Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection
It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community det...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6646768 |
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| _version_ | 1832550847603539968 |
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| author | Shuoben Bi Yuyu Sheng Wenwu He Jingjin Fan Ruizhuang Xu |
| author_facet | Shuoben Bi Yuyu Sheng Wenwu He Jingjin Fan Ruizhuang Xu |
| author_sort | Shuoben Bi |
| collection | DOAJ |
| description | It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management. |
| format | Article |
| id | doaj-art-8a713c864e134b5d9f12bd39bf1c7968 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8a713c864e134b5d9f12bd39bf1c79682025-02-03T06:05:36ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66467686646768Analysis of Travel Hot Spots of Taxi Passengers Based on Community DetectionShuoben Bi0Yuyu Sheng1Wenwu He2Jingjin Fan3Ruizhuang Xu4School of Geographical Sciences, Nanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing 210044, ChinaMathematics and Physics Institute, Fujian University of Technology, No. 33 Xuefu South Road, University New District, Fuzhou 350001, ChinaInstitute for the History of Science and Technology, Nanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing 210044, ChinaIt is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management.http://dx.doi.org/10.1155/2021/6646768 |
| spellingShingle | Shuoben Bi Yuyu Sheng Wenwu He Jingjin Fan Ruizhuang Xu Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection Journal of Advanced Transportation |
| title | Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection |
| title_full | Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection |
| title_fullStr | Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection |
| title_full_unstemmed | Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection |
| title_short | Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection |
| title_sort | analysis of travel hot spots of taxi passengers based on community detection |
| url | http://dx.doi.org/10.1155/2021/6646768 |
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