Understanding TDM and its Role in the Delivery of Sustainable Urban Transport
Bibliographic record
Abstract
This paper describes how transport professionals worldwide, particularly in Japan, India, Canada, Australia, USA, New Zealand and in the Far East, have been working together to update understanding of transport demand management (TDM) as a philosophy that underpins the approach to improving the sustainability of transport. A new paradigm in transport planning, which is internationally recognized as TDM, is emerging to embrace mobility management under its umbrella. The paper draws on good practice from across Europe and North America to demonstrate how, through proper understanding of TDM, professionals can be encouraged to work collaboratively towards the sustainable transport policy objective. Furthermore, the paper will explain how TDM unifies the work travel planners, public transport operators, and highway engineers alike to deliver more effective transport solutions. The fascinating results of benchmarking work using the Mobility Enhancement and Trip Reduction Index to aide Comparison (METRIC©) system will be summarized, which is being used internationally to benchmark comparative progress on TDM implementation (including mobility management). The output is report card that can be used by cities to identify opportunities for improvement and what to learn from whom. The paper concludes with the proposition that is needed to form a way to frame the benefits of TDM in terms of a future vision of sustainability that goes beyond the current short term political and operational requirements. The paper will argue that the assumption that ‘mobility management’ interventions help provide a ‘foot-in-the-door’ approach (that will lead individuals up a virtuous ladder of more far-reaching behavioral changes) is fraught with contradictions. Finally, the paper will signpost a growing professional literature that emphasizes the importance of appealing to intrinsic over extrinsic values in order to stimulate longer-lasting behavior change.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".