On the Resilience Evaluation of Urban Multimodal Transportation Network Considering Dynamic Travel Demand
Bibliographic record
Abstract
The urban multimodal transportation network is an essential urban infrastructure for daily mobility, while it is vulnerable to severe disturbances. Existing research often evaluates multimodal network resilience and criticality from either a structural or operational perspective, overlooking its multidimensional characterization. Most studies incorporating dynamic demand are conducted at daily or hourly intervals, neglecting finer temporal granularity that better captures network resilience and criticality. To address these gaps, this study proposes a comprehensive resilience evaluation method for multimodal transportation networks by integrating network structure and function. Node criticality is identified using a novel demand growth rate indicator. Various disturbance scenarios, including random and deliberate disturbances, are constructed to simulate sudden events, considering the impacts of the disturbance scale and intensity of nodes or edges. Moreover, an affected demand redistribution model is developed by combining graph convolutional network (GCN) and the Logit model, considering travel time, distance, transfer numbers, and path complexity. The proposed methods are applied to the multimodal transportation network in Tianjin, China, using transit smart card transaction data. Results reveal multimodal networks exhibit better resistance from a structural perspective, while the subway network achieves higher efficiency when the disturbance scale is less than 0.2. A threshold effect emerges between disturbance scale and residual passenger capacity. Node disturbances cause an average of 21% higher performance losses than edge disturbances. This method quantifies resilience and identifies the critical nodes considering minute-level dynamic travel demand, dynamic demand between nodes, and travel behaviors. These insights support decision-makers in generating more effective response strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".