# Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

arXiv: https://arxiv.org/abs/2106.04678 · HTML: https://mlsanity.com/paper/2106.04678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 7 of 7)

- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [Asking Easy Questions: A User-Friendly Approach to Active Reward Learning](https://mlsanity.com/paper/1910.04365/) (2019)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [Learning How to Dynamically Route Autonomous Vehicles on Shared Roads](https://mlsanity.com/paper/1909.03664/) (2019)
- [Altruistic Autonomy: Beating Congestion on Shared Roads](https://mlsanity.com/paper/1810.11978/) (2018)
- [Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices](https://mlsanity.com/paper/1509.05113/) (2015)
- [Effect of Adaptive and Cooperative Adaptive Cruise Control on Throughput of Signalized Arterials](https://mlsanity.com/paper/1703.01657/) (2017)

## Built on by (most cited first, 3 of 3)

- [Learning Preferences for Interactive Autonomy](https://mlsanity.com/paper/2210.10899/) (2022)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)

## Abstract (arXiv)

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.