Identifying Strong Connectivity in Urban Road Networks Considering Traffic Constraints: An Analysis of Road Networks With Different Patterns
Bibliographic record
Abstract
Road network connectivity is an important indicator for measuring the operational efficiency and reliability of urban road networks, and it plays an important role in supporting traffic planning and management decisions. The implementation of traffic management measures, such as traffic bans and temporary traffic flow changes, will restrict access to some sections and lanes, reduce the passable paths in the road network, and thus affect the overall connectivity performance of the road network. Existing road network research results mostly evaluate the topological connectivity of the network at the physical level, and it is difficult to accurately portray the actual road network connectivity under traffic management conditions. To quantitatively evaluate the road network connectivity performance after the implementation of traffic management tools, this paper proposes a road network connectivity evaluation method based on strongly connected effective paths. Firstly, the node steering coefficients are used to describe the no‐traffic constraints of turning lanes, and the connectivity evaluation indexes are constructed based on the number of strongly connected effective paths and the shortest paths of strongly connected paths. Secondly, combining the Floyd‐Warshall algorithm and the depth‐first search algorithm, we constructed a strong connectivity effective path search algorithm to adapt to the refined traffic management situation, and identified the key road sections that have the greatest impact on the connectivity of the road network by considering the maximum acceptable level of the path and the road access constraints. Finally, Sioux‐Falls network and nine urban road networks with different layout patterns are selected for the case study and compared with traditional road network connectivity indicators. The case studies show that: (1) the connectivity of the square grid road network structure is superior, while the connectivity of the free‐form road network is the lowest; (2) road access management measures reduce the overall road network connectivity, and the banning of traffic in critical sections has the most significant effect on connectivity. Accurately assessing the changes in road network connectivity performance under different traffic management measures provides a scientific basis for the development of road control strategies, which can effectively improve urban traffic fluency and residents’ travel efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".