IMI Policy Labs · Policy sandbox
Mobility Mayor
You’re the mayor. Decide how many autonomous vehicles are allowed on the streets, what riders pay on every trip, and whether every ward gets equal service — then watch wait times, fares, equity, and city revenue move across all eight wards.
Includes the Allen Bill as introduced, so you can see what the proposal on the table actually does before you change anything.
Illustrative, not predictive. Numbers here express the direction and shape of a tradeoff, not a forecast. Open in a new window →
This sandbox has a lot of controls and works best on a larger screen.
What this is, and what it isn't
The sandbox expresses the directional logic of regulatory tradeoffs. It is not a forecast. Output numbers should not be cited as predictions for what would happen in D.C. under any specific rule set.
It is also not a peer-reviewed model. The closest published analogue is Gao and Li (2023), a Nash-equilibrium model of autonomous ride-hail regulation calibrated for San Francisco. We borrow structure and parameter ranges from that work; our implementation is materially simpler.
And it is not a case for or against any policy. Where our calibration leans in a direction, we say so.
Where the model is weakest
The most damaging critique of a policy simulator is one its authors should have anticipated and didn't. These are the places a skeptical reviewer should start.
- Operator pricing is fixed. We treat baseline fare as exogenous. In reality an operator facing a binding vehicle cap would re-optimize its prices. This understates the welfare cost of supply-restricting regulation.
- Vehicle utilization is the single most consequential assumption. We use a conservative early-deployment figure. Mature operations run substantially higher, and raising it materially changes the results under identical rules.
- Transit is absent. The model has no transit channel, which matters for the equity findings.
- All trips are three miles. Real trip distance is right-skewed and varies by ward, which affects how per-trip and per-mile fees compare.
- D.C.-specific ride-hail data isn't published at the granularity this needs. Most calibration is inference from cities that do publish trip-level data, adjusted for D.C.
References
- Gao, J. and Li, S. (2023). "Regulating For-Hire Autonomous Vehicles for an Equitable Multimodal Transportation Network." arXiv:2301.05798.
- Li, S., Tavafoghi, H., Poolla, K., and Varaiya, P. (2019). "Regulating TNCs: Should Uber and Lyft Set Their Own Rules?" arXiv:1902.01076.
- Cohen, P., Hahn, R., Hall, J., Levitt, S., and Metcalfe, R. (2016). "Using Big Data to Estimate Consumer Surplus: The Case of Uber." NBER Working Paper 22627.
- Mark, B. and Tarduno, M. (2021). "Not all fees are created equal: Equity implications of ride-hail fee structures and revenues." Transport Policy.
- District of Columbia Department of For-Hire Vehicles (2024). "Review of Rate Structure and Decision Pursuant to D.C. Official Code § 50-301.17."
We continue to calibrate this sandbox and welcome feedback on the method. If you know of a D.C. data source we’ve missed, tell us.