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An Entropy-Based Model for Recommendation of Taxis’ Cruising Route

Yizhi Liu1, 2, Xuesong Wang1, 2, Jianxun Liu1, 2, *, Zhuhua Liao1, 2, Yijiang Zhao1, 2, Jianjun Wang1, 2
1 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.
2 Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province, Xiangtan, 411201, China.
* Corresponding Author: Jianxun Liu. Email: .

Journal on Artificial Intelligence 2020, 2(3), 137-148. https://doi.org/10.32604/jai.2020.010620

Received 14 March 2020; Accepted 06 June 2020; Issue published 15 July 2020

Abstract

Cruising route recommendation based on trajectory mining can improve taxidrivers' income and reduce energy consumption. However, existing methods mostly recommend pick-up points for taxis only. Moreover, their performance is not good enough since there lacks a good evaluation model for the pick-up points. Therefore, we propose an entropy-based model for recommendation of taxis' cruising route. Firstly, we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal features. Secondly, the information entropy of spatial-temporal features is integrated in the evaluation model. Then it is applied for getting the next pick-up points and further recommending a series of successive points. These points are constructed a cruising route for taxi-drivers. Experimental results show that our method is able to obviously improve the recommendation accuracy of pick-up points, and help taxi-drivers make profitable benefits more than before.

Keywords

Trajectory data mining, location-based services (LBS), optimal route recommendation, pick-up point recommendation, information entropy.

Cite This Article

Y. Liu, X. Wang, J. Liu, Z. Liao, Y. Zhao et al., "An entropy-based model for recommendation of taxis’ cruising route," Journal on Artificial Intelligence, vol. 2, no.3, pp. 137–148, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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