TripRec: An Efficient Approach for Trip Planning with Time Constraints

Heli Sun, Jianbin Huang, Xinwei She, Zhou Yang, Jiao Liu, Jianhua Zou, Qinbao Song and Dong Wang
Additional contact information
Heli Sun: School of Electronic and Information Engineering, Xi'an Jiaotong Univeristy, Xi'an, China & State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing China, China
Jianbin Huang: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China & School of Software, Xidian University, Xi'an, China
Xinwei She: School of Software, Xidian University, Xi'an, China
Zhou Yang: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
Jiao Liu: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
Jianhua Zou: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
Qinbao Song: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
Dong Wang: School of Information Science and Technology, Northwest University, Xi'an, China

International Journal of Data Warehousing and Mining (IJDWM), 2015, vol. 11, issue 1, 45-65

Abstract: The problem of trip planning with time constraints aims to find the optimal routes satisfying the maximum time requirement and possessing the highest attraction score. In this paper, a more efficient algorithm TripRec is proposed to solve this problem. Based on the principle of the Aprior algorithm for mining frequent item sets, our method constructs candidate attraction sets containing k attractions by using the join rule on valid sets consisting of k-1 attractions. After all the valid routes from the valid k-1 attraction sets have been obtained, all of the candidate routes for the candidate k-sets can be acquired through a route extension approach. This method exhibits manifest improvement of the efficiency in the valid routes generation process. Then, by determining whether there exists at least one valid route, the paper prunes some candidate attraction sets to gain all the valid sets. The process will continue until no more valid attraction sets can be obtained. In addition, several optimization strategies are employed to greatly enhance the performance of the algorithm. Experimental results on both real-world and synthetic data sets show that our algorithm has the better pruning rate and efficiency compared with the state-of-the-art method.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijdwm.2015010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:11:y:2015:i:1:p:45-65

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().