Research on the Influencing Factors of Package Storage Time in the Parcel Lockers Based on User Classification

Abstract

Under the circumstance that parcel locker has gradually become an important way of terminal distribution, the resource turnover rate of intelligent parcel locker is low, which cannot meet more needs. In China, there is a huge B2 e-commerce business here, and there is a relatively large population density in first-tier cities, causing the last mile of Chinese e-commerce delivery to face tremendous pressure every day. However, through analysis of survey data, in a certain tier city in China, each parcel was stored in a parcel locker for more than 10 hours, which caused a very low turnover rate of the lockers. In order to explore the influencing factors that affect the turnover rate of parcel lockers, this article started with obtaining real-time data of parcel lockers, and then analyzed the characteristics of the data to find the effect function of the storage time for the cargo in the parcel lockers. During the empirical analysis, we found that users showed significantly different characteristics, that is, they were divided into two types of users. Then, this article mines the characteristics of the two types of users and names them as the First-class users, Second-class users. On this basis, this article finds that the length of parcel storage is related to user classification. In addition, this article conducted a series of rigorous empirical research and analysis in order to explore the impact of other factors on the storage time of the package,, and finally found that under the same type of user, there is a significant correlation between the length of storage time and the pickup time. Therefore, this paper established a dumb linear regression model to explore the law of parcel staying time in parcel locker, which the user type was used as a dummy variable, and the pickup time was used as another independent variable. The model passed the significance test.Therefore, this article further analyzes the influencing factors of the utilization efficiency of lockers. This provides some perspectives for us to find measures to improve the utilization rate of lockers in the future. For example, the idea of user classification can be applied to the research of pickup users and provide some personalized distribution services.