Mining top-N high-utility operation patterns for taxi drivers. (15th May 2021)

• Record Type: Journal Article
• Authors: Liu, Caihong; Guo, Chonghui
• Is Part Of: Expert systems with applications. Volume 170(2021)
• Publication Date: 2021-05-15
• Subjects: Top-N -- High-utility -- Operation pattern -- Order sequence -- Dynamic update; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114546
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Propose a framework for top-N high-utility order sequences mining. Construct a function to calculate the utility of the order sequence. Construct a high-utility sequence tree to mine taxi driver's operation patterns. Propose two pruning strategies to reduce the size of candidate set. Validate the proposed algorithm on real world dataset. Abstract: In recent years, the rapid development of mobile network and wireless sensor technology has brought opportunities to change the way of the existing taxi business operation. How to improve the operation revenues of taxi drivers has become a topic worthy of research. This paper analyzes and mines taxi operation data to provide taxi drivers with personalized sequence recommendation services, thereby increasing their expected revenues. Different from previous works, the proposed method in this paper recommends a series of future operation orders for taxi drivers, instead of recommending several discrete locations for the current order. In this paper, firstly, by performing spatial-temporal clustering on the origins and destinations of passengers, the spatial and temporal distribution characteristics of passengers in the city are identified. Secondly, the origin of the current passenger is used as the root node to construct a top-N high-utility sequence tree, and this process can be divided into two processes: top-down building tree and bottom-up sorting path utility. The two pruning strategies of node utility and path utility are used to reduce the generation of candidate sets. Finally, a series of potential orders based on dynamic context are recommended to taxi drivers, so as to maximize the expected revenues of taxi drivers. The experimental results demonstrate that there is a close relationship between taxi drivers' operation behavior patterns and their revenues. The proposed system framework and algorithm in this paper can effectively mine global and long-term top-N high-utility operation patterns.