@Article{jai.2020.010620,
AUTHOR = {Yizhi Liu, Xuesong Wang, Jianxun Liu, Zhuhua Liao, Yijiang Zhao, Jianjun Wang},
TITLE = {An Entropy-Based Model for Recommendation of Taxis’ Cruising  Route},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {137--148},
URL = {http://www.techscience.com/jai/v2n3/39521},
ISSN = {2579-003X},
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.},
DOI = {10.32604/jai.2020.010620}
}