An improved probabilistic relaxation method for matching multi-scale road networks

Zhang, Jianchen; Wang, Yanhui; Zhao, Wenji

doi:10.1080/17538947.2017.1341557

Cited by 18 publications

(23 citation statements)

References 16 publications

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“…However, the algorithm was inaccurate, highly sensitive to sampling methods, exhibited low robustness to noisy data, and was computationally intensive [32]. Wang et al and Wu et al used the orthophoto distance to measure line-to-line distance [33,34]. The orthographic projection was not sensitive to the density of the sample points, and many calculations were required.…”

Section: Spatial Metrics Of Trajectory Similaritymentioning

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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Section: Spatial Metrics Of Trajectory Similaritymentioning

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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show abstract

“…Numerous algorithms have been proposed to solve the road-network matching problem [1][2][3][4][5]8,13,14,19]. The accuracy of road-networks matching is greatly promoted in previous studies.…”

Section: Research Objectmentioning

confidence: 99%

“…Therefore, it is not possible to compare our parallel method with a similar parallelized method. In recent years, the probability-relaxation-matching algorithm (PRM) is often employed in road-network matching studies [5,[13][14][15]19,21,22], thus it was adopted as the benchmark. The general probability-relaxation framework was utilized to implement the PRM in our experiment [23].…”

Section: Model Evaluation Indicesmentioning

confidence: 99%

A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

Wan

Yang

Zhou

et al. 2018

IJGI

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The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

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“…As far as shape similarity is concerned, there are also various operators to measure shape similarity [20,28,31]. Among them, the steering angle function matching algorithm can accurately describe the local, detailed features of complex graphics on their shape and direction, and it is easier to operate and more accurate than other matching algorithms.…”

Section: Selection Of Hierarchical Matching Operatorsmentioning

confidence: 99%

“…Additionally, most researchers use the degree of area overlapping as a criterion to measure the size similarity between area objects [20]. In addition, Hausdorff distance and symmetric difference could also reflect the size similarity of the objects to a certain extent [11].…”

Section: Selection Of Hierarchical Matching Operatorsmentioning

confidence: 99%

Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

Wang

Zhang

Guan

2018

IJGI

Self Cite

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Detecting and extracting the change types of spatial area objects can track area objects' spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data.

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An improved probabilistic relaxation method for matching multi-scale road networks

Cited by 18 publications

References 16 publications

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

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

A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

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