Bridging the Income and Digital Divide with Shared Automated Electric Vehicles

This research investigates strategies to improve the mobility of low-income travelers by incentivizing the use of electric SAVs (SAEVs) and public transit. We employ two agent-based simulation engines, an activity-based travel demand model of the San Francisco Bay Area, and vehicle movement data from the San Francisco Bay Area and the Los Angeles Basin to model emergent travel behavior of commute trips in response to subsidies for TNCs and public transit. Sensitivity analysis was conducted to assess the impacts of different subsidy scenarios on mode choices, TNC pooling and match rates, vehicle occupancies, vehicle miles traveled (VMT), and TNC revenues. The scenarios varied in the determination of which travel modes and income levels were eligible to receive a subsidy of $1.25, $2.50, or $5.00 per ride. Four different mode-specific subsidies were investigated, including subsidies for 1) all TNC rides, 2) pooled TNC rides only, 3) all public transit rides, and 4) TNC rides to/from public transit only. Each of the four modespecific subsidies were applied in scenarios which subsidized travelers of all income levels, as well as scenarios that only subsidized low-income travelers (earning less than $50,000 annual household income). Simulations estimating wait times for TNC trips in both the San Francisco Bay Area and Los Angeles regions also revealed that wait times are distributed approximately equally across low- and high-income trip requests.