Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".