Highly automated vehicles (AV) are in the early stages of deployment and are likely to have significant impacts on the United States transportation system. In particular, a broad deployment of shared, on-demand AVs might significantly impact vehicle ownership and transportation energy consumption; projecting these impacts is essential for climate, infrastructure, and policy planning. However, it seems increasingly likely that AVs will be deployed gradually over a period of decades, in which case there may be geographic or functional variation in their availability. This might occur for a combination of technological, policy, and economic reasons.

This manuscript seeks to advance a new framework for projecting AV impacts, with a particular focus on energy consumption impacts. Specifically, we introduce a framework for AV impacts that allows for AVs catering to specific operating environments or ride types. As a demonstration of this framework, we use the 2009 National Household Transportation Survey (NHTS) to segment US household travel demand based on built environment and ride length. Our framework allows us to specify AV “availability” for each population segment and ride type and use that information to predict the impact of AVs. We analyze a case scenario where shared, on-demand AVs are mostly suited for short trips in highly urbanized environments. We project the impact on household relocation, private vehicle ownership, induced travel demand, and fuel consumption. Utilization of this framework would help identify policy levers for sustainable deployment of AVs.

高度自动化的车辆（AV）处于部署的早期阶段，可能会对美国运输系统产生重大影响。特别是，共享的按需AV的广泛部署可能会严重影响车辆拥有量和运输能耗。预测这些影响对于气候，基础设施和政策规划至关重要。但是，AV越来越有可能在几十年内逐步部署，在这种情况下，其可用性可能会因地理位置或功能而有所不同。这可能是由于技术，政策和经济方面的原因而发生的。

本手稿旨在推进一个新的框架来预测视听影响，特别是对能耗的影响。具体来说，我们引入了一个针对影音影响的框架，该框架允许影音适应特定的操作环境或行驶类型。为了说明这一框架，我们使用了2009年全国家庭运输调查（NHTS），根据建筑环境和乘车时长对美国家庭出行需求进行了细分。我们的框架允许我们为每个人群和乘车类型指定AV的“可用性”，并使用该信息来预测AV的影响。我们分析了一个案例场景，其中共享的按需AV最适合高度城市化环境中的短途旅行。我们预计将对家庭搬迁，私人车辆拥有，感应出行需求和燃料消耗产生影响。