The Sense of Travel Deprivation and Its Impact on Travel Decision: A Case Study of China’s Rapidly Urbanizing Region
Bibliographic record
Abstract
Under the guidance of regional policy, many regions in China have recently become urbanized at an unprecedented speed. In these rapidly urbanizing regions, urban transport systems have been constructed and implemented to improve efficiency yet fail to consider transport equity. This article studies the transport equity of residents in China’s rapidly urbanizing regions and depicts the impacts on travel behavior by promoting equity in transport policies at this current stage. As an area of rapid urbanization, we chose the Chenggong district, Kunming, which was established as a new urban district in 2011. The data on travel and transport equity were collected by designing and conducting a questionnaire survey in this area. To characterize residents’ perceptions of transport equity, we defined and developed a method of measuring their sense of travel deprivation so that transport equity describes both their sense of public and private travel deprivation. We then modeled residents’ travel decision-making, which was influenced by their sense of travel deprivation. The results demonstrated that in regions of rapid urbanization, the sense of private travel deprivation was stronger than that of public transport, and private travel deprivation had a negative impact on travel frequency. Meanwhile, no significant correlation was found between the sense of public travel deprivation and travel decision-making. We propose that raising residents’ income alone will not effectively relieve their sense of travel deprivation. Our research highlights that policymakers should also carefully consider the sense of travel deprivation among the elderly in this region and the welfare of indigenous people. Expansion of private transport in this region should be conducted proactively in accordance with scientific and rational planning and management rather than through reactively increasing private transport as rapid urbanization continues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".