Operational Efficiency Comparison and Transportation Resilience: Case Study of Nanjing, China
Bibliographic record
Abstract
With the changes in global climate, adverse weather events appear to be more frequent. The efficiency, reliability, and safety of transportation operations can be compromised by extreme weather conditions. It is critical to analyse the spatiotemporal effects of extreme weather on the operational efficiency of transportation operational efficiency for meeting people’s travel and transport needs and formulating emergency management operation to prevent road congestion. It is also important to improve the resilience of transportation system. We compared the changes in transportation operational efficiency in Nanjing during normal weather on January 23 and snowstorm on January 25, 2018, using real-time travel data from Gaode Map. The results indicate that there are differences in the distribution of accessibility changes from different residential areas to the main city area in the morning peak. Overall, 90% of the accessibility change values are concentrated in −10 to 10 minutes, while 65% are concentrated in −5 to 5 minutes, and 70% of the rangeability of accessibility is concentrated in −20% to 20%. In a word, the travel in Nanjing in the snowstorm weather is basically normal. This observation is mainly due to the overnight snow removal and the reduction in the number of small car trips. In addition to the Xinjiekou area, the Hexi business district and the exit area of the cross-river tunnel should also be included in the key management areas for taking emergency management operation in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".