Abstract
Under the "2024 Problem," stricter overtime regulations for drivers have exacerbated labor shortages in the logistics industry. While recruiting novice drivers is a critical countermeasure, their lack of knowledge about optimal parking locations poses efficiency challenges. In contrast, experienced drivers possess valuable knowledge in selecting optimal parking locations, which can be leveraged to support novices and improve logistics efficiency. This study proposes a parking recommendation algorithm based on veteran driver knowledge. The algorithm utilizes a database combining delivery destination addresses and parking locations and comprises three main components: (1) selecting reliable parking locations from past parking data with a focus on proximity; (2) identifying nearby parking locations for unregistered buildings; and (3) If multiple delivery destinations can be consolidated into a single parking location, clustering-based aggregation processing is applied. The proposed method was validated through subjective evaluations by drivers and proof-of-concept experiments, demonstrating its high practicality and accuracy in effectively recommending parking locations to novice drivers.
