Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems
Bibliographic record
Abstract
Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".