A foresight study on urban mobility: Singapore in 2040
Bibliographic record
Abstract
Purpose The transportation system in any city is complex and evolving, shaped by various driving forces and uncertainties in the social, economic, technological, political and environmental situations. Its development and demands upon it cannot be projected by simply extrapolating past and current trends. This paper aims to present a foresight study examining the future of urban mobility, focusing on the dense Asian city-state of Singapore. The objective is to develop scenarios for the future of urban mobility, to facilitate future policy implementation by highlighting long term challenges and opportunities for transportation planning in cities. Design/methodology/approach To create future scenarios, the authors first sought to identify key drivers of change through environmental scanning, expert interviews, focus group discussions and technology scanning. These drivers of change were subsequently used in a scenario planning workshop, organized to co-create alternative future visions for urban mobility 2040 with experts and local stakeholders. Findings Two scenarios emerged, called the Shared World and the Virtual World. For each scenario, the authors described the key features in terms of dominant transport modes for the movements of passengers and freight. Subsequently, the authors discussed possible implications of each scenario to the individual, society, industry and government. Originality/value As cities grow and develop, city and transport planners should not only address daily operational issues but also develop a well-informed, long-term understanding of the evolving mobility system to address challenges that lie beyond the five- or even ten-year horizon. By using scenario planning approach, the authors hope to prepare stakeholders for the uncertain futures that are continuously shaped by the decisions today.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".