Analysis of the Impacts of Open Residential Communities on Road Traffic Based on AHP and Fuzzy Theory
Bibliographic record
Abstract
With the continuous urbanization in China, the number of private cars has grown rapidly in urban areas, leading to increasingly prominent road congestions.Urban road networks are wide but sparse, which can easily cause congestion.To tackle with this problem, the State Council proposed the idea of "open residential community" in 2016, which is to connect the roads within the residential community with external roads to densify the road networks and increase the area of branch roads, so as to mitigate urban road traffic pressure.In order to study the impacts of open residential communities on the traffic capacity of surrounding roads, this paper first establishes an evaluation indicator system for road traffic using a number of factors such as traffic density, traffic delay time, number of intersection conflicts, road congestion rate and road accessibility based on the analytic hierarchy process (AHP) theory, and also builds a fuzzy comprehensive evaluation model.Then, with the above evaluation indicator system and model, this paper employs the VISSIM traffic simulation technology to simulate the residential community for testing.Finally, it uses the grey relational algorithm to compare and analyze the test data, and obtains the following conclusions: for residential communities with a large area and large traffic volume and those with a small area and large traffic volume, the surrounding road traffic is significantly reduced, and the traffic pressure is greatly alleviated; the effect comes second for those with a large area and small traffic volume; and for those with a small area and small traffic volume, surrounding traffic sees no improvement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".