Affiliation:
1. School of Computer and Information, Anhui Normal University, Wuhu, China
2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, China
Abstract
The analysis of user trajectory information and social relationships in social media, combined with the personalization of travel needs, allows users to better plan their travel routes. However, existing methods take only local factors into account, which results in a lack of pertinence and accuracy for the recommended route. In this study, we propose a method by which user clustering, improved genetic, and rectangular region path planning algorithms are combined to design personalized travel routes for users. First, the social relationships of users are analyzed, and close friends are clustered into categories to obtain several friend clusters. Next, the historical trajectory data of users in the cluster are analyzed to obtain joint points in the trajectory map, these are matched according to the keywords entered by users. Finally, the search area is narrowed and the recommended travel route is obtained through improved genetic and rectangular region path planning algorithms. Theoretical analyses and experimental evaluations show that the proposed method is more accurate at path prediction and regional coverage than other methods. In particular, the average area coverage rate of the proposed method is better than that of the existing algorithm, with a maximum increasement ratio of 31.80%.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference26 articles.
1. Trajectory Data Mining: An Overview;Zheng;ACM Trans Intell Syst Technol,2015
2. Optimizing last trains timetable in the urban rail network: social welfare and synchronization;Yin;Transportmetrica B,2019
3. Green Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center Based on Improved Intelligent Water Drop Algorithms;Chen;Mathematical Problems in Engineering,2020
4. Optimal travel route recommendation mechanism based on neural networks and particle swarm optimization for efficient tourism using tourist vehicular data;Malik;Sustainability,2019
5. Knowledge extraction from crowdsourced data for the enrichment of road networks;Jossé;GeoInformatica,2017
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献