An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data
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一种利用海量GPS轨迹数据的自动路网构建方法

DOI:
10.3390/ijgi6120400
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发表时间:
2017-12
影响因子:
3.4
通讯作者:
张福浩
张福浩
中科院分区:
地球科学3区
文献类型:
--
作者:
张用川;刘纪平;钱新林;仇阿根;张福浩

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自动获取全面、准确、实时的地图信息并将其转换为数字地图是一个具有挑战性的问题。传统的方法既耗时又昂贵,因为它们需要昂贵的实地测量和劳动密集型的后处理。最近,定位技术在车辆和其他设备中的普遍使用产生了大量的轨迹数据,这为数字地图的制作和更新提供了新的机会。本文提出了一种从原始车辆全球定位系统(GPS)轨迹数据自动生成道路网络的方法。首先,原始GPS定位数据进行处理,以消除噪声,采用灵活的空间,时间和逻辑约束规则的新提出的算法。然后,一个新的道路网络构建算法被用来增量合并轨迹到一个有向图表示的数字地图。此外,计算平均道路交通量和速度并将其分配给相应的路段。为了评估该方法的性能,使用来自200辆出租车的576万个轨迹数据点进行了实验。将结果与OpenStreetMap进行定性比较,并与基于F分数的两种现有方法进行定量比较。研究结果表明,我们的方法可以自动生成一个道路网络表示的数字地图。
Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.
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