Pricing shared vehicles

Abstract

This paper analyzes profit-maximizing pricing in a model of shared vehicle (SV) market, with particular emphasis on spatial inequality of demand. I show that the best policy assigns a score to every location, and rewards (penalizes) customers for relocating the vehicle to a place with higher (lower) score. Such spatially explicit pricing enables providers to expand the vehicle dropoff “home” area into otherwise unprofitable low-density suburban areas and into for-fee parking zones. A greater geographic coverage has positive spillovers on operations within the initial home area. The empirical part of the paper uses novel microdata on SV trips to develop a strategy to estimate demand parameters, extrapolate them into larger counterfactual home area, evaluate optimal location scores, and predict profit gains from the expansion.