Incentivizing Efficient Equilibria in Traffic Networks With Mixed Autonomy
Incentivizing Efficient Equilibria in Traffic Networks With Mixed Autonomy
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DOI：
10.1109/tcns.2021.3084045
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发表时间：
2021-12-01
影响因子：
4.2
通讯作者：
Sadigh, Dorsa
中科院分区：
文献类型：
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作者：
Biyik, Erdem;Lazar, Daniel A.;Sadigh, Dorsa
交通拥堵具有巨大的经济和社会成本。自动驾驶汽车的引入可以通过车辆队列增加道路容量，并创造一个影响人们选择路线的途径，从而潜在地减少这种拥堵。我们考虑一个有两种交通方式的平行道路网络：1)人类司机，他们会选择对他们来说最快的路线；2)乘车服务，它为用户提供一系列自动驾驶汽车路线选择，每条路线都有不同的价格。我们形式化了混合自治中的车辆流模型和自治服务用户如何在不同价格和延迟的路线之间做出选择的模型。开发一种算法来学习用户的偏好，我们制定了一个计划优化，选择价格以最大化社会目标。我们通过将结果与理论基准进行比较，证明了所提出方案的好处，我们表明，可以有效地计算。
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: 1) human drivers, who will choose the quickest route available to them, and 2) 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 that, we show, can be efficiently calculated.