University of California Institute of Transportation Studies

Commuter rail is known to have a “first- and last-mile” problem (i.e., a lack of options for getting commuters to and from a rail station). The first- and last-mile dilemma creates inequalities in access. For example, high-income commuters drive to work (forgoing transit altogether), middle-income commuters drive to a rail station and pay to park, and low-income commuters rely on feeder buses or walking to reach a rail station. Transportation network companies (TNCs), like Uber and Lyft, are a viable option for connecting travelers to rail stations, especially for those who don’t own a car, however, their high fares make them attractive only to higher-income travelers. To close this equity gap, subsidies could be provided for TNC rides that connect travelers to commuter rail. To explore this concept further, we developed idealized (but physically realistic and rational) models to describe communities in the San Francisco Bay Area, and simulated the effects of various subsidization policies (i.e., providing subsidies for TNC rides to and from rail stations, increasing rail stations parking fees) using real-world data representative of Bay Area commuter populations.