A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

Xiaoyao Zheng,Qingying Yu,Ji Zhang,Liping Sun,Yonglong Luo,Siguang Chen

doi:10.3390/app112110497

Abstract

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

Highlights

IntroductionWith the rapid development of Internet technology in recent years, the explosive growth of information has increased the burden of retrieving personally interested information and content
The results show that the mechanism of crowd sensing can effectively improve the recommendation performance of tour routes
We proposed a multi-objective and multi-constrained travel route recommendation algorithm

Summary

Introduction

With the rapid development of Internet technology in recent years, the explosive growth of information has increased the burden of retrieving personally interested information and content. The birth of recommended technologies can help people acquire the resources in which they are interested more efficiently. Given the potentially huge returns, major companies such as Google, Amazon, and Taobao, to just name a few, have produced sophisticated and advanced recommender systems and technologies for effectively marketing their products to users. Due to many factors affecting tourism routes, such as the complex information of real-time traffic flow, weather, and user preferences [1,2,3,4], the recommendation of tourist routes is far less mature [5,6]

Methods

Results

Conclusion