Autonomous vehicles (AVs) have the potential of reshaping the human mobility in a wide variety of aspects. This paper focuses on a new possibility that the AV owners have the option of “renting” their AVs to a company, which can use these collected AVs to provide on-demand ride services without any drivers. We call such a mobility market with AV renting options the “AV crowdsourcing market”. This paper establishes an aggregate equilibrium model with multiple transport modes to analyze the AV crowdsourcing market. The modeling framework can capture the customers’ mode choices and AV owners’ rental decisions with the presence of traffic congestion. Then, we explore different scenarios that either maximize the crowdsourcing platform’s profit or maximize social welfare. Gradient-based optimization algorithms are designed for solving the problems. The results obtained by numerical examples reveal the welfare enhancement and the strong profitability of the AV crowdsourcing service. However, when the crowdsourcing scale is small, the crowdsourcing platform might not be profitable. A second-best pricing scheme is able to avoid such undesirable cases. The insights generated from the analyses provide guidance for regulators, service providers and citizens to make future decisions regarding the utilization of the AV crowdsourcing markets for serving the good of the society.

自动驾驶汽车 (AV) 具有在各个方面重塑人类出行方式的潜力。本文重点讨论了一种新的可能性，即自动驾驶车主可以选择将他们的自动驾驶汽车“出租”给一家公司，该公司可以使用这些收集到的自动驾驶汽车来提供无需任何司机的按需乘车服务。我们将这种具有 AV 租赁选项的移动市场称为“AV 众包市场”。本文建立了具有多种运输方式的聚合均衡模型来分析AV众包市场。建模框架可以在存在交通拥堵的情况下捕获客户的模式选择和 AV 所有者的租赁决策。然后，我们探索了使众包平台利润最大化或社会福利最大化的不同场景。基于梯度的优化算法是为解决这些问题而设计的。通过数值例子得到的结果揭示了AV众包服务的福利增强和强大的盈利能力。但是，当众包规模较小时，众包平台可能无法盈利。次优定价方案能够避免此类不良情况。从分析中产生的见解为监管机构、服务提供商和公民提供指导，以便在未来利用 AV 众包市场为社会服务做出决策时。