Parking Lot Pricing Optimization Strategy Considering Autonomous Vehicle User Choice Behavior

The unbalanced distribution of parking demand is a primary source of parking problems. The autonomous driving system and parking assist system of fully autonomous vehicles provide a possibility to alleviate the uneven distribution of parking demand in the city. Exploring the parking behavior of autonomous vehicle (AV) users is necessary for assessing parking pricing policies. In turn, the distribution of parking demand and people's preference for parking lots may be impacted by the change in the AV parking lot's parking charge. Thus, this paper aims to provide an effective optimization method for AV parking lot price strategy, based on the analysis of the parking behavior of AV users. A stated choice experiment is designed to understand AV users' parking behavior. To estimate the impact of attributes and social-demographic factors, the multinomial logit model is adopted. From the results, we find a farther but cheaper parking lot is more attractive than a closer but more expensive parking lot to AV users. Based on the estimated results, a bilayer optimization model based on parking behavior is built to optimize AV parking lot prices. The Lagrangian relaxation method is employed to solve the model. By empirical testing, the AV parking price optimization model based on parking behavior is proven to be feasible and effective. According to the case study's findings, the parking lot pricing optimization model and algorithm proposed in this paper can not only meet the benefits of parking lots and the utility of autonomous vehicle users, but also alleviate the demand for parking concentrated in the city center, which means the parking difficulties caused by the distribution of parking demand can be alleviated through the optimization of AV parking lots' parking price.