A Route Diversity–Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network
Bibliographic record
Abstract
Multimodal public transport network (MPTN) plays an important role in relieving road traffic pressure for metropolitan area. Nevertheless, the impact of an accident happened in an individual station may not only disrupt the station itself or the single lines that go through the station but also spread over the whole network. Therefore, identifying the vulnerable stations is essential for improving the MPTN management against the systematic risk caused by accidents. In this paper, we proposed a route diversity‐based approach to measure the vulnerability of stations in MPTN based on the complex network theory. The route constraint parameters were established to reflect the travel time restriction in constructing the set of passengers’ acceptable routes. In addition, an algorithm was formulated to rapidly calculate the route diversity index and meanwhile avoid the “overlapping routes” problem. A simple virtual network was used as a numerical example to compare the proposed approach with the vulnerability evaluation approaches based on degree centrality and betweenness centrality. Finally, the proposed approach was applied to the MPTN of Beijing to explain its effectiveness and potential applications. The results show that the proposed method can efficaciously estimate vulnerable nodes compared with degree centrality and betweenness centrality. Meanwhile, the acceptable routes between any OD pairs in the MPTN are 1–10 according to the constrained parameter. In addition, the average number of acceptable routes between OD pairs of Beijing MPTN is 3.649. By ranking the stations according to their vulnerability, it can be found that the top 5 vulnerable stations are all external traffic hubs or the stations around famous commercial areas. The results suggest that these stations are significant for external transport as well as crucial for internal urban transportation systems. The research output could contribute to the MPTN management in accident prevention and emergency handling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".