Solutions to cultural, organizational, and technical challenges in developing PECAS models for the cities of Shanghai, Wuhan, and Guangzhou
Bibliographic record
Abstract
Massive construction of transportation infrastructure and fast growth of private car ownership have brought unprecedented changes in land use and transportation systems to cities and regions in many developing countries. Traditional “four-step” travel demand models, which are not designed to assess transport policies under the case of rapid land-use change, cannot be used to achieve coordinated planning of transport and land use. Therefore, there is a pressing need to develop and use integrated land-use transport models (ILUTMs), which consider interactions among socioeconomic activities, urban land use, and transportation development, for policy analysis and for guiding the progressive urbanization process taking place in many parts of these countries. In light of this, efforts have been invested in developing production, exchange, and consumption allocation system (PECAS) models for the cities of Shanghai, Wuhan, and Guangzhou in mainland China. This paper presents the cultural, organizational, and technical challenges encountered in the development of PECAS models for the cities of Shanghai, Wuhan, and Guangzhou and the mitigating solutions from the development teams for taking up or working around them. The solutions and discussions presented in this paper should be interesting to researchers and practitioners for developing ILUTMs in the context of a developing country like China.
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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".