Vulnerability Analysis of Public Transportation Systems
Bibliographic record
Abstract
RÉSUMÉ: «La rapide urbanisation et industrialisation des villes a entraîné une augmentation significative des volumes de déplacements urbains, intensifiant les demandes sur les systèmes de transport public, qui constituent une infrastructure urbaine essentielle. Les systèmes de transport en commun jouent un rôle crucial dans la mobilité quotidienne, en particulier pour les individus de divers milieux socio-économiques qui dépendent de ces services. Les perturbations dans les systèmes de transport en commun peuvent donc avoir des impacts sociaux étendus. Par ailleurs, des incidents tels que les accidents, les conditions météorologiques défavorables et les problèmes opérationnels provoquent fréquemment des interruptions temporaires ou prolongées des services de transport en commun. Par exemple, un examen des données historiques de perturbations dans le métro de Montréal des cinq dernières années montre que ce système subit plus de 7 400 interruptions annuellement de moins de 14 minutes, environ 100 interruptions de 15 à 29 minutes, et entre 70 et 75 interruptions par an de plus de 30 minutes. Ces statistiques soulignent l’importance d’étudier la vulnérabilité des systèmes de transport public. Introduit en 2002 comme concept en ingénierie des transports, la vulnérabilité est définie comme une « susceptibilité aux incidents pouvant entraîner une réduction considérable de la serviceabilité du réseau routier » (Traduit de l'anglais). Depuis son introduction, des progrès substantiels ont été réalisés dans ce domaine, avec des chercheurs développant des méthodologies et indicateurs innovants. Les études ont évolué, passant de l’examen des seules caractéristiques topologiques des réseaux de transport à l’analyse des aspects fonctionnels, ce qui a permis d’améliorer la compréhension de la serviceabilité du réseau. L’introduction de concepts tels que la vulnérabilité dynamique et les défaillances en cascade a également perfectionné la précision des simulations de perturbations dans les réseaux de transport en commun. De plus, diverses mesures d’atténuation – notamment pour les interruptions dans le métro – ont été optimisées grâce à des techniques avancées d’optimisation pour réduire les effets des interruptions de moyen et long terme. Cependant, malgré ces avancées, il reste des lacunes importantes dans l’analyse de la vulnérabilité des systèmes de transport en commun. Cette thèse vise à combler certaines de ces lacunes à travers une série de quatre articles, chacun contribuant à une compréhension et évaluation plus approfondies de la vulnérabilité des systèmes de transport en commun.» ABSTRACT: «The rapid urbanization and industrialization of cities have led to a significant increase in urban travel volumes, intensifying demands on public transportation systems, which serve as essential urban infrastructure. Public transit systems play a crucial role in daily mobility, particularly for individuals from diverse socio-economic backgrounds who rely on these services. Disruptions to transit systems, therefore, can have widespread societal impacts. Meanwhile, incidents such as accidents, adverse weather conditions, and operational issues frequently cause temporary or prolonged disruptions to transit services. An examination of a five-year disruption history in Montreal’s metro system, for instance, shows that this system experiences over 7,400 annual disruptions of up to 14 minutes, around 100 disruptions lasting between 15 and 29 minutes, and between 70 to 75 disruptions each year that extend beyond 30 minutes. These statistics underscore the importance of studying the vulnerability of public transportation systems. Vulnerability, introduced as a concept in transportation engineering in 2002, is defined as “a susceptibility to incidents that can result in considerable reductions in road network serviceability.” Since its introduction, substantial progress has been made in this field, with researchers developing innovative methodologies and indicators. Studies have evolved from focusing solely on the topological features of transportation networks to examining functional aspects, enhancing the understanding of network serviceability. The introduction of concepts like dynamic vulnerability and cascading failures has further advanced the precision of disruption simulations in transit networks. Additionally, various mitigation measures—particularly for metro disruptions—have been optimized through advanced optimization techniques to alleviate the effects of medium- and long-term disruptions. However, despite these advancements, there remain significant research gaps in vulnerability analysis of public transportation systems. This dissertation seeks to address some of these gaps through a series of four articles, each contributing to a deeper understanding and assessment of transit system vulnerability.»
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".