Network-wide parking provision for autonomous vehicles: An integrated land use and transportation perspective

The self-driving capability enables fully autonomous vehicles (AVs) to drive empty and park remotely, offering new opportunities to enhance urban land use efficiency by reallocating public parking spaces for other uses. However, existing studies often focus either on mitigating the adverse impacts of empty trips on transportation systems or on evaluating land use implications of AV adoption, without providing an integrated network-wide parking planning framework that balances land use efficiency enhancement and potential transportation system performance degradation. To address this gap, this study presents an integrated perspective on the strategic parking provision problem in the era of AVs. To capture the influences of AVs’ joint (and inseparable) routing and parking behaviors on network traffic flows, we develop a journey-based modeling toolkit that analyzes the network-wide distribution of self-parking AVs. Built upon this toolkit, we propose a bi-level parking provision model that maximizes transportation network capacity subject to land supply and land use efficiency constraints. This bi-level model incorporates trip generation/distribution to account for the impact of land use patterns on AV travel demand, which is then allocated to the network through the journey-based modeling toolkit. A gradient-based algorithm is presented to solve the bi-level model. Numerical experiments show that the self-parking capability can improve network capacity under limited parking supply. Additionally, prioritizing the development of areas surrounding the central business district (CBD) may further enhance network capacity compared to development within the CBD, but the induced empty trips of AVs may create new traffic bottlenecks.