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

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, 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.

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.