An Analysis of Transportation Demand in Atlanta: How Much Will Atlanta’s Proposed MARTA Expansions Increase Ridership? By SAM TUCKER* Transportation in Atlanta is considered poor by national standards. I look at the MARTA light rail expansion proposals from the Atlanta City Government. I use survey data from the 2011 Atlanta Regional Commission’s Household Travel Survey to estimate travel demand. Since explicit coordinates are not given for each survey participant, I use Traffic Analysis Zones as proxies to estimate distances. A discrete choice multinomal logit model is then estimated by maximum likelihood estimation. Change in predicted ridership is then calculated by adding the proposed train stations to the choice set. Clifton and Campbellton are predicted to be the most successful lines, and then I examine the demographics of those who benefit the most from the MARTA expansion. Public transportation is an important duty of city governments, and it is becoming more important than ever with the environmental and traffic concerns of the 21st century. The need for improvements to public transportation could not be overstated in Atlanta. In 2017, Atlanta ranked in the top ten out of 1,360 cities worldwide for most time drivers spent in congestion, according to INRIX’s Global Traffic Scorecard. In addition, Atlanta ranked in the bottom ten of eligible American metropolitan areas in access to transit, according to a Brookings Institution report in 2011. Also, the Federal Transit Administration has found that MARTA’s passenger trips decreased by 2.6 percent in 2017 (Green, 2018). Attempting to remedy the dire state of public transportation in Atlanta, in 2016 the city approved a half-cent sales tax that will raise $2.5 billion over the next 40 years (Freemark, 2017). However, the list of all transportation improvements for eligible funding covers $10.3 billion in capital projects operations and maintenance. Many projects were considered initially. However, according to the Atlanta Journal-Constitution, as of May 2018 the list of light rail projects being considered has been narrowed to six: Campbellton Line (C), Clifton Corridor (D), Beltline-Loop Northeast (A), Beltline-Loop Southwest (B), Crosstown Downtown West Extension (F), and the Crosstown Downtown East Expansion (E).1 Some other projects are being considered, such as bus rapid transit and arterial rapid transit, but I will be focusing my analysis on the light rail expansion projects. The goal is to understand what drives demand for public transportation, and how much each of the expansions will increase MARTA ridership. To answer this question, I will use the multinomial logit framework laid out by McFadden, et al. (1977) and expanded upon by Train (2009) and apply it to the survey data from Atlanta Regional Commission (2011). The survey * This paper was submitted as a graduation requirement to the Department of Economics, Terry College of Business, University of Georgia, Athens, GA (email: [email protected]). Thanks to Professor Josh Kinsler for advice and guidance. 1 A map of the proposed new lines can be accessed at https://www.ajc.com/news/local-govt--politics/local-update- marta-plan-boosts-atlanta-beltline-cuts-funding-for-emory-line/ILPofMcJKbAw0e3KNBBIUI/ AUGUST 2019 THE UGA JOURNAL OF ECONOMICS 2 data contains demographic information, information on home and work location, and transportation mode of choice for Atlanta commuters in 2011. This model will estimate parameters so that I can determine which factors are relevant for demand of transportation. Afterwards, I plot the coordinates for the proposed stations and add them to the choice set, to see which stations give the highest predicted increase in ridership. I find that the Clifton Corridor (A), and Campbellton Line (C) are predicted to be the most successful lines. I. Literature Review Billings (2011) looks at light rail transit (LRT) in Charlotte, North Carolina. He notes that economists seem to be puzzled by the popularity of LRT expansion in U.S. cities, due to their limited time savings over bus transit and their high costs. However, urban planners notice that LRT usually commands increased ridership over bus. Billings (2011) claims that increased utility of living near an LRT station should be capitalized in housing values. Previous literature has identified four ways through which rail transit affects housing values: 1) the direct distance between a residential property and a transit station; 2) the positive effect of transit stations on commercial development; 3) increased parking congestion near transit stations; and 4) greater opportunity for crime with improved access to neighborhoods with LRT stations. The author also notes that areas with LRT stations are usually targeted by cities for future investment to improve land use. Using a differences-in-differences estimator, he finds positive capitalization in neighborhoods with LRT, specifically, 4.0 percent for single-family properties, 11.3 percent for condominiums. However, no capitalization is found for commercial properties. This evidence suggests that LRT may be a better investment in economic development for certain neighborhoods rather than specifically as a transportation option. In conclusion, Billings (2011) estimates a neighborhood benefit of $97.2 million due to LRT expansion in Charlotte. However, costs for the new South Line were $450 million. He notes that the full benefits may not be captured in his estimates, because pollution and traffic congestion reduction as well as other unobserved factors are not captured in his estimates. Baum-Snow et al. (2005) notes that while 16 American cities have spent a combined $25 billion on rail transit expansion between 1970 and 2000, the number of Americans taking public transportation has decreased by half during that time period. However, ridership increased in areas within two kilometers of a new line and beyond ten kilometers of the city center. The issue is that since cities are becoming decentralized, a smaller fraction of the population now has access to the existing stations. Atlanta is one of the cities in the analysis, and while only five percent of people were taking public transit in 2000, the authors estimate that 11 percent would take public transit in Atlanta if the population were at its 1970 spatial distribution. Also, thirteen percent of commuters lived within two kilometers of a transit line in 2000, a two percent increase from 1990. Their model of transit use insists that those who live close to the center of the city will tend to walk or take a bus a short distance to the rail line and those who live further out will drive to the line. This is useful information, because I will define costs of transit options in my model assuming a cost of driving to the nearest station. The authors present several arguments that could justify large public investments in public transit. Since rail transit exhibits increasing returns, it may be optimal to subsidize transit up to the point where the average social cost of taking public transit is less than the average social cost of driving. Also, negative externalities caused by driving, such as congestion and pollution, could be mitigated by more people taking public transit. They estimate that a little over 19,000 people switched to public transit between VOL. 1 NO. 2 Tucker: Transportation Demand in Atlanta 3 1980-2000, with a daily savings of 31,100 hours and $624,000. In conclusion, they find that there are significant increases in ridership in areas greater than ten kilometers from the city center. Most of the effects close to the city center are found in bus riders switching to rail. While this does not increase share of the population taking public transit, it does have value in reducing commute times. II. Data To estimate demand I use data from the Atlanta Regional Commission’s 2011 Household Activity Travel Survey. Here is how the ARC describes the survey: The purpose of the survey was to update ARC’s travel demand model and get a better understanding of travel behavior in a 20 County Region. Households were randomly selected to participate in the survey. Those agreeing to participate were assigned a one-day travel period and asked to track of all trips during that period. Information was collected from 10,278 households. When weighted to reflect the population of the region, the households represent 25,810 persons, 93,713 trips and 21,270 vehicles. (ARC, 2011) The sample size for my analysis is 5,077 due to incomplete information in the survey. I do not believe that incompletes will bias work trip estimates, because most of the incompletes are either children under eighteen or senior citizens who are likely retired. What is important for my analysis is the location of each person’s house, their place of work, and their transportation mode of choice. Table 1 shows the demographics of my sample divided by transportation modes of choice. Table 1: Demographics by Mode of Transportation to Work Variable Train Bus Car % of Sample 1.65 1.2 97.15 % with Income over $75,000 46.4 37.7 61.1 % with Bachelors Degree or higher 61.9 45.9 54.4 Average Age 42.54 43.67 45.55 Average Household Size 2.35 2.72 2.95 % Owning Home 73. 68.8 88.9 % with Employer Provided Parking 37 57 93 % with Employer Subsidized Transit 51 37 11 Average Distance to Work (miles) 12.87 18.04 11.29 Average Distance to nearest Train Station from Home (miles) 4.62 11.54 14.28 Average Distance to nearest Train Station from Work (miles) 1.16 1.84 10.37 Average Distance to nearest Bus Station from Home (miles) 1.95 7.41 9.33 Average Distance to nearest Bus Station from Work (miles 0.48 0.59 6.41 Average Estimated Daily Cost of Travel $4.14 $4.52 $3.81 I limited modes of choice to bus, train, or car.