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

Wenyong ZHANG; Dawen XIA; Guoyan CHANG; Yang HU; Yujia HUO; Fujian FENG; Yantao LI; Huaqing LI

doi:10.1631/FITEE.2100530

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

Regular Papers | Updated：2022-10-28

- APFD: an effective approach to taxi route recommendation with mobile trajectory big data
- APFD：面向移动轨迹大数据的出租车路径推荐方法
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 10, Pages: 1494-1510(2022)
- Affiliations：
  
  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
- Author bio：
  
  †E-mail: gzmdzwy@gzmu.edu.cn
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2100530
  CLC： TP39
- Received：14 November 2021，
  
  Revised：2022-08-01，
  
  Accepted：24 June 2022，
  
  Published Online：24 September 2022，
  
  Published：0 October 2022
- Accepted：
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Wenyong ZHANG, Dawen XIA, Guoyan CHANG, et al. 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.
DOI：

Wenyong ZHANG, Dawen XIA, Guoyan CHANG, et al. 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. DOI： 10.1631/FITEE.2100530.

摘要

随着数据驱动智能交通系统的迅猛发展，高效的出租车路径推荐方法成为智慧城市的研究热点。基于移动轨迹大数据，提出一种基于人工势场（APF）和Dijkstra方法的出租车路径推荐方法。为提高路径推荐效率，提出一种区域提取方法，该方法通过原点和终点坐标搜索包含最优路径的区域。基于APF方法，提出一种有效的冗余节点去除方法。最后，通过Dijkstra方法推荐最优路径。将APFD方法应用于仿真地图和北京四环的实际路网。在地图上随机选取20对起点和终点坐标，采用APFD方法、蚁群（AC）算法、贪婪算法（A*）、APF、迅速探索随机树（RRT）、非支配排序遗传算法-II（NSGA-II）、粒子群算法（PSO）和Dijkstra算法进行最短路径推荐。在最短路径规划方面，与AC、A*、APF、RRT、NSGA-II和PSO相比，APFD的路径规划能力分别提高了1.45%–39.56%、4.64%–54.75%、8.59%–37.25%、5.06%–45.34%、0.94%–20.40%和2.43%–38.31%。与Dijkstra算法相比，APFD的执行效率提高了1.03–27.75倍。此外，在北京四环实际路网中，APFD推荐最短路径的能力优于AC、A*、APF、RRT、NSGA-II和PSO，且APFD的执行效率高于Dijkstra方法。

Abstract

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

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

APF

RRT

NSGA-II

and PSO

and the execution efficiency of APFD is higher than that of the Dijkstra method.

关键词

Keywords

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