Assessing the Determinant Factors Influencing Transport Mode Choice: A Case of Debre Berhan City
Bibliographic record
Abstract
Mode choice behavior directly affects the layout of the urban transportation system and serves as the foundation for the development of policies about the planning and administration of urban transportation. This study focused on the city’s various transportation options, aiming to pinpoint and investigate several factors influencing transportation mode selection. The data included the traffic survey; traveler interviews were gathered using a questionnaire survey, structured interviews, and secondary documents. The collected data from the questionnaire survey were then analyzed using a multinomial logit (MNL) model to assess the relationships between different parameters and mode choice. The investigation considered several concerns, including travel distance, travel time, travel cost, safety, environmental impact, health benefits, and comfort. Both qualitative and quantitative methods of sustainability have been integrated into this study. The MNL model’s pseudo‐R‐squared value illustrates the apparent correlation between the independent and dependent variables. The multilayer perceptron (MLP) model was used as a comparison model. The results show that MLP has higher predictive performance than the MNL model in assessing transport mode choice in the city. The study reveals that travel distance, time, availability, health benefits, comfort, safety, cost, and environmental impact significantly influence mode choice for work trips. Public services are safer, less environmentally impactful, and more accessible, while walking is safest, offers health benefits, and is more environmentally friendly but is preferred by the youngest. Private vehicle users offer more safety but are less cost‐effective. Minibus users provide better cost‐benefit and safety but take longer travel times. Overall, the study was used to understand passenger preferences and critical factors in transport options, thereby aiding policymakers in making informed decisions and suggestions for improving the transport system in similar cities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".