Tourist Transportation Problems and Guidelines for Developing the Tourism Industry in Khon Kaen, Thailand
Bibliographic record
Abstract
Lack of public transportation and traffic jams are major issues in many tourist destinations. In this study, we present the tourism-related problems of Khon Kaen, Thailand, and provide guidelines to develop its tourism industry. We performed in-depth interviews with 30 tourists, 20 local residents, and 5 government agencies in the Khon Kaen municipality. In addition, we did a small group discussion by inviting 5 tourists, 5 local residents, 5 government agencies, and 3 academicians to discuss ways to improve tourist transportation in Khon Kaen. From the study, we found that Khon Kaen lacks public transportation. We also found that, although the city can be reached by air, the current flight options are not enough to meet the needs of passengers; furthermore, the city’s rail transportation needs to be developed, and there is no municipal bus service around the city or between the city and the airport. To develop transportation guidelines for the tourism industry, local governments, especially in the Khon Kaen municipality, should host an initiative and bring all stakeholders together to solve the problem. In addition, a city bus system needs to be developed immediately, and a public transportation network that links to tourist attractions is also important because it is difficult for tourists to access attractions if they do not have private transportation. Finally, public facilities such as toilets, as well as walkways for disabled people and elderly, also need to be developed, but with environmentally sustainable designs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".