In many developed cities around the world, vehicle sharing is becoming an increasingly popular form of green transportation. While such services are associated with lower emissions and easier mobility, their management poses a significant challenge. In this paper, we examine a dataset collected in Barcelona during the months of august and september 2020 in order to investigate relocation strategies and user clustering. By proposing a neighborhood area split and relating it to user demand, we propose two different areas based on majority demand and users’ requests and provide interpretations of both. We then aim to identify groups of similar users using a variant of Recency Frequency Monetary/Duration (RFM or RFD) clustering that extends to GPS coordinates of voyages in order to differentiate scores based on economic and geographical factors; furthermore, a user-based clustering approach was used to maximize client preferences. As a result of our analysis, the sharing company may be able to make more informed decisions regarding where to focus its resources. In fact, we find that the majority of the demand is concentrated in an area that represents 7.47 percent of the city’s area. Additionally, we propose a discount-based approach in order to influence the user’s behavior in parking the vehicle where it is most needed.
2022, INFORMATION, Pages 511- (volume: 13)
Addressing Vehicle Sharing through Behavioral Analysis: A Solution to User Clustering Using Recency-Frequency-Monetary and Vehicle Relocation Based on Neighborhood Splits (01a Articolo in rivista)
Brandizzi N., Russo S., Galati G., Napoli C.
Gruppo di ricerca: Artificial Intelligence and Robotics