Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map
With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Transport, Warsaw University of Technology
2023-11-01
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| Series: | Archives of Transport |
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| Online Access: | https://www.archivesoftransport.com/index.php/aot/article/view/14 |
| _version_ | 1797463896436506624 |
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| author | Yunwei Meng Shibao Li Kang Chen Binbin Li Ji’en Zhang Guangyan Qing |
| author_facet | Yunwei Meng Shibao Li Kang Chen Binbin Li Ji’en Zhang Guangyan Qing |
| author_sort | Yunwei Meng |
| collection | DOAJ |
| description |
With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin—Destination) planning are of great significance.
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| first_indexed | 2024-03-09T17:59:13Z |
| format | Article |
| id | doaj.art-83da0be831744253935443931677ef47 |
| institution | Directory Open Access Journal |
| issn | 0866-9546 2300-8830 |
| language | English |
| last_indexed | 2024-03-09T17:59:13Z |
| publishDate | 2023-11-01 |
| publisher | Faculty of Transport, Warsaw University of Technology |
| record_format | Article |
| series | Archives of Transport |
| spelling | doaj.art-83da0be831744253935443931677ef472023-11-24T10:04:48ZengFaculty of Transport, Warsaw University of TechnologyArchives of Transport0866-95462300-88302023-11-0168410.61089/aot2023.1g53c194Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat mapYunwei Meng0https://orcid.org/0000-0001-8605-4920Shibao Li1https://orcid.org/0009-0008-3078-9205Kang Chen2https://orcid.org/0009-0001-3625-5086Binbin Li3https://orcid.org/0000-0003-2493-5129Ji’en Zhang4https://orcid.org/0009-0008-8053-2256Guangyan Qing5https://orcid.org/0009-0009-9364-2662School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaSchool of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing, ChinaSchool of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing, ChinaChina Merchants Roadway Information Technology (Chongqing) Co., Ltd, Chongqing, China With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin—Destination) planning are of great significance. https://www.archivesoftransport.com/index.php/aot/article/view/14crowd gatheringbaidu heatmapurban core areaspatiotemporal characteristicspopulation equivalent density |
| spellingShingle | Yunwei Meng Shibao Li Kang Chen Binbin Li Ji’en Zhang Guangyan Qing Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map Archives of Transport crowd gathering baidu heatmap urban core area spatiotemporal characteristics population equivalent density |
| title | Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map |
| title_full | Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map |
| title_fullStr | Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map |
| title_full_unstemmed | Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map |
| title_short | Research on spatiotemporal characteristics of urban crowd gathering based on Baidu map heat map |
| title_sort | research on spatiotemporal characteristics of urban crowd gathering based on baidu map heat map |
| topic | crowd gathering baidu heatmap urban core area spatiotemporal characteristics population equivalent density |
| url | https://www.archivesoftransport.com/index.php/aot/article/view/14 |
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