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TripRec: An Efficient Approach for Trip Planning with Time Constraints

TripRec: An Efficient Approach for Trip Planning with Time Constraints

Heli Sun, Jianbin Huang, Xinwei She, Zhou Yang, Jiao Liu, Jianhua Zou, Qinbao Song, Dong Wang
Copyright: © 2015 |Volume: 11 |Issue: 1 |Pages: 21
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466675919|DOI: 10.4018/ijdwm.2015010103
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MLA

Sun, Heli, et al. "TripRec: An Efficient Approach for Trip Planning with Time Constraints." IJDWM vol.11, no.1 2015: pp.45-65. http://doi.org/10.4018/ijdwm.2015010103

APA

Sun, H., Huang, J., She, X., Yang, Z., Liu, J., Zou, J., Song, Q., & Wang, D. (2015). TripRec: An Efficient Approach for Trip Planning with Time Constraints. International Journal of Data Warehousing and Mining (IJDWM), 11(1), 45-65. http://doi.org/10.4018/ijdwm.2015010103

Chicago

Sun, Heli, et al. "TripRec: An Efficient Approach for Trip Planning with Time Constraints," International Journal of Data Warehousing and Mining (IJDWM) 11, no.1: 45-65. http://doi.org/10.4018/ijdwm.2015010103

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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.

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