Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China
Due to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This s...
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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/10.1155/2022/7647121 |
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| _version_ | 1832551434679222272 |
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| author | Jun Bi Qiuyue Sai Fujun Wang Yakun Chen |
| author_facet | Jun Bi Qiuyue Sai Fujun Wang Yakun Chen |
| author_sort | Jun Bi |
| collection | DOAJ |
| description | Due to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This study analyzes electric waybill and state data of garbage trucks to identify hot nodes of construction waste transportation, where the volume of garbage trucks is relatively high. Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. Second, the transportation trajectory of working trucks is extracted, and its spatiotemporal characteristics are further analyzed. Hot nodes are found based on density clustering. Finally, a case study is conducted based on the Shenzhen construction waste transportation system. The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. Considering the minimum number of samples and the neighborhood radius, the clustering number is determined as 100. The ratio of noise points is determined as 0.79. The results can provide decision support for the management of electronic waybill and garbage truck transportation. |
| format | Article |
| id | doaj-art-f92916e90bf34a0fbcef8a4d13b9ecc4 |
| 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-f92916e90bf34a0fbcef8a4d13b9ecc42025-02-03T06:01:38ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7647121Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, ChinaJun Bi0Qiuyue Sai1Fujun Wang2Yakun Chen3Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportSchool of Traffic and TransportationSchool of Traffic and TransportationSchool of Traffic and TransportationDue to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This study analyzes electric waybill and state data of garbage trucks to identify hot nodes of construction waste transportation, where the volume of garbage trucks is relatively high. Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. Second, the transportation trajectory of working trucks is extracted, and its spatiotemporal characteristics are further analyzed. Hot nodes are found based on density clustering. Finally, a case study is conducted based on the Shenzhen construction waste transportation system. The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. Considering the minimum number of samples and the neighborhood radius, the clustering number is determined as 100. The ratio of noise points is determined as 0.79. The results can provide decision support for the management of electronic waybill and garbage truck transportation.http://dx.doi.org/10.1155/2022/7647121 |
| spellingShingle | Jun Bi Qiuyue Sai Fujun Wang Yakun Chen Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China Journal of Advanced Transportation |
| title | Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China |
| title_full | Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China |
| title_fullStr | Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China |
| title_full_unstemmed | Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China |
| title_short | Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China |
| title_sort | identification of working trucks and critical path nodes for construction waste transportation based on electric waybills a case study of shenzhen china |
| url | http://dx.doi.org/10.1155/2022/7647121 |
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