Abstract
Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers.
Highlights
Datasets comprising Global Navigation Satellite System (GNSS)-based vehicle trajectories are instrumental to traffic management, traffic congestion detection, and transportation planning [1,2,3]
The trajectory can be considered as a set of tracking links that are defined as vectors connecting two consecutive tracking points
DisAcmusosniognthe tracking links within the Mudanyuan area, shortcut link outliers account 4fo.1r. ~D5o.e8s39th%e I(nTfaobrmlea2ti)o.nWEenntrootpeytEhafftectthiveenlyuDmebsecriboef tlhinekSeopuatrlaietirosn(IoIfIt)hies LhoigcahteiornthofaTnrtahcaktinogf PthoeinotsthienraoRuotlaiderNs e(tIw, IoI)rkb?ecause “on-road” links represent the majority in comparison with the “Tohne-crtorascsrkoinadgsp”oliinnktss.were grouped into two categories (“on-crossroads” and “onroad”) based on the spatial relationship between the tracking points and road network
Summary
Datasets comprising Global Navigation Satellite System (GNSS)-based vehicle trajectories are instrumental to traffic management, traffic congestion detection, and transportation planning [1,2,3]. A shortcut link represents a vehicle undercutting an intersection without following the roadway; less existing studies have concerned these implicit errors This issue, due to a long sampling interval, has been considered to be a map matching problem, where a trajectory is partitioned into a set of segments or sections based on headings, angle changes, and the velocity [31]. This in turn helps to identify abnormal segments departing from an existing road network based on a quality evaluation index [15], structural similarities [32,33], or the Hausdorff distance [34] Such approaches focus on large trajectory sections during map matching instead of individual links along a trajectory, such that the spatial accuracy of cutting positions between adjacent sections and the geometric and motion properties of the sections are quite coarse [35]. We test our method using Beijing taxi trajectory datasets collected in 2012
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