Planning of Dynamic Routing of Logistics in Urban Public Sports Facilities Based on MAS
Bibliographic record
Abstract
Urban public sports facilities are an important carrier of comprehensive function in a city. So the government should develop urban public sports facilities to satisfy the civic requirements to improve public health by sports activities. However, China has the largest population in the world. Especially, China has entered an accelerating period of urbanization at present, and the city has become increasingly crowded. So, it will be a crucial problem how to use the urban public sports facilities efficiently to develop public health, and there are many factors to affect the efficiency to use urban public sports facilities such as spare time, space location, the number of urban public sports facilities, and so on. This paper will only discuss planning on dynamic routing of logistics in urban public sports facilities based on MAS and Dijkstra algorithm theory. Actually, intellectual traffic system (ITS) has been noticed and researched gradually with the development and demand of high technology and traffic transportation. Currently, intellectual traffic system has also become one of the most important studying hotspot, which is tested extensively including for logistics transportation. Meanwhile, geography information system (GIS) has to apply to the traffic transportation if the cost of logistics transportation is to be lowest. With the development of GIS, it is further applied to prediction, planning, and decision making in logistics. In fact, urban community sports depend on development of urban public sports facilities in a way, and it is necessary to use urban public sports facilities thoroughly. So, this paper studies dynamic routing planning in logistics of urban public sports facilities and analyzes the algorithm of the shortest path through the calculation on the basis of GIS and MAS in the region of logistics to the urban public sports facilities. It also draws a conclusion that GIS and MAS can improve efficiency and quality promptly of transportation in the logistics of urban public sports facilities. Furthermore, it also amends algorithm of logistics transportation routing net in urban public sports facilities and provides some valuable suggestions for development of urban public sports facilities based on MAS and Dijkstra algorithm theory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".