Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".