Parking Infrastructure Location Design and User Pricing in the Prospective Era of Autonomous Vehicle Operations

Abstract

The lack of parking infrastructure continues to pose a problem for urban commuters, as the parking demand in most cities outstrips supply and significant driver time is expended searching for parking. The emergence of vehicle automation offers an opportunity to help mitigate this issue. In the autonomous vehicle (AV) era, it is expected that after dropping off its passengers at their destinations, the AV will park at a relatively inexpensive parking facility located outside the downtown area instead of the existing, higher-priced facilities in the central business district (CBD). This is expected to decrease CBD parking demand, ultimately leading to the possible decommissioning and repurposing of some existing parking infrastructure in the CBD and the construction of new infrastructure in the city’s outlying areas. What is needed, therefore, is a framework for city road agencies for decommissioning/relocating/locating and user pricing of parking infrastructure to serve human-driven vehicles (HDVs) and AVs. To address this issue, this study presents a bilevel framework. The road agency (at the upper level) seeks to: (1) minimize travelers’ cost systemwide; and (2) maximize monetary benefits of infrastructure decommissioning and parking fee revenue at the upper level. Travelers (at the lower level) seek to reduce their costs of travel in response to the road agency’s decisions made at the upper level. A hybridized solution approach (optimization heuristics and machine learning) is implemented for this mixed-integer nonlinear problem. The numerical experiments provided a number of insights regarding parking infrastructure location design and user pricing in the prospective AV era.