Retrofit or new construction? Strategic budget allocation to improve transportation network redundancy under uncertain disruptions
Bibliographic record
Abstract
• Provide an efficient tool for resilience-oriented transportation planning. • Justify model solutions theoretically and through numerical experiments. • Explore complementary advantages of link construction and retrofitting. • Highlight the tradeoff between new construction and redundancy improvement. • Recommend retrofitting in cases of high disruption probability and cost disparity. Enhancing transportation network redundancy is an effective approach to proactively improving network resilience and mitigating the consequences of potential disruptions. This research addresses a route diversity redundancy-oriented strategic transportation network planning problem that involves the integration of two typical means: (1) constructing new infrastructure (e.g., road segments and bridges) and (2) retrofitting existing infrastructure. The two means differ in their mechanisms, effectiveness, and costs. To determine the optimal budget allocation between the two means, we establish a stochastic programming model that minimizes the expected loss of network redundancy (measured by network-level efficient routes) under uncertain disruptions. To overcome challenges due to the non-explicit redundancy formulation and the exponentially growing solution space, we provide an approximation algorithm to efficiently solve the model. Model extensions to include practical concerns, such as planners’ preferences for new construction and fairness in O-D-level redundancy, are also discussed. Using the 0–1 knapsack transformation, we theoretically elucidate the tradeoffs and priorities in retrofitting and new construction under varying disruption probabilities and cost disparities between the two means. We show the features of the model solutions and the applicability of the method using a 16-node network and the realistic Winnipeg network. We demonstrate that the model flexibly adjusts the budget allocation ratio between new link construction and link retrofitting under various conditions of disruption probability, cost disparity and budget level, effectively leveraging their complementary advantages to enhance network redundancy. In conditions with low disruption probabilities, low ratios of new link construction cost to retrofitting cost, and sufficient budgets, the scheme tends to favor new link construction. However, increasing the allocation for constructing new links — while beneficial for redundancy in the normal state — may conflict with reducing redundancy loss under disruptive events. Notably, we identify a critical budget threshold beyond which redundancy loss transitions from positive to negative, offering insights for determining appropriate budget levels. The proposed mathematical framework and numerical findings may provide practical support for planners in justifying redundancy-oriented decision-making (e.g., project prioritization), contributing to advancing efforts toward resilient urban infrastructure planning.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".