Autonomous vehicle parking policies: A case study of the City of Toronto

Autonomous Vehicles (AVs) can eliminate the burden of finding a parking spot upon arrival to the destination. AVs can park at a strategic location or cruise until summoned by their users. In this study, we investigate AV users’ parking decision considering their cost and time constraints. Each users’ decision has impacts on congestion which can change feasible options of other users. Hence, we use an agent-based simulation model to study AV parking policies. Results show that travelers consider sending their vehicles to park at home if they have to pay to use a parking facility. Also, our analysis for downtown Toronto shows that AVs would travel on average 12 min and a maximum of 47 min to park in cheaper parking lots. We also find that assigning the same parking price across all the parking facilities would exacerbate the congestion by motiving more AVs to cruise instead of choosing the closest parking lot. However, we show that a toll for zero-occupant AVs leads to a tradeoff between parking cost and distance that would decrease the VKT by 3.5% in downtown Toronto