关键词: 交通工程, 高速公路, RFMS, 客户细分, 商业价值, 差异化收费, Adaboost

Abstract: With the occurrence of highway congestion and uneven spatial and temporal distribution of traffic flow in China, the supply side reform of expressway, which took expressway differential charging as the main way, was an important measure for the diversified expressway users to alleviate the current highway congestion. Based on the customer segmentation theory, the RFMS model was put forward, in which the expressway users were divided into heterogeneous customer groups and their commercial value was analyzed. Considering the structural characteristics of expressway toll data, the small clustering phenomenon of traditional k-means algorithm in the initial cluster center selection and big data clustering was improved, a hybrid algorithm combining Adaboost and k-means + + was established, and a comparative analysis of the clustering effect was carried out. The research results show that the Adaboost-k-means + + hybrid algorithm can improve the clustering effect; the business value mining method of expressway customers based on RFMS can effectively divide heterogeneous customer groups and evaluate customer value, which can provide reliable theoretical support for expressway differential charging and expressway operation management departments.

Key words: traffic engineering, highway, RFMS, customer segmentation, commercial value, differentiated charges, Adaboost