Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.
Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0249318 |
| _version_ | 1818902692103192576 |
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| author | Sung-Bae Cho Jin-Young Kim |
| author_facet | Sung-Bae Cho Jin-Young Kim |
| author_sort | Sung-Bae Cho |
| collection | DOAJ |
| description | Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobility patterns and embedding them to capture their implicit meaning. Clustering groups mobility patterns based on their spatiotemporal characteristics, and embedding provides meaningful information regarding both individual residents (i.e., personalized mobility) and all residents as a whole, enabling a more effective analysis of mobility patterns. Experiments were performed to predict the successive points of interest (POIs) based on transportation data collected from 1.5 million citizens in a large metropolitan city; the results demonstrate that the proposed method achieves top-1, 3, and 5 accuracies of 73.64%, 88.65%, and 91.54%, respectively, which are much higher than those of the conventional method (59.48%, 75.85%, and 80.1%, respectively). We also demonstrate that the proposed method facilitates the analysis of urban mobility through arithmetic operations between POI vectors. |
| first_indexed | 2024-12-19T20:39:41Z |
| format | Article |
| id | doaj.art-2a334465720c4098932e22b3b5cfea7c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T20:39:41Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2a334465720c4098932e22b3b5cfea7c2022-12-21T20:06:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024931810.1371/journal.pone.0249318Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.Sung-Bae ChoJin-Young KimUrban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobility patterns and embedding them to capture their implicit meaning. Clustering groups mobility patterns based on their spatiotemporal characteristics, and embedding provides meaningful information regarding both individual residents (i.e., personalized mobility) and all residents as a whole, enabling a more effective analysis of mobility patterns. Experiments were performed to predict the successive points of interest (POIs) based on transportation data collected from 1.5 million citizens in a large metropolitan city; the results demonstrate that the proposed method achieves top-1, 3, and 5 accuracies of 73.64%, 88.65%, and 91.54%, respectively, which are much higher than those of the conventional method (59.48%, 75.85%, and 80.1%, respectively). We also demonstrate that the proposed method facilitates the analysis of urban mobility through arithmetic operations between POI vectors.https://doi.org/10.1371/journal.pone.0249318 |
| spellingShingle | Sung-Bae Cho Jin-Young Kim Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. PLoS ONE |
| title | Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. |
| title_full | Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. |
| title_fullStr | Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. |
| title_full_unstemmed | Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. |
| title_short | Clustered embedding using deep learning to analyze urban mobility based on complex transportation data. |
| title_sort | clustered embedding using deep learning to analyze urban mobility based on complex transportation data |
| url | https://doi.org/10.1371/journal.pone.0249318 |
| work_keys_str_mv | AT sungbaecho clusteredembeddingusingdeeplearningtoanalyzeurbanmobilitybasedoncomplextransportationdata AT jinyoungkim clusteredembeddingusingdeeplearningtoanalyzeurbanmobilitybasedoncomplextransportationdata |