Research on Optimization Model of Logistics Transportation Truck Path considering Environmental Impact: Experimental Data from Xiqing District, Tianjin
Bibliographic record
Abstract
In recent years, the prohibition of trucks which could cause environmental pollution on urban roads has become widespread in China. However, some truck restriction policies might lead to a reduction in logistics transportation efficiency. With the help of big data, technology companies have developed many truck applications, such as HCB, truck home, and truck help, to provide the drivers available traffic information. In this context, this paper put forward a truck path optimization model considering environmental impact (TPOM-EI), which is solved by a heuristic algorithm—ant colony optimization (ACO) algorithm. Most previous studies focused on unilateral benefits rather than overall benefits; this paper aims to propose a path optimization model based on real-time minimization of social and transportation costs. Finally, data of Xiqing Economic and Technological Development Zone in Tianjin city (XQ-EDZ) have been used to demonstrate the applicability of the proposed algorithm. The results show that logistics truck path has a huge impact on social costs, and real-time activities in various areas will also change the path of a truck. This research will also help logistics truck drivers to choose the best route in real time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".