The Location Optimization of Urban Shared New Energy Vehicles Based on P-Median Model: The Example of Xuzhou City, China

Abstract

Sharing new energy vehicles is crucial for addressing the issue of traditional vehicles’ carbon emissions, reducing urban traffic congestion, safeguarding the environment, and promoting citizens’ use of green transportation. However, the parking lot’s drawbacks—poor location, challenging parking, and difficulty finding a car—lead to a low popularity rate, few users, and infrequent use. How to scientifically choose parking outlets and maximize the advantages of sharing new energy vehicles has become an important topic in current urban traffic management. This paper constructed a “G-B-U” framework starting with quasi-public goods and stakeholders to analyze the factors influencing the location selection of these vehicles. On this basis, a three-stage location decision method of “market demand prediction—alternative network screening—location model solution” is proposed to optimize the location selection of shared new energy vehicles. The factors are analyzed, and numerical examples are studied, using the districts of Xuzhou City in China as examples: Gulou, Yunlong, and Quanshan. The findings indicate that the main variables influencing how frequently Xuzhou residents use shared new energy cars are network dispersion, rental and return convenience, and usage experience. After site selection optimization, the journey distance is nearly cut in half, saving users a significant amount of travel time. It may meet the travel needs of residents better based on the same number of parking lots.