A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6301 |
| _version_ | 1797481066560225280 |
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| author | Kaiqi Chen Guowei Chu Kaiyuan Lei Yan Shi Min Deng |
| author_facet | Kaiqi Chen Guowei Chu Kaiyuan Lei Yan Shi Min Deng |
| author_sort | Kaiqi Chen |
| collection | DOAJ |
| description | Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis. |
| first_indexed | 2024-03-09T22:09:10Z |
| format | Article |
| id | doaj.art-2c6ef34ce3214adc97b01502d2787cd7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:09:10Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2c6ef34ce3214adc97b01502d2787cd72023-11-23T19:34:14ZengMDPI AGApplied Sciences2076-34172022-06-011213630110.3390/app12136301A Multiview Representation Learning Framework for Large-Scale Urban Road NetworksKaiqi Chen0Guowei Chu1Kaiyuan Lei2Yan Shi3Min Deng4School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaMethods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis.https://www.mdpi.com/2076-3417/12/13/6301road networkrepresentation learninghuman mobilityintelligent transportation systems |
| spellingShingle | Kaiqi Chen Guowei Chu Kaiyuan Lei Yan Shi Min Deng A Multiview Representation Learning Framework for Large-Scale Urban Road Networks Applied Sciences road network representation learning human mobility intelligent transportation systems |
| title | A Multiview Representation Learning Framework for Large-Scale Urban Road Networks |
| title_full | A Multiview Representation Learning Framework for Large-Scale Urban Road Networks |
| title_fullStr | A Multiview Representation Learning Framework for Large-Scale Urban Road Networks |
| title_full_unstemmed | A Multiview Representation Learning Framework for Large-Scale Urban Road Networks |
| title_short | A Multiview Representation Learning Framework for Large-Scale Urban Road Networks |
| title_sort | multiview representation learning framework for large scale urban road networks |
| topic | road network representation learning human mobility intelligent transportation systems |
| url | https://www.mdpi.com/2076-3417/12/13/6301 |
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