System Dynamics Modelling for Dynamic Emergency Response to Accidents Involving Transport of Dangerous Goods by Road
Bibliographic record
Abstract
For improving the emergency capacities during road transportation accidents of dangerous goods, this paper divides the accident emergency procedure into four subsystems: accident information processing, personnel rescue and evacuation, vehicle evacuation, and rescue materials delivery. On this basis, the causal mechanism and the loop diagrams of the emergency process are established by using the System Dynamics Method and Vensim software. First, six factors including transportation distance, the degree of mechanical modernization, delay time of personnel control, delay time of traffic information release, command level coefficient, and delay time of department arrival are selected as parameters to test the model’s sensitivity analysis. Then, the influences of the abovementioned factors on the observed value, such as the demand gap of on-site materials, number of people in safe area, number of vehicles in safe area, amount of disposal information, and the dynamic evolution behavior of the system, are analyzed. The results show that the transportation distance is shortened by half, and the time to fill the demand gap is reduced by 39%. The level of mechanical modernization is doubled, the peak inventory of materials in transit will increase by 9.2%, and the time to reach the peak will be shortened by 6.8%. If the delay time of personnel control is shortened by 480 s, the number of people to be evacuated in accident area will be reduced by 56. The delay time of traffic information release is shortened by 480 s, the number of vehicles to be evacuated is reduced by 74, and the time when the vehicle stops entering accident area is 1646 s in advance. The command level coefficient increases by 9.5%, and the speed of action execution increases by 9.6%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".