Social Equity in Distance Based Fares Steven Farber, University of Utah GIS in Transit October 16-17, 2013

Background UTA transitioning from a flat fare to a distance based fare Title VI and EJ requirements (differential impacts) Different populations will be impacted differently since trip gen’s and distances travelled vary systematically with demographics Understanding of travel behavior required to assess differential impacts

Fares and Sustainability Competing goals of transit agency o Economic – Increase Revenue o Environmental – Decrease automobile use o Social – Provide transit service to those in need or equally to all Distance based fares o Economically efficient (capturing external costs) o Socially beneficial (shorter but more frequent trips) o Environmentally detrimental (increased costs for long distance discretionary riders)

Fares and Equity EquityFairnessTransit FareTaxation  Flat fare per tripFixed tax dollar amount regardless of income  / Distance based fare (DBF) Single tax rate for all DBF with reduced cost of successive starts Progressive tax rates (sliding scale) Social equity in transportation is summarized by Sanchez as the distribution of “benefits and burdens from transportation projects equally across all income levels and communities” Fairness: Whether the costs and benefits are equal after taking needs, means, and abilities into consideration. Are we interested in equality or fairness?

Research Questions What are the social equity and fairness impacts of a transition to DBF? How can travel behavior be used to assess social equity in this case? If DBFs are generally desirable, how can we find and address exceptions to this rule?

Data Utah Household Travel Survey o Spring 2012 o 1 day travel diary o 9,155 Households o 27,046 People o 101,404 Trips Filtered to only those residing in UTA’s core service area - 68% of respondents # daily of transit trips Daily distance travelled by transit

Ridership Percentage Trips Distance Travelled (miles) Household Income No Answer Under $35, $35,000 - $49, $50,000 - $99, $100,000 or more Hispanic Yes No Prefer not to answer Race White or Caucasian All other Age years old years old years old years old years old >65 years old Ridership Percentage Trips Distance Travelled (miles) Education High school or less Some College/Vocational/Associates Bachelors Grad/Post Grad Licensed Yes No Number of vehicles Zero vehicle household vehicle household vehicle household vehicle household Home Ownership Rent Own Other Residence Type Single-family house (detached house) Apartments Other Observed Travel Behavior

Selection of Fares UTA is considering fares that consist of a flat component and a distance-based component For this study, we selected a revenue-neutral fare o $ $0.19 per mile Distance is measured as Euclidean distance so that users are not penalized by indirect network design

Transit Trips Distance Travelled Flat Fare Distance Based Fare Percentage Change Household Income Under $35, % $35,000 - $49, % $50,000 - $99, % $100,000 or more % Hispanic Yes % No % Race White or Caucasian % All other % Age years old % years old % years old % years old % years old % >65 years old %

Method Estimate a joint model of transit trip generations and distance travelled Spatially expanded coefficients – controls for contextual information not captured in the dataset Convert travel behavior to fares and compare results

Ref. Low Income & Education Low Income & Elderly Non-White

Ref. Low Income & Education Low Income & Elderly Non-White

Conclusions Distance based fares generally result in cheaper fares for those who need it most Pockets of mismatch exist – suburbanization of the poor poses a problem Burden on long-distance low-income travellers can be mitigated through reduced flat-fare components Changes in price are likely to shift wealthy discretionary riders back to their cars, but it may attract a plethora of new low-income riders

Next Steps Developing a GIS Decision Support System Analysts at UTA can compute fare surfaces for different demographic profiles and different fare structures Targeted studies of riders in particular at-risk neighborhoods identified by the DSS

Acknowledgements Xiao Li, Graduate RA Keith Bartholomew, UofU Antonio Páez, McMaster University Khandker M. Nurul Habib, University of Toronto Utah Transit Authority Partial support from: o National Institute for Transportation and Communities (DTRT12-G-UTC ) o National Science Foundation (BCS )