APFD:an effective approach to taxi route recommendation with mobile trajectory big data  

作　　者：Wenyong ZHANG Dawen XIA Guoyan CHANG Yang HU Yujia HUO Fujian FENG Yantao LI Huaqing LI 

机构地区：[1]College of Data Science and Information Engineering,Guizhou Minzu University,Guiyang 550025,China [2]Department of Automotive Engineering,Guizhou Traffic Technician and Transportation College,Guiyang 550008,China [3]College of Computer Science,Chongqing University,Chongqing 400044,China [4]College of Electronic and Information Engineering,Southwest University,Chongqing 400715,China [5]The Affiliated Hospital of Guizhou Medical University,Guiyang 550001,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2022年第10期1494-1510,共17页信息与电子工程前沿（英文版）

基　　金：the National Natural Science Foundation of China(Nos.62162012,62173278,and 62072061);the Science and Technology Support Program of Guizhou Province,China(No.QKHZC2021YB531);the Youth Science and Technology Talents Development Project of Colleges and Universities in Guizhou Province,China(No.QJHKY2022175);the Science and Technology Foundation of Guizhou Province,China(Nos.QKHJCZK2022YB195 and QKHJCZK2022YB197);the Natural Science Research Project of the Department of Education of Guizhou Province,China(No.QJJ2022015);the Scientific Research Platform Project of Guizhou Minzu University,China(No.GZMUSYS[2021]04)。

摘　　要：With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.

关 键 词：Big data analytics Region extraction Artificial potential field DIJKSTRA Route recommendation GPS trajectories of taxis 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]