and Transportation Affordability and Urban Sprawl." Journal of the Transportation Research Board. Hamidi, Ewing, & Renne. 2016. "How Affordable Is HUD Affordable Housing?" Housing Policy Debate, 26(3) 437-455. Hess, D. B. 2005. Access to employment for adults in poverty in the Buffalo-Niagara region. Urban Studies, 42(7), 1177-1200 Hickey, Lu, & Reddy. 2010. "Using quantitative methods in equity and demographic analysis to inform transit fare restructuring decisions." Transportation Research Record. 2144, 80â&#x20AC;&#x201C; 92. Hine,, Julian, and Fiona Mitchell. 2001. "Better for everyone? Travel experiences and transport exclusion." (Urban Studies) 38 (2). Institute of Urban Studies. 2017. IUS Output. Arlington, TX: University of Texas at Arlington. Jiao and Dillivan. 2013. "Transit Deserts: The Gap between Demand and Supply." Journal of Public Transportation, 16 (3) 23-39. Jorgensen, Preston. 2007. "The relationship between fare and travel distance." Journal of Transport Economics and Policy 41 (3), 451â&#x20AC;&#x201C;468. Kawabata, M., & Shen, Q. 2007. Commuting inequality between cars and public transit: The case of the San Francisco Bay Area, 1990-2000. Urban Studies, 44(9), 1759-1780. Kenyon, Susan, Glenn Lyons, and Jackie Rafferty. 2002. "Transport and social exclusion: investigating the possibility of promoting inclusion." (Journal of Transport Geography) 10 (3). Li, Y. W. 2003. Evaluating the urban commute experience: A time perception approach. Journal of Public Transportation, 6(4), 3. Lineberry, Robert L. 1977. Equality and Urban Policy: The Distribution of Municipal Services. Beverly Hills, Ca: Sage Publication. Litman, Todd. 2003. "Measuring transportation: traffic, mobility and accessibility." (ITE Journal) 23 (10).

Litman, T. 2008. Valuing transit service quality improvements. Journal of Public t Litman, Todd. 2006. Travel time. Transportation Cost and Benefit Analysis. Victoria Transport Policy Institute

ransportation, 11(2), 3 Longitudinal Employer-Household Dynamics. Data. U.S. Census Bureau. 2002 & 2014. Mitchell, A. 1999. The ESRI guide to GIS analysis, volume 1: geographic patterns and relationships. . Redlands, CA: ESRI . Murray, Alan T. 2001. "Strategic analysis of public transport coverage." Socio-Economic Planning Sciences 35 (3): 175-188. NCTCOG Shapefile. 2017. Arlington: NCTCOG. Nuworsoo, Deakin, & Golub. 2012. "Equity Impacts of Transit Fare Proposals: A Case Study of AC Transit." In Journal of the Transportation Research Forum (Vol. 47, No. 3). Nuworsoo, Golub, & Deakin. 2009. "Analyzing equity impacts of transit fare changes: case study of Alameda-Contra Costa Transit California." Evaluation and Program Planning 32 (4), 360â&#x20AC;&#x201C;368. Owen, A., & Levinson, D. (2014). Access across America. Transit, 4(2014), 5. Pratt, R. H., Turnbull, K. F., Evans, I. V., John, E., McCollom, B. E., Spielberg, F., ... & Kuzmyak, J. R. 2000. Traveler response to transportation system changes: Interim handbook (No. TCRP Project B-12,). Pucher, J. 1982. "A Decade of Change for Mass Transit." Transportaion Research Record (858): 48-57. Ramsey, K., & Bell, A. (2014). Smart location database. Washington, DC. Salvin, P. Factoring Transit Costs into Housing Affordability. Urban Land: The Magazine of the Urban Land Institute, 2014. https://urbanland.uli.org/news/factoring-transit-costshousing-affordability/. Accessed July 8, 2017. Sanchez and Brenman. 2007. The Right to Transportation: Moving to Equity. Chicago, IL.: APA Planners Press. Shen, Q. 2001. A spatial analysis of job openings and access in a US metropolitan area. Journal of the American Planning Association, 67(1), 53-68. Stanley, J., and K. Lucas. 2008. "Social exclusion: What can public transport offer?" (Research in Transportation Economics) 22. Stanley, Janet, and Dianne Vella-Brodrick. 2009. "The usefulness of social exclusion to inform social policy in transport." (Transport Policy) 16 (3). Steiss, Todd. 2006. Calculating/Analyzing Transit Dependent Populations Using 2000 Census Data and GIS. Washington: U.S. Department of Transportation. 52

Title VI/ADA Programs Manager. 2012. Federal Highway Administration. 04 12. Accessed 08 01, 2017. https://collaboration.fhwa.dot.gov/dot/fhwa/ReNepa/Lists/aDiscussions/DispForm.aspx ?ID=8891. Transit Future Campaign. 2013. Transit Deserts in Cook County. CNT. Zwald, Marissa L., James A Hipp, Marui W. Corseuil, and Elizabeth A. Dodson. 2014. "Correlates of Walking for Transportation and Use of Public Transportation Among Adults." Preventing Chronic Disease 11.

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