Location Selection of Shared Bicycle Distribution Points Based on User Demand

Shared bicycle systems must select the best delivery point locations to meet user demand. Because different value users have different demand, this paper used the RFM (Recency, Frequency and Monetary) model to divide users into different value user types, and used the analytic hierarchy process to determine the weight of each user type. We selected the shortest weighted total walking distance of users as the optimization objective, constructed a siting model of shared bicycle delivery points based on user demand, and solved this problem using genetic algorithm. Finally, we validated the model using the bike-sharing data of a region in Shanghai. The results showed that high-value users were given greater weight in the analytic hierarchy process, so the final placement point was closer to these users, which reduced their walking distance. When we set the number of delivery points to 600, the average walking distance of each user was 206.98 meters. Therefore, the method proposed by us can better meet the demand of users and can also effectively optimize the distribution of shared bikes.