Notice bibliographique
Résumé
Underground subway system is the key transportation means in dense urban areas such as Hong Kong. Subway stations are crowded with passengers on the platforms and they are observed to squeeze into the train carriages during rush hours. Putting in platform screen doors made the situation even worse. As reported in the local news, subway management claims that after following the change in maximum capacity from six passengers per meter square to four passengers per meter square, the capacity is only 70% full at rush hours. However, the capacity can be over 90% of full loading under the new calculation. Subway stations become more crowded with an average weekday patronage of nearly 5.3 million passengers.  Subway stations are mostly located in the basement or ground levels connecting the shopping mall, commercial or residential building in downtown areas. The occupancy density of passengers can be much higher than expected during festivals with fireworks show and during large-scale movements such as Occupy Central. Therefore, evacuation time in emergency situation will be prolonged. To have a better understanding of the safety issue in subway stations, evacuation time in emergency situations will be studied in this paper.  Two subway stations, Station A and Station B are selected in this paper to study the evacuation hazard of crowded stations when a fire occurs. Station A is an interchange station between two railway lines, being one of the most crowded stations with high occupancy density. Station B is the first station in the local rail network to feature a special design - “Lift-only Entrancesâ€. This is a deep underground station which lies under 70 m of ground level, the passengers have to be evacuated by lift. The occupancy density in Station B is relatively much lower than Station A under normal conditions at the moment, though the station can be very crowded if there are train delays due to signal failure or other reasons.  In this paper, the evacuation effectiveness of Station A and Station B are estimated in terms of evacuation time in different scenarios by using Hydraulic Model Calculation. Moreover, the special evacuation feature of “Lift-only Entrances†in Station B and the fire safety management strategies for emergency evacuation will be discussed.  Three scenarios will be studied in each station:  Scenario A: Assume that the passengers are evenly distributed in different exits in emergency situation. All the possible factors such as passenger behaviors and conditions are eliminated.  Scenario B: Passengers have a higher tendency to evacuate at the larger exit, this is one of the passenger behaviors in emergency situation. Therefore, the passenger distribution which depends on the exit width will be studied.  Scenario C: Assume that some of the exit routes are blocked.  The most important factor for the above study is the passenger behaviors. As in scenario B, passenger behaviors would affect the evacuation time. Therefore, fire safety management is identified to be a key part in keeping efficient evacuation. For example, a good fire action plan on crowd control is needed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».