A Route Choice Model for Road Network Users in Mountainous Cities considering Vulnerability
Bibliographic record
Abstract
Urban resilience has become one of the core arguments of sustainable urban development. The urban road system should also respond effectively to various changes or impacts and reduce the uncertainty and vulnerability of road network caused by emergencies. A route choice model considering the vulnerability was proposed. The model analyzed the traveler’ behavior in complex road network. The model is validated by the road network in Chongqing city. First, the vulnerability of road network in mountainous cities is defined, and the failure situation and state classification of road network considering vulnerability are analyzed. The results show that travelers with different sensitive states have obvious different route choice behavior in vulnerable networks. Since nonsensitive users do not reserve travel time in the early days, they are more inclined to respond to emergencies quickly and then choose fast detour paths. However, sensitive users have set off earlier than normal time and have psychological expectations of emergencies. They have more time to maintain the existing path. Finally, a calculation example is used to test that a certain section of the road network fails randomly, and network users can effectively choose the individual optimal route under different failure states.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".