Grid‐Based Whole Trajectory Clustering in Road Networks Environment Article Swipe
Fangshu Wang
,
Shuai Wang
,
Xinzheng Niu
,
Jiahui Zhu
,
Ting Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5295784
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5295784
In the data mining of road networks, trajectory clustering of moving objects plays an important role in many applications. Most existing algorithms for this problem are based on every position point in a trajectory and face a significant challenge in dealing with complex and length‐varying trajectories. This paper proposes a grid‐based whole trajectory clustering model (GBWTC) in road networks, which regards the trajectory as a whole. In this model, we first propose a trajectory mapping algorithm based on grid estimation, which transforms the trajectories in road network space into grid sequences in grid space and forms grid trajectories by recognizing and eliminating redundant, abnormal, and stranded information of grid sequences. We then design an algorithm to extract initial clustering centers based on density weight and improve a shape similarity measuring algorithm to measure the distance between two grid trajectories. Finally, we dynamically allocate every grid trajectory to the best clusters by the nearest neighbor principle and an outlier function. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Silhouette Coefficient to maximize intercluster separation and intracluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated on a real‐world dataset in comparison with existing algorithms.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/5295784
- https://downloads.hindawi.com/journals/wcmc/2021/5295784.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3216876631
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Raw OpenAlex JSON
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https://openalex.org/W3216876631Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/5295784Digital Object Identifier
- Title
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Grid‐Based Whole Trajectory Clustering in Road Networks EnvironmentWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Fangshu Wang, Shuai Wang, Xinzheng Niu, Jiahui Zhu, Ting ChenList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/5295784Publisher landing page
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https://downloads.hindawi.com/journals/wcmc/2021/5295784.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/wcmc/2021/5295784.pdfDirect OA link when available
- Concepts
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Computer science, Cluster analysis, Trajectory, Grid, Data mining, Distributed computing, Real-time computing, Artificial intelligence, Geodesy, Physics, Astronomy, GeographyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2973046156, https://openalex.org/W2948104333, https://openalex.org/W2768818405, https://openalex.org/W3106525726, https://openalex.org/W3204586135, https://openalex.org/W3130499959, https://openalex.org/W1981398125, https://openalex.org/W2577691995, https://openalex.org/W1536861392, https://openalex.org/W2147880780, https://openalex.org/W1990997812, https://openalex.org/W1994882049, https://openalex.org/W2466554409, https://openalex.org/W2793343370, https://openalex.org/W2752868845, https://openalex.org/W2068074762, https://openalex.org/W3087451749, https://openalex.org/W2899926324, https://openalex.org/W644979858, https://openalex.org/W2888063336, https://openalex.org/W2986889843, https://openalex.org/W2613068496, https://openalex.org/W2909968474, https://openalex.org/W2115911119, https://openalex.org/W2315883997, https://openalex.org/W2966587580, https://openalex.org/W3035078642, https://openalex.org/W2118529802, https://openalex.org/W2734775449, https://openalex.org/W1983494083, https://openalex.org/W2193209126, https://openalex.org/W2378659577, https://openalex.org/W2118371392 |
| referenced_works_count | 33 |
| abstract_inverted_index.a | 32, 36, 49, 64, 72, 126, 167, 196 |
| abstract_inverted_index.In | 0, 66 |
| abstract_inverted_index.We | 110 |
| abstract_inverted_index.an | 13, 113, 156 |
| abstract_inverted_index.as | 63 |
| abstract_inverted_index.by | 98, 150 |
| abstract_inverted_index.in | 16, 31, 39, 56, 84, 91, 199 |
