Adapting Land Use and Infrastructure for Automated Driving: Part A

Abstract

Automated vehicles (AVs) will likely yield a transformation of urban form, its land use and mobility system. This report is concerned with adapting land use and transportation infrastructure for automated driving. In the first part of the report, we propose an infrastructurebased approach to close the connectivity gap for connected and automated vehicles (CAVs) in a mixed traffic environment. It is envisioned that roadside units can be deployed to sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. We thus develop a quantitative modeling framework to analyze the impacts of this type of roadside units at the strategic planning level. In the second part, we analytically examine how the parking locations of AVs impact the morning and evening commuting pattern, and then investigate the optimal AV parking supply that minimizes the total system cost. We also offer some insights through numerical studies regarding relationship among traffic efficiency, tolling schemes and AV parking supply.