Exploring parking choices under the coexistence of autonomous and conventional vehicles

Xinying Zhang, Kelly Pitera and Yuanqing Wang

Physica A: Statistical Mechanics and its Applications, 2024, vol. 636, issue C

Abstract: The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

Keywords: Autonomous vehicles (AVs); Parking choice; Agent-based simulation; Parking policy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:636:y:2024:i:c:s0378437124000505

DOI: 10.1016/j.physa.2024.129542

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