Updating Road Networks by Local Renewal from GPS Trajectories

Wu, Tao; Xiang, Longgang; Gong, Jianya

doi:10.3390/ijgi5090163

Cited by 23 publications

(15 citation statements)

References 31 publications

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“…For the commercial vehicle application, it might be interesting to find routes or parts of routes locating outside of the existed road network. Actually, it is important to know these kinds of mismatches between trajectories and a road network structure, since researchers and staff can detect possible problem of road network data [37], such as a local topological connectivity error and a road segment deficiency, or even infer the potential demand for constructing a new road. For this kind of problem, the question could be: Q3: Dose the trajectory T1 drive off the existing road?…”

Section: Semantic Trajectory Queriesmentioning

confidence: 99%

TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

Qin

Wan

2019

IJGI

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To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

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Section: Semantic Trajectory Queriesmentioning

confidence: 99%

TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

Qin

Wan

2019

IJGI

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“…With the development of intelligent transportation, floating vehicle trajectories have become a new source of road network data [13]. The floating car data (FCD) system refers mainly to the global positioning system (GPS) equipped on commercial vehicles such as taxis or buses [14,15].…”

Section: Lane-level Data Acquisition Of Road Geometrymentioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

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With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

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“…Detecting new and missing roads on existing road networks has become a common concern in the fields of urban management, intelligent transportation, and driverless technology [2]. Imagery, GNSS (global navigation satellite system) trajectories, and multisource data fusion are some of the major data sources used for the renewal and repair of missing GIS (geographical information system) road network data [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Wu³

2019

IJGI

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With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

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Updating Road Networks by Local Renewal from GPS Trajectories

Cited by 23 publications

References 31 publications

TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

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