| abstract_inverted_index.of | 4, 9, 107, 162, 189 |
| abstract_inverted_index.on | 27, 77, 121, 171, 195 |
| abstract_inverted_index.to | 115, 131, 146, 176 |
| abstract_inverted_index.we | 69, 140, 165 |
| abstract_inverted_index.For | 159 |
| abstract_inverted_index.The | 183 |
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| abstract_inverted_index.are | 25, 193 |
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| abstract_inverted_index.paper | 47 |
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| abstract_inverted_index.mining | 3 |
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| abstract_inverted_index.moving | 10 |
| abstract_inverted_index.weight | 123 |
| abstract_inverted_index.whole. | 65 |
| abstract_inverted_index.(GBWTC) | 55 |
| abstract_inverted_index.between | 135 |
| abstract_inverted_index.centers | 119 |
| abstract_inverted_index.complex | 42 |
| abstract_inverted_index.dataset | 198 |
| abstract_inverted_index.dealing | 40 |
| abstract_inverted_index.density | 122 |
| abstract_inverted_index.extract | 116 |
| abstract_inverted_index.improve | 125 |
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| abstract_inverted_index.mapping | 74 |
| abstract_inverted_index.measure | 132 |
| abstract_inverted_index.nearest | 152 |
| abstract_inverted_index.network | 86 |
| abstract_inverted_index.objects | 11 |
| abstract_inverted_index.outlier | 157 |
| abstract_inverted_index.problem | 24 |
| abstract_inverted_index.propose | 71 |
| abstract_inverted_index.regards | 60 |
| abstract_inverted_index.Finally, | 139 |
| abstract_inverted_index.accuracy | 185 |
| abstract_inverted_index.allocate | 142 |
| abstract_inverted_index.clusters | 149 |
| abstract_inverted_index.distance | 134 |
| abstract_inverted_index.existing | 20, 202 |
| abstract_inverted_index.maximize | 177 |
| abstract_inverted_index.neighbor | 153 |
| abstract_inverted_index.position | 29 |
| abstract_inverted_index.proposed | 191 |
| abstract_inverted_index.proposes | 48 |
| abstract_inverted_index.stranded | 105 |
| abstract_inverted_index.abnormal, | 103 |
| abstract_inverted_index.algorithm | 75, 114, 130, 192 |
| abstract_inverted_index.challenge | 38 |
| abstract_inverted_index.classical | 173 |
| abstract_inverted_index.criterion | 169 |
| abstract_inverted_index.establish | 166 |
| abstract_inverted_index.function. | 158 |
| abstract_inverted_index.important | 14 |
| abstract_inverted_index.measuring | 129 |
| abstract_inverted_index.networks, | 6, 58 |
| abstract_inverted_index.principle | 154 |
| abstract_inverted_index.sequences | 90 |
| abstract_inverted_index.Silhouette | 174 |
| abstract_inverted_index.algorithms | 21 |
| abstract_inverted_index.clustering | 8, 53, 118, 163, 168, 184 |
| abstract_inverted_index.comparison | 200 |
| abstract_inverted_index.evaluation | 161 |
| abstract_inverted_index.redundant, | 102 |
| abstract_inverted_index.separation | 179 |
| abstract_inverted_index.sequences. | 109 |
| abstract_inverted_index.similarity | 128 |
| abstract_inverted_index.trajectory | 7, 33, 52, 62, 73, 145 |
| abstract_inverted_index.transforms | 81 |
| abstract_inverted_index.Coefficient | 175 |
| abstract_inverted_index.algorithms. | 203 |
| abstract_inverted_index.dynamically | 141 |
| abstract_inverted_index.eliminating | 101 |
| abstract_inverted_index.estimation, | 79 |
| abstract_inverted_index.illustrated | 194 |
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| abstract_inverted_index.recognizing | 99 |
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| abstract_inverted_index.grid‐based | 50 |
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| abstract_inverted_index.intercluster | 178 |
| abstract_inverted_index.intracluster | 181 |
| abstract_inverted_index.performance, | 164 |
| abstract_inverted_index.real‐world | 197 |
| abstract_inverted_index.trajectories | 83, 97 |
| abstract_inverted_index.applications. | 18 |
| abstract_inverted_index.trajectories. | 45, 138 |
| abstract_inverted_index.length‐varying | 44 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100328312 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I150229711 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.48343142 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |