Evaluation of Alternative Fare Structures for Boston's Subway
Evaluation of Alternative Fare Structures for Boston’s Subway Robert GoodspeedPhD Student, MIT Department of Urban Studies and Planning 9 May 2011 ESD.86 Final Project Prof. Larson and Welch Photo: Dave Morris (Flickr: Daveybot)
Project Overview• Context – Fourth busiest subway in the U.S. – Also operates: • Commuter Rail • Ferries • Bus Lines • Paratransit Services• Project: What if the MBTA adopted distance-based or peak fares for the subway? – Why subway? • Majority of fare revenue • The system’s ―core‖ • Aging infrastructure in need of repair – Compare to what? • Higher flat fares (NYC) • Distance-based fares (Washington, D.C. - WMATA) – Methodology? • Estimate round trips based on available boarding data • Apply estimated price elasticities
MBTA Finances (Abridged)Forward Funding (2000) RealityDedicated portion of sales tax, assumed Sales tax revenue declinedwould increase 3% per yearAssumed operating expenses would not Increased at 5% per year driven by energyincrease and employee wages and benefitsGiven $2 billion in debt, mostly Big Dig Annual debt service exceeds fare revenue,related must restructure each yearResults• $8.6 billion in debt, nearingdebt ceiling• Severe maintenance backlog• Band-aid fixes negotiatedeach year with state legislature.
Current Fare StructureHow much are people paying for the subway? (Needed for elasticity)• Average LinkPass owner: $1.13• Tourists: $2.00• System wide average: $1.26
Alternative Fare Structures1. $2.00 flat2. $2.25 flat3. WMATA Peak ($.20 surcharge)4. WMATA Distance5. WMATA Peak & Distance WMATA Fare Structure $6.00 $5.00 y = 0.272x + 1.186 R² = 0.998 $4.00 Fare $3.00 $2.00 $1.00 $0.00 0 2 4 6 8 10 12 14 16 18 20 Trip Distance (Miles) Regular Off-Peak Regular Fares Linear (Regular Fares)
MBTA Boardings, 2008 14,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 11AM 12AM 3AM 4AM 5AM 6AM 7AM 8AM 9AM 10AM 1AM 2AM 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM 9PM 12PM 10PM 11PM• Morning boardings at each station assigned to stations according to the PM boarding probability distribution• Trip distribution applied to origin-destination distance matrix• Completed for all 52 stations with data (excluding Green Line and Silver Line)
0.01 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.02 0 Airport (0.6) Alewife (0.6) Andrew Square (1.5) Aquarium (1.7) Ashmont (1.8) Back Bay (1.9) Beachmont (1.9) Bowdoin (1.9) Braintree (2.1) Broadway (2.1) Central Square (2.2) Charles MGH (2.2) Chinatown (2.2)Community College (2.4) Copley Square (2.5) Davis Square (2.5)Downtown Crossing (2.7) Fields Corner (3.0) Forest Hills (3.0) Goverment Center (3.0) Green Street (3.2) Harvard (3.2) Haymarket (3.5) Jackson Square (3.7) JFK/U Mass (3.9) Kendall Square (3.9) Malden Center (4.0) Mass Ave (4.1) Maverick (4.1)N.E.Medical Center (4.6) North Quincy (4.6) North Station (5.2) Oak Grove (5.2) Orient Heights (5.2) Trip Length Estimation Park Street (5.3) Porter Square (5.7) Airport Station Destination PMF Quincy Adams (6.2) Quincy Center (6.2) Revere Beach (6.3) Roxbury Crossing (6.9) Ruggles (6.9) Savin Hill (6.9) Shawmut (7.0) South Station (7.5) State Street (7.5) Stony Brook (7.8) Suffolk Downs (7.8) Sullivan Square (8.2) Wellington (9.0) Wollaston (10.3) Wonderland (11.6) Wood Island (13.4)
Elasticities Mode Single-ride Pass Trips Total Trips Bus -0.12 -0.51 -0.21 Subway -0.23 -0.38 -0.29 Commuter Rail -1.00 -0.04 -0.44• Rate of change of demand in response to price, varies along the demand curve• Estimated by CTPS after 2007 fare restructuring• Reflect total transportation demand (discretionary travel) as well as cross- elasticities with other modes• Expected to vary with time, as well as differ between short-term and long- term• In long term, transportation cost a determinate in land value and density
Finding the Balance PublicallyPublic Good Provided Private Good
Conclusions• Revenue – Core subway relatively inelastic, could raise significant revenue with fare increases with modest losses in ridership – MBTA not ―too small‖ for WMATA-type fares to raise significant revenue – Result of passes is real cost for most riders below the full fare• Methodology – Extensive analysis possible with AFC data; suggestions by academic studies not followed aggressively in Boston – Next step is linking trips with unique card IDs, raises privacy issues• Policy – Unresolved debate between alternative conceptions of transit – Will fiscal crisis force the state to address the issue?
ReferencesRidership data courtesy Central Transportation Planning Staff, Metropolitan Area Planning CouncilBarry, J.J., R. Newhouser, A. Rahbee, and S. Sayeda. 2002. Origin and destination estimation in New York City with automated fare system data. Transportation Research Record: Journal of the Transportation Research Board 1817 (-1):183-187.Central Transportation Planning Staff. 2006. Impact Analysis of a Potential MBTA Fare Increase and Restructuring in 2007. Boston, MA.———. 2008. Impact Analysis of the 2007 MBTA Fare Increase and Restructuring. Boston, MA.Chan, J. 2007. Rail transit OD matrix estimation and journey time reliability metrics using automated fare data.Massachusetts Transportation Finance Commission. 2007. Transportation Finance in Massachusetts: An Unsustainable System. Boston, MA.Sanchez, Thomas W., Marc Brenman, Jacinta S. Ma, and Rich Stolz. 2007. The right to transportation : moving to equity. Chicago: APA Planners Press.Schofield, Mark L. 2004. Evaluating the costs and benefits of increased funding for public transportation in Chicago. Thesis S.M. --Massachusetts Institute of Technology Dept. of Civil and Environmental Engineering 2004.Wilson, Nigel H.M., Jinhua Zhao, and Adam Rahbee. 2009. The Potential Impact of Automated Data Collection Systems on Urban Public Transport Planning. In Schedule-Based Modeling of Transportation Networks: Theory and Applications, edited by N. H. M. Wilson and A. Nuzzolo. New York, NY: Springer.