Transportation System Vulnerability Assessment considering Environmental Impact
Bibliographic record
Abstract
Transportation system has a close bearing on the prosperity of society. However, transportation infrastructures are highly vulnerable to extreme events. Therefore, identifying the most critical component of the transportation system is among the first priorities of transportation network planners and managers. This paper proposes a novel framework to identify the most vulnerable component for a road transportation system. A key characteristic of vulnerability assessment is the travelers’ response to the changes in the transportation network topology and capacity after an extreme event. Hence, the problem is formulated as a nonlinear programme with equilibrium constraints, considering travelers’ route choice behavior. In the methodology, two types of vulnerability measures are developed to assess the vulnerability of road transportation system, namely, system travel time-based vulnerability (STTV) and system emissions-based vulnerability (SEV). The former is developed on the basis of short-term planning, while the latter is put forward on the basis of long-term planning. With these vulnerability measures, the proposed framework is then demonstrated using an extended Nguyen-Dupuis network under different demand levels and different capacity degradation levels, taking into account two modes: bus and car. The results indicate that different vulnerability measures can identify similar vulnerable components. Moreover, it is shown that the SEV can find more critical components than the STTV regardless of capacity degradation or demand growth. Our research helps to create a recovery plan by assigning priority to the critical transportation infrastructures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".