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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100530


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


Author(s):  Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI

Affiliation(s):  College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China; more

Corresponding email(s):   dwxia@gzmu.edu.cn, huaqingli@swu.edu.cn

Key Words:  Big data analytics, Region extraction, Artificial potential field, Dijkstra, Route recommendation, GPS trajectories of taxis


Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU,Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI. APFD: an effective approach to taxi route recommendation with mobile trajectory big data[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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publisher="Zhejiang University Press & Springer",
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Abstract: 
With the rapid development of data-driven intelligent transportation system (ITS), an efficient route recommendation method for taxis has become a hot topic in smart cities. This paper presents an effective taxi route recommendation approach (called APFD) based on the artificial potential field and dijkstra methods with mobile trajectory big data. Specifically, to improve the efficiency of route recommendation, we propose a region extraction method that searches a region including the optimal route through the origin and destination coordinates. Then, based on the artificial potential field 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 of the Fourth Ring Road in Beijing. On the map, we randomly select 20 pairs of origin and destination coordinates and use APFD with an ant colony (AC) algorithm, Greedy algorithm (A*), artificial potential field (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 capabilities by 1.45%–39.56%, 4.64%–54.75%, 8.59%–33.87%, 6.75%– 45.34%, 0.94%–20.4%, and 2.43%–38.31%, respectively. Compared with dijkstra, the performance of APFD is improved by 1.03-30.56 times in terms of execution efficiency. In addition, in the real-world road network, the Fourth Ring Road in Beijing, the ability of APFD to recommend the shortest route is better than that of AC, A*, APF, RRT, NSGA-II, and PSO, and the execution efficiency is higher.

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