Has Transportation Demand of Shanghai, China, Passed Its Peak Growth?
Bibliographic record
Abstract
On the basis of four comprehensive transportation surveys in Shanghai, China, this study examined the latest trends in Shanghai's travel demand; investigated their social, economic, and spatial drivers; and compared the pace of travel demand growth in three periods: 1980s to early 1990s, early 1990s to mid-2000s, and mid-2000s to the present. The demand growth was relatively slow in the first period and then sped up in the second before it returned to a slower pace in the third period. As for trip purpose, Shanghai's travel is much more diversified than previously, with an increasing share of noncommuting trips (from 28% in 1995 to 46% in 2009). Spatially, travel demand is dispersed from the central district to peripheral districts because of urban expansion and decentralization and from Puxi (west of the Huangpu River) to Pudong (east of the Huangpu River) as a result of significant economic development of the Pudong New Area. Both spatial diffusion and purpose diversification favor the convenience and flexibility of private motor vehicles. Driven by rapid motorization, vehicle travel is growing at a much faster pace than person travel. Overall, the annual growth rate for travel demand in Shanghai reached its peak in 2004 for both person trips and vehicle trips. In absolute numbers, person trip growth has peaked, but vehicle trip growth has not. In response to the growing demand, especially rapid motorization, the local government has made tremendous investments in road infrastructure and public transit, and it has attempted to manage demand through vehicle ownership control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".