text: 14,131,1402898805373083654,145986026,Erdem Bıyık,"New paper at IEEE Transactions on Control of Network Systems: <LINK> In traffic, many equilibria exist (see the GIF, the flow is the same in all 5 configs). When all drivers select their routes selfishly, we often end up in congested equilibria. (1/n) <LINK> Autonomous cars may alleviate this problem, as they can keep shorter headways with the car they are following. Besides, travel times might be further reduced if some vehicles (or drivers) are altruistic. But, does altruism really exist? (2/n) Luckily, financial incentives (through ride-hailing services like Uber & Lyft) can give the same benefits of altruism! These services should give multiple options to the users: ""do you prefer a cheaper but slower route or are you willing to pay more to get there faster?"" (3/n) Using preference-based learning techniques helps learn passengers' price/latency tradeoff. The services should then use this information to optimize route prices to minimize congestion. In simulations, this approach decreased travel times up to 50%. (4/n) This is an extension of our earlier works presented at WAFR 2018 (<LINK>) and CDC 2019 (<LINK>) with Daniel A. Lazar, Ramtin Pedarsani and @DorsaSadigh. (5/5)",https://arxiv.org/abs/2106.04678,"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. ","Incentivizing Efficient Equilibria in Traffic Networks with Mixed