Developing sustainable transportation for university trips in low-income countries
Bibliographic record
Abstract
The UN has defined Sustainable Development Goals, in which sustainable cities and sustainable transportation can be achieved. Traffic congestion with adverse environmental, social and economic side effects has been determined as a threat to sustainable transport. This issue is more evident in developing countries, particularly around large organisations. Thus, this paper aims to assess the impact of different variables on university students’ mode choice in developing countries to propose countermeasures for shifting students’ trips from private cars to sustainable transport modes and reduce traffic congestion. The Shahid Bahonar University of Kerman in Iran is considered the case study in this research. Structural equation modelling is used to evaluate the relationships between the probability of using a specific mode with human characteristics, transportation mode specification and environmental conditions. The model outputs indicate that cycling needs to be incentivised and the general attitude about women cycling needs to be changed. The regular bus timetable, driving style, service frequency and spatial coverage also need to be modified. Regarding taxis, the driving style must be changed, and more attention needs to be paid to the travel-time reliability of taxis. Currently, carpooling is not well known and organised. Thus, developing a web-based application is recommended to organise the use of carpooling and help students living nearby to find each other. Regarding passenger cars, a web-based application for parking management can be used to manage the requests and increase the utility of other sustainable modes.
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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 | 0.001 | 0.002 |
| 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".