This paper investigates the equity impacts of autonomous vehicles (AV) on for-hire human drivers and passengers in a ride-hailing market, and examines regulation policies that protect human drivers and improve transport equity for ride-hailing passengers. We consider a transportation network companies (TNC) that employs a mixture of AVs and human drivers to provide ride-hailing services. The TNC platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and TNC over the transportation network. The overall problem is formulated as a non-concave program, and an algorithm is developed to derive its approximate solution with a theoretical performance guarantee. Our study shows that TNC prioritizes AV deployment in higher-demand areas to make a higher profit. As AVs flood into these higher-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. To mitigate these concerns, we consider: (a) a minimum wage for human drivers; and (b) a restrictive pickup policy that prohibits AVs from picking up passengers in higher-demand areas. In the former case, we show that a minimum wage for human drivers will protect them from the negative impact of AVs with negligible impacts on passengers. However, there exists a threshold beyond which the minimum wage will trigger the platform to replace the majority of human drivers with AVs. In the latter case, we show that prohibiting AVs from picking up passengers in higher-demand areas not only improves the spatial equity of ride-hailing services for passengers, but also substantially increases human driver surplus and restricts the increase of total fleet size compared to the unregulated case. These results are validated with realistic case studies for San Francisco.

本文调查了自动驾驶汽车 (AV) 对网约车市场中的人类司机和乘客的公平影响，并研究了保护人类司机和改善网约车乘客运输公平性的监管政策。我们考虑一家交通网络公司 (TNC)，它混合使用自动驾驶汽车和人类司机来提供叫车服务。TNC 平台决定空间价格、车队规模、司机付款和车辆重新定位策略，以最大限度地提高利润，而个人乘客则可以选择不同的交通方式，以最大限度地降低出行成本。提出了一个市场均衡模型来捕捉乘客、人类司机、自动驾驶汽车和跨国公司在交通网络上的相互作用。整个问题被表述为一个非凹程序，并开发了一种算法来推导其具有理论性能保证的近似解。我们的研究表明，TNC 优先在需求较高的地区部署自动驾驶汽车，以获得更高的利润。随着自动驾驶汽车涌入这些需求较高的地区，它们在城市核心与人类司机竞争，并迫使他们搬到郊区。这导致人类司机的赚钱机会减少，乘客的空间不平等加剧。为了减轻这些担忧，我们考虑： (a) 人类司机的最低工资；(b) 限制性接送政策，禁止自动驾驶汽车在需求较高的地区接载乘客。在前一种情况下，我们表明人类司机的最低工资将保护他们免受自动驾驶汽车的负面影响，而对乘客的影响可以忽略不计。然而，存在一个门槛，超过最低工资将触发平台用自动驾驶汽车取代大多数人类司机。在后一种情况下，我们发现，与自动驾驶汽车相比，禁止自动驾驶汽车在需求较高的地区接载乘客不仅可以改善乘客乘车服务的空间公平性，而且还可以大幅增加人力驾驶员剩余并限制车队总规模的增长。不受监管的情况。这些结果通过旧金山的实际案例研究得到了验证。