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

Frontiers of Information Technology & Electronic Engineering 2022年第10期23卷 1494-1510页

作者： Wenyong ZHANG Dawen XIA Guoyan CHANG Yang HU Yujia HUO Fujian FENG Yantao LI Huaqing LI College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China Department of Automotive Engineering Guizhou Traffic Technician and Transportation CollegeGuiyang 550008China College of Computer Science Chongqing UniversityChongqing 400044China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China The Affiliated Hospital of Guizhou Medical University Guiyang 550001China

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

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