Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain
Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6044540 |
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| _version_ | 1832548374203596800 |
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| author | Wenbin Yao Caijun Chen Hongyang Su Nuo Chen Sheng Jin Congcong Bai |
| author_facet | Wenbin Yao Caijun Chen Hongyang Su Nuo Chen Sheng Jin Congcong Bai |
| author_sort | Wenbin Yao |
| collection | DOAJ |
| description | Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward. |
| format | Article |
| id | doaj-art-fbc162089cb343a68d10301f26bd6bb5 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-fbc162089cb343a68d10301f26bd6bb52025-02-03T06:14:14ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6044540Analysis of Key Commuting Routes Based on Spatiotemporal Trip ChainWenbin Yao0Caijun Chen1Hongyang Su2Nuo Chen3Sheng Jin4Congcong Bai5Institute of Intelligent Transportation Systems, College of Civil Engineering and ArchitectureEnjoyor Co., LtdInstitute of Intelligent Transportation Systems, College of Civil Engineering and ArchitectureInstitute of Intelligent Transportation Systems, College of Civil Engineering and ArchitectureInstitute of Intelligent Transportation Systems, College of Civil Engineering and ArchitectureInstitute of Intelligent Transportation Systems, College of Civil Engineering and ArchitectureCommuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward.http://dx.doi.org/10.1155/2022/6044540 |
| spellingShingle | Wenbin Yao Caijun Chen Hongyang Su Nuo Chen Sheng Jin Congcong Bai Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain Journal of Advanced Transportation |
| title | Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain |
| title_full | Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain |
| title_fullStr | Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain |
| title_full_unstemmed | Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain |
| title_short | Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain |
| title_sort | analysis of key commuting routes based on spatiotemporal trip chain |
| url | http://dx.doi.org/10.1155/2022/6044540 |
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