Context sensitive transit oriented development assessment: AHP weighted TOD standards for regional railway hubs in Thailand
Bibliographic record
Abstract
This study develops a context-specific Transit-Oriented Development (TOD) evaluation framework for Thailand's regional railway hubs by integrating the Analytic Hierarchy Process (AHP) with established TOD Standards. Through expert-based pairwise comparisons, we determined that transit accessibility (19.1%), connectivity (15.0%), and walkability (14.1%) represent priority criteria for the Thai context, contrasting with the uniform weighting system of international standards. We applied this AHP-weighted framework to assess six regional railway stations: Chiang Mai, Phitsanulok, Nakhon Ratchasima, Khon Kaen, Pattaya, and Hat Yai Junction. Comparative analysis revealed that Hat Yai Junction achieved the highest TOD potential ranking under both standard (74/100) and AHP-weighted (79.7/100) methods, followed by Chiang Mai (72/100 standard; 78.8/100 weighted). The most notable scoring differential appeared in Nakhon Ratchasima (69/100 vs. 78.4/100), demonstrating the significant impact of context-sensitive weighting. All stations showed common weaknesses in cycling infrastructure (average 3.2/5) and car use reduction metrics while achieving the highest scores in transit accessibility criteria. Station-specific evaluation identified targeted improvement priorities: enhancing cycling networks in Chiang Mai, improving pedestrian infrastructure in Phitsanulok, and increasing block connectivity in Pattaya. This contextualized framework gives planners a practical tool for prioritizing TOD investments in Thailand's regional centers.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".