Every time you open a ride app, an algorithm sets a price, matches you to a driver, and decides where idle cars should wait. Those decisions, repeated millions of times a day, quietly shape a city’s mobility — and a lot of people’s incomes. My group has spent years asking what it means for that machinery to be not just efficient but fair, and whether fairness can be written down precisely enough to optimize.
Fair pricing, made precise
Demand in a city is lopsided: some neighborhoods and some hours generate far more trips, and travel times are asymmetric. Naïve dynamic pricing can entrench those asymmetries. In Fair Pricing of Ridehailing Services with Asymmetric Demand and Travel Time (2021) we formalize fairness constraints and derive prices that respect them, showing you don’t have to throw away efficiency to get there. The point is that “fair” stops being a slogan and becomes a constraint you can design around.
Getting operators to cooperate
Seamless mobility usually requires several operators — a rideshare company, a transit agency, a bike network — to cooperate, and each is out for itself. In Incentive design and profit sharing in multi-modal transportation networks (2022) we design the incentives and profit-sharing rules that make cooperation each operator’s best move, so a rider can go door-to-door across modes that would otherwise never coordinate.
Keeping the fleet where it’s needed
Cars drift to where the last trips ended, not where the next ones will start. In Multi-objective vehicle rebalancing for ridehailing systems using a reinforcement learning approach (2022) we learn where to reposition idle vehicles, balancing competing objectives — rider wait time, driver earnings, efficiency. And in Fleet sizing and charger allocation in electric vehicle sharing systems (2022) we plan the harder, slower decisions: how many electric vehicles, and where to put the chargers.
Why it matters
Mobility is being rewritten by software, and the rules encoded in that software have real distributional consequences — for riders in underserved areas and for drivers whose livelihoods depend on the matching and pricing logic. Making fairness a formal objective, rather than an afterthought, is how you keep those consequences from being accidental.
What it means for the cities we’re building
Three shifts make this work more relevant, not less:
- Electrification ties mobility to the grid — fleets become flexible demand, and charging logistics become part of the pricing problem.
- Multimodal “mobility-as-a-service” only works if independent operators are incentivized to interoperate; that is a mechanism-design problem, not an app feature.
- The gig economy has made algorithmic fairness a matter of livelihoods, which raises the stakes on getting the objective right.
As transportation, energy, and labor markets fuse into one algorithmic system, the question “what is a fair price for a ride?” turns out to be a question about what kind of city we want the algorithms to build.
Papers behind this post: Fair Pricing of Ridehailing Services with Asymmetric Demand and Travel Time (2021) · Incentive design and profit sharing in multi-modal transportation networks (2022) · Multi-objective vehicle rebalancing for ridehailing system using a reinforcement learning approach (2022) · Fleet sizing and charger allocation in electric vehicle sharing systems (2022). See them on the Publications page and the transportation-markets project.