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CLC number: TP39

On-line Access: 2022-10-24

Received: 2021-11-14

Revision Accepted: 2022-10-24

Crosschecked: 2022-06-24

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Citations:  Bibtex RefMan EndNote GB/T7714


Huaqing Li


Wenyong ZHANG


Dawen XIA


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.10 P.1494-1510


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):   gzmdzwy@gzmu.edu.cn, 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, 2022, 23(10): 1494-1510.

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author="Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T APFD: an effective approach to taxi route recommendation with mobile trajectory big data
%A Wenyong ZHANG
%A Dawen XIA
%A Guoyan CHANG
%A Yang HU
%A Yujia HUO
%A Fujian FENG
%A Yantao LI
%A Huaqing LI
%J Frontiers of Information Technology & Electronic Engineering
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%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100530

T1 - APFD: an effective approach to taxi route recommendation with mobile trajectory big data
A1 - Wenyong ZHANG
A1 - Dawen XIA
A1 - Guoyan CHANG
A1 - Yang HU
A1 - Yujia HUO
A1 - Fujian FENG
A1 - Yantao LI
A1 - Huaqing LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 10
SP - 1494
EP - 1510
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100530

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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