Motorcycle Use and Mode Choice in Hanoi, Vietnam
Bibliographic record
Abstract
Motor vehicle ownership in developing countries in Asia has been rising rapidly. In Vietnam, the path has been a dramatic rise in the ownership of motorcycles and cars. Between 2006 and 2011, the number of registered cars in Hanoi rose by 179 percent to 235,000 while the number of motorcycles rose by 85 percent to 4.0 million units. Such growth presents major challenges for city and national officials. The current paper reports on early findings of a research study to identify the factors that drive modal shift to and from motos (2-wheel vehicles) for different segments of the urban transport market in Hanoi as a means to support government efforts to promote transit and also to slow a shift from motos to cars. They study undertook major traffic surveys and surveys of traveler choice, including stated preference surveys, in the second quarter of 2014. Discrete choice model estimation by a number of socio-demographic and trip-making characteristics will be established in the future. Initial key findings from the surveys are the dominance of motorcycles in traffic movement, the low rate of helmet wearing for children on motorcycles, the high frequency in which respondents are involved in accidents, safety concerns having a major influence in decisions to purchase and use vehicles, a greater level of concern for vehicle and fuel costs with regard to the purchase of motorcycles, scooters and bicycles than car, and concerns regarding personal security, travel time, reliability, flexibility and personal space in the case of bus use.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